From 6aab1897c202b4a24b5dc76fb8b4378028bea181 Mon Sep 17 00:00:00 2001 From: AwsSdkPhpAutomation Date: Tue, 11 Jun 2019 18:32:05 +0000 Subject: [PATCH] Update models for release --- .changes/3.100.1 | 7 +++ CHANGELOG.md | 4 ++ src/data/sagemaker/2017-07-24/api-2.json | 31 ++++++++-- src/data/sagemaker/2017-07-24/api-2.json.php | 2 +- src/data/sagemaker/2017-07-24/docs-2.json | 62 ++++++++++++------- src/data/sagemaker/2017-07-24/docs-2.json.php | 2 +- 6 files changed, 81 insertions(+), 27 deletions(-) create mode 100644 .changes/3.100.1 diff --git a/.changes/3.100.1 b/.changes/3.100.1 new file mode 100644 index 0000000000..4458bdd0ef --- /dev/null +++ b/.changes/3.100.1 @@ -0,0 +1,7 @@ +[ + { + "type": "api-change", + "category": "SageMaker", + "description": "The default TaskTimeLimitInSeconds of labeling job is increased to 8 hours. Batch Transform introduces a new DataProcessing field which supports input and output filtering and data joining. Training job increases the max allowed input channels from 8 to 20." + } +] diff --git a/CHANGELOG.md b/CHANGELOG.md index 9f94bd0951..19e12bdce2 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,5 +1,9 @@ # CHANGELOG +## next release + +* `Aws\SageMaker` - The default TaskTimeLimitInSeconds of labeling job is increased to 8 hours. Batch Transform introduces a new DataProcessing field which supports input and output filtering and data joining. Training job increases the max allowed input channels from 8 to 20. + ## 3.100.0 - 2019-06-10 * `Aws\CodeBuild` - AWS CodeBuild adds support for source version on project level. diff --git a/src/data/sagemaker/2017-07-24/api-2.json b/src/data/sagemaker/2017-07-24/api-2.json index 6177f05efc..718f05c65b 100644 --- a/src/data/sagemaker/2017-07-24/api-2.json +++ b/src/data/sagemaker/2017-07-24/api-2.json @@ -1556,6 +1556,7 @@ "TransformInput":{"shape":"TransformInput"}, "TransformOutput":{"shape":"TransformOutput"}, "TransformResources":{"shape":"TransformResources"}, + "DataProcessing":{"shape":"DataProcessing"}, "Tags":{"shape":"TagList"} } }, @@ -1594,6 +1595,14 @@ "min":1, "pattern":"[\\S\\s]+" }, + "DataProcessing":{ + "type":"structure", + "members":{ + "InputFilter":{"shape":"JsonPath"}, + "OutputFilter":{"shape":"JsonPath"}, + "JoinSource":{"shape":"JoinSource"} + } + }, "DataSource":{ "type":"structure", "members":{ @@ -2119,7 +2128,8 @@ "CreationTime":{"shape":"Timestamp"}, "TransformStartTime":{"shape":"Timestamp"}, "TransformEndTime":{"shape":"Timestamp"}, - "LabelingJobArn":{"shape":"LabelingJobArn"} + "LabelingJobArn":{"shape":"LabelingJobArn"}, + "DataProcessing":{"shape":"DataProcessing"} } }, "DescribeWorkteamRequest":{ @@ -2681,7 +2691,7 @@ "InputDataConfig":{ "type":"list", "member":{"shape":"Channel"}, - "max":8, + "max":20, "min":1 }, "InputModes":{ @@ -2774,6 +2784,18 @@ "min":1, "pattern":".+" }, + "JoinSource":{ + "type":"string", + "enum":[ + "Input", + "None" + ] + }, + "JsonPath":{ + "type":"string", + "max":63, + "min":0 + }, "KmsKeyId":{ "type":"string", "max":2048, @@ -4535,7 +4557,8 @@ "rasp3b", "deeplens", "rk3399", - "rk3288" + "rk3288", + "sbe_c" ] }, "TaskAvailabilityLifetimeInSeconds":{ @@ -4573,7 +4596,7 @@ }, "TaskTimeLimitInSeconds":{ "type":"integer", - "max":3600, + "max":28800, "min":1 }, "TaskTitle":{ diff --git a/src/data/sagemaker/2017-07-24/api-2.json.php b/src/data/sagemaker/2017-07-24/api-2.json.php index 8e74859a1d..4c7b58554d 100644 --- a/src/data/sagemaker/2017-07-24/api-2.json.php +++ b/src/data/sagemaker/2017-07-24/api-2.json.php @@ -1,3 +1,3 @@ '2.0', 'metadata' => [ 'apiVersion' => '2017-07-24', 'endpointPrefix' => 'api.sagemaker', 'jsonVersion' => '1.1', 'protocol' => 'json', 'serviceAbbreviation' => 'SageMaker', 'serviceFullName' => 'Amazon SageMaker Service', 'serviceId' => 'SageMaker', 'signatureVersion' => 'v4', 'signingName' => 'sagemaker', 'targetPrefix' => 'SageMaker', 'uid' => 'sagemaker-2017-07-24', ], 'operations' => [ 'AddTags' => [ 'name' => 'AddTags', 'http' => [ 'method' => 'POST', 'requestUri' => '/', ], 'input' => [ 'shape' => 'AddTagsInput', ], 'output' => [ 'shape' => 'AddTagsOutput', ], ], 'CreateAlgorithm' => [ 'name' => 'CreateAlgorithm', 'http' => [ 'method' => 'POST', 'requestUri' => '/', ], 'input' => [ 'shape' => 'CreateAlgorithmInput', ], 'output' => [ 'shape' => 'CreateAlgorithmOutput', ], ], 'CreateCodeRepository' => [ 'name' => 'CreateCodeRepository', 'http' => [ 'method' => 'POST', 'requestUri' => '/', ], 'input' => [ 'shape' => 'CreateCodeRepositoryInput', ], 'output' => [ 'shape' => 'CreateCodeRepositoryOutput', ], ], 'CreateCompilationJob' => [ 'name' => 'CreateCompilationJob', 'http' => [ 'method' => 'POST', 'requestUri' => '/', ], 'input' => [ 'shape' => 'CreateCompilationJobRequest', ], 'output' => [ 'shape' => 'CreateCompilationJobResponse', ], 'errors' => [ [ 'shape' => 'ResourceInUse', ], [ 'shape' => 'ResourceLimitExceeded', ], ], ], 'CreateEndpoint' => [ 'name' => 'CreateEndpoint', 'http' => [ 'method' => 'POST', 'requestUri' => '/', ], 'input' => [ 'shape' => 'CreateEndpointInput', ], 'output' => [ 'shape' => 'CreateEndpointOutput', ], 'errors' => [ [ 'shape' => 'ResourceLimitExceeded', ], ], ], 'CreateEndpointConfig' => [ 'name' => 'CreateEndpointConfig', 'http' => [ 'method' => 'POST', 'requestUri' => '/', ], 'input' => [ 'shape' => 'CreateEndpointConfigInput', ], 'output' => [ 'shape' => 'CreateEndpointConfigOutput', ], 'errors' => [ [ 'shape' => 'ResourceLimitExceeded', ], ], ], 'CreateHyperParameterTuningJob' => [ 'name' => 'CreateHyperParameterTuningJob', 'http' => [ 'method' => 'POST', 'requestUri' => '/', ], 'input' => [ 'shape' => 'CreateHyperParameterTuningJobRequest', ], 'output' => [ 'shape' => 'CreateHyperParameterTuningJobResponse', ], 'errors' => [ [ 'shape' => 'ResourceInUse', ], [ 'shape' => 'ResourceLimitExceeded', ], ], ], 'CreateLabelingJob' => [ 'name' => 'CreateLabelingJob', 'http' => [ 'method' => 'POST', 'requestUri' => '/', ], 'input' => [ 'shape' => 'CreateLabelingJobRequest', ], 'output' => [ 'shape' => 'CreateLabelingJobResponse', ], 'errors' => [ [ 'shape' => 'ResourceInUse', ], [ 'shape' => 'ResourceLimitExceeded', ], ], ], 'CreateModel' => [ 'name' => 'CreateModel', 'http' => [ 'method' => 'POST', 'requestUri' => '/', ], 'input' => [ 'shape' => 'CreateModelInput', ], 'output' => [ 'shape' => 'CreateModelOutput', ], 'errors' => [ [ 'shape' => 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'http' => [ 'method' => 'POST', 'requestUri' => '/', ], 'input' => [ 'shape' => 'DescribeModelInput', ], 'output' => [ 'shape' => 'DescribeModelOutput', ], ], 'DescribeModelPackage' => [ 'name' => 'DescribeModelPackage', 'http' => [ 'method' => 'POST', 'requestUri' => '/', ], 'input' => [ 'shape' => 'DescribeModelPackageInput', ], 'output' => [ 'shape' => 'DescribeModelPackageOutput', ], ], 'DescribeNotebookInstance' => [ 'name' => 'DescribeNotebookInstance', 'http' => [ 'method' => 'POST', 'requestUri' => '/', ], 'input' => [ 'shape' => 'DescribeNotebookInstanceInput', ], 'output' => [ 'shape' => 'DescribeNotebookInstanceOutput', ], ], 'DescribeNotebookInstanceLifecycleConfig' => [ 'name' => 'DescribeNotebookInstanceLifecycleConfig', 'http' => [ 'method' => 'POST', 'requestUri' => '/', ], 'input' => [ 'shape' => 'DescribeNotebookInstanceLifecycleConfigInput', ], 'output' => [ 'shape' => 'DescribeNotebookInstanceLifecycleConfigOutput', ], ], 'DescribeSubscribedWorkteam' => [ 'name' => 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'NonRetryableError' => [ 'shape' => 'TrainingJobStatusCounter', ], 'Stopped' => [ 'shape' => 'TrainingJobStatusCounter', ], ], ], 'TrainingJobSummaries' => [ 'type' => 'list', 'member' => [ 'shape' => 'TrainingJobSummary', ], ], 'TrainingJobSummary' => [ 'type' => 'structure', 'required' => [ 'TrainingJobName', 'TrainingJobArn', 'CreationTime', 'TrainingJobStatus', ], 'members' => [ 'TrainingJobName' => [ 'shape' => 'TrainingJobName', ], 'TrainingJobArn' => [ 'shape' => 'TrainingJobArn', ], 'CreationTime' => [ 'shape' => 'Timestamp', ], 'TrainingEndTime' => [ 'shape' => 'Timestamp', ], 'LastModifiedTime' => [ 'shape' => 'Timestamp', ], 'TrainingJobStatus' => [ 'shape' => 'TrainingJobStatus', ], ], ], 'TrainingSpecification' => [ 'type' => 'structure', 'required' => [ 'TrainingImage', 'SupportedTrainingInstanceTypes', 'TrainingChannels', ], 'members' => [ 'TrainingImage' => [ 'shape' => 'Image', ], 'TrainingImageDigest' => [ 'shape' => 'ImageDigest', ], 'SupportedHyperParameters' => [ 'shape' => 'HyperParameterSpecifications', ], 'SupportedTrainingInstanceTypes' => [ 'shape' => 'TrainingInstanceTypes', ], 'SupportsDistributedTraining' => [ 'shape' => 'Boolean', ], 'MetricDefinitions' => [ 'shape' => 'MetricDefinitionList', ], 'TrainingChannels' => [ 'shape' => 'ChannelSpecifications', ], 'SupportedTuningJobObjectiveMetrics' => [ 'shape' => 'HyperParameterTuningJobObjectives', ], ], ], 'TransformDataSource' => [ 'type' => 'structure', 'required' => [ 'S3DataSource', ], 'members' => [ 'S3DataSource' => [ 'shape' => 'TransformS3DataSource', ], ], ], 'TransformEnvironmentKey' => [ 'type' => 'string', 'max' => 1024, 'pattern' => '[a-zA-Z_][a-zA-Z0-9_]*', ], 'TransformEnvironmentMap' => [ 'type' => 'map', 'key' => [ 'shape' => 'TransformEnvironmentKey', ], 'value' => [ 'shape' => 'TransformEnvironmentValue', ], 'max' => 16, ], 'TransformEnvironmentValue' => [ 'type' => 'string', 'max' => 10240, 'pattern' => '[\\S\\s]*', ], 'TransformInput' => [ 'type' => 'structure', 'required' => [ 'DataSource', ], 'members' => [ 'DataSource' => [ 'shape' => 'TransformDataSource', ], 'ContentType' => [ 'shape' => 'ContentType', ], 'CompressionType' => [ 'shape' => 'CompressionType', ], 'SplitType' => [ 'shape' => 'SplitType', ], ], ], 'TransformInstanceCount' => [ 'type' => 'integer', 'min' => 1, ], 'TransformInstanceType' => [ 'type' => 'string', 'enum' => [ 'ml.m4.xlarge', 'ml.m4.2xlarge', 'ml.m4.4xlarge', 'ml.m4.10xlarge', 'ml.m4.16xlarge', 'ml.c4.xlarge', 'ml.c4.2xlarge', 'ml.c4.4xlarge', 'ml.c4.8xlarge', 'ml.p2.xlarge', 'ml.p2.8xlarge', 'ml.p2.16xlarge', 'ml.p3.2xlarge', 'ml.p3.8xlarge', 'ml.p3.16xlarge', 'ml.c5.xlarge', 'ml.c5.2xlarge', 'ml.c5.4xlarge', 'ml.c5.9xlarge', 'ml.c5.18xlarge', 'ml.m5.large', 'ml.m5.xlarge', 'ml.m5.2xlarge', 'ml.m5.4xlarge', 'ml.m5.12xlarge', 'ml.m5.24xlarge', ], ], 'TransformInstanceTypes' => [ 'type' => 'list', 'member' => [ 'shape' => 'TransformInstanceType', ], 'min' => 1, ], 'TransformJobArn' => [ 'type' => 'string', 'max' => 256, 'pattern' => 'arn:aws[a-z\\-]*:sagemaker:[a-z0-9\\-]*:[0-9]{12}:transform-job/.*', ], 'TransformJobDefinition' => [ 'type' => 'structure', 'required' => [ 'TransformInput', 'TransformOutput', 'TransformResources', ], 'members' => [ 'MaxConcurrentTransforms' => [ 'shape' => 'MaxConcurrentTransforms', ], 'MaxPayloadInMB' => [ 'shape' => 'MaxPayloadInMB', ], 'BatchStrategy' => [ 'shape' => 'BatchStrategy', ], 'Environment' => [ 'shape' => 'TransformEnvironmentMap', ], 'TransformInput' => [ 'shape' => 'TransformInput', ], 'TransformOutput' => [ 'shape' => 'TransformOutput', ], 'TransformResources' => [ 'shape' => 'TransformResources', ], ], ], 'TransformJobName' => [ 'type' => 'string', 'max' => 63, 'min' => 1, 'pattern' => '^[a-zA-Z0-9](-*[a-zA-Z0-9])*', ], 'TransformJobStatus' => [ 'type' => 'string', 'enum' => [ 'InProgress', 'Completed', 'Failed', 'Stopping', 'Stopped', ], ], 'TransformJobSummaries' => [ 'type' => 'list', 'member' => [ 'shape' => 'TransformJobSummary', ], ], 'TransformJobSummary' => [ 'type' => 'structure', 'required' => [ 'TransformJobName', 'TransformJobArn', 'CreationTime', 'TransformJobStatus', ], 'members' => [ 'TransformJobName' => [ 'shape' => 'TransformJobName', ], 'TransformJobArn' => [ 'shape' => 'TransformJobArn', ], 'CreationTime' => [ 'shape' => 'Timestamp', ], 'TransformEndTime' => [ 'shape' => 'Timestamp', ], 'LastModifiedTime' => [ 'shape' => 'Timestamp', ], 'TransformJobStatus' => [ 'shape' => 'TransformJobStatus', ], 'FailureReason' => [ 'shape' => 'FailureReason', ], ], ], 'TransformOutput' => [ 'type' => 'structure', 'required' => [ 'S3OutputPath', ], 'members' => [ 'S3OutputPath' => [ 'shape' => 'S3Uri', ], 'Accept' => [ 'shape' => 'Accept', ], 'AssembleWith' => [ 'shape' => 'AssemblyType', ], 'KmsKeyId' => [ 'shape' => 'KmsKeyId', ], ], ], 'TransformResources' => [ 'type' => 'structure', 'required' => [ 'InstanceType', 'InstanceCount', ], 'members' => [ 'InstanceType' => [ 'shape' => 'TransformInstanceType', ], 'InstanceCount' => [ 'shape' => 'TransformInstanceCount', ], 'VolumeKmsKeyId' => [ 'shape' => 'KmsKeyId', ], ], ], 'TransformS3DataSource' => [ 'type' => 'structure', 'required' => [ 'S3DataType', 'S3Uri', ], 'members' => [ 'S3DataType' => [ 'shape' => 'S3DataType', ], 'S3Uri' => [ 'shape' => 'S3Uri', ], ], ], 'USD' => [ 'type' => 'structure', 'members' => [ 'Dollars' => [ 'shape' => 'Dollars', ], 'Cents' => [ 'shape' => 'Cents', ], 'TenthFractionsOfACent' => [ 'shape' => 'TenthFractionsOfACent', ], ], ], 'UiConfig' => [ 'type' => 'structure', 'required' => [ 'UiTemplateS3Uri', ], 'members' => [ 'UiTemplateS3Uri' => [ 'shape' => 'S3Uri', ], ], ], 'UiTemplate' => [ 'type' => 'structure', 'required' => [ 'Content', ], 'members' => [ 'Content' => [ 'shape' => 'TemplateContent', ], ], ], 'UpdateCodeRepositoryInput' => [ 'type' => 'structure', 'required' => [ 'CodeRepositoryName', ], 'members' => [ 'CodeRepositoryName' => [ 'shape' => 'EntityName', ], 'GitConfig' => [ 'shape' => 'GitConfigForUpdate', ], ], ], 'UpdateCodeRepositoryOutput' => [ 'type' => 'structure', 'required' => [ 'CodeRepositoryArn', ], 'members' => [ 'CodeRepositoryArn' => [ 'shape' => 'CodeRepositoryArn', ], ], ], 'UpdateEndpointInput' => [ 'type' => 'structure', 'required' => [ 'EndpointName', 'EndpointConfigName', ], 'members' => [ 'EndpointName' => [ 'shape' => 'EndpointName', ], 'EndpointConfigName' => [ 'shape' => 'EndpointConfigName', ], ], ], 'UpdateEndpointOutput' => [ 'type' => 'structure', 'required' => [ 'EndpointArn', ], 'members' => [ 'EndpointArn' => [ 'shape' => 'EndpointArn', ], ], ], 'UpdateEndpointWeightsAndCapacitiesInput' => [ 'type' => 'structure', 'required' => [ 'EndpointName', 'DesiredWeightsAndCapacities', ], 'members' => [ 'EndpointName' => [ 'shape' => 'EndpointName', ], 'DesiredWeightsAndCapacities' => [ 'shape' => 'DesiredWeightAndCapacityList', ], ], ], 'UpdateEndpointWeightsAndCapacitiesOutput' => [ 'type' => 'structure', 'required' => [ 'EndpointArn', ], 'members' => [ 'EndpointArn' => [ 'shape' => 'EndpointArn', ], ], ], 'UpdateNotebookInstanceInput' => [ 'type' => 'structure', 'required' => [ 'NotebookInstanceName', ], 'members' => [ 'NotebookInstanceName' => [ 'shape' => 'NotebookInstanceName', ], 'InstanceType' => [ 'shape' => 'InstanceType', ], 'RoleArn' => [ 'shape' => 'RoleArn', ], 'LifecycleConfigName' => [ 'shape' => 'NotebookInstanceLifecycleConfigName', ], 'DisassociateLifecycleConfig' => [ 'shape' => 'DisassociateNotebookInstanceLifecycleConfig', ], 'VolumeSizeInGB' => [ 'shape' => 'NotebookInstanceVolumeSizeInGB', ], 'DefaultCodeRepository' => [ 'shape' => 'CodeRepositoryNameOrUrl', ], 'AdditionalCodeRepositories' => [ 'shape' => 'AdditionalCodeRepositoryNamesOrUrls', ], 'AcceleratorTypes' => [ 'shape' => 'NotebookInstanceAcceleratorTypes', ], 'DisassociateAcceleratorTypes' => [ 'shape' => 'DisassociateNotebookInstanceAcceleratorTypes', ], 'DisassociateDefaultCodeRepository' => [ 'shape' => 'DisassociateDefaultCodeRepository', ], 'DisassociateAdditionalCodeRepositories' => [ 'shape' => 'DisassociateAdditionalCodeRepositories', ], 'RootAccess' => [ 'shape' => 'RootAccess', ], ], ], 'UpdateNotebookInstanceLifecycleConfigInput' => [ 'type' => 'structure', 'required' => [ 'NotebookInstanceLifecycleConfigName', ], 'members' => [ 'NotebookInstanceLifecycleConfigName' => [ 'shape' => 'NotebookInstanceLifecycleConfigName', ], 'OnCreate' => [ 'shape' => 'NotebookInstanceLifecycleConfigList', ], 'OnStart' => [ 'shape' => 'NotebookInstanceLifecycleConfigList', ], ], ], 'UpdateNotebookInstanceLifecycleConfigOutput' => [ 'type' => 'structure', 'members' => [], ], 'UpdateNotebookInstanceOutput' => [ 'type' => 'structure', 'members' => [], ], 'UpdateWorkteamRequest' => [ 'type' => 'structure', 'required' => [ 'WorkteamName', ], 'members' => [ 'WorkteamName' => [ 'shape' => 'WorkteamName', ], 'MemberDefinitions' => [ 'shape' => 'MemberDefinitions', ], 'Description' => [ 'shape' => 'String200', ], 'NotificationConfiguration' => [ 'shape' => 'NotificationConfiguration', ], ], ], 'UpdateWorkteamResponse' => [ 'type' => 'structure', 'required' => [ 'Workteam', ], 'members' => [ 'Workteam' => [ 'shape' => 'Workteam', ], ], ], 'Url' => [ 'type' => 'string', 'max' => 1024, 'pattern' => '^(https|s3)://([^/]+)/?(.*)$', ], 'VariantName' => [ 'type' => 'string', 'max' => 63, 'pattern' => '^[a-zA-Z0-9](-*[a-zA-Z0-9])*', ], 'VariantWeight' => [ 'type' => 'float', 'min' => 0, ], 'VolumeSizeInGB' => [ 'type' => 'integer', 'min' => 1, ], 'VpcConfig' => [ 'type' => 'structure', 'required' => [ 'SecurityGroupIds', 'Subnets', ], 'members' => [ 'SecurityGroupIds' => [ 'shape' => 'VpcSecurityGroupIds', ], 'Subnets' => [ 'shape' => 'Subnets', ], ], ], 'VpcSecurityGroupIds' => [ 'type' => 'list', 'member' => [ 'shape' => 'SecurityGroupId', ], 'max' => 5, 'min' => 1, ], 'Workteam' => [ 'type' => 'structure', 'required' => [ 'WorkteamName', 'MemberDefinitions', 'WorkteamArn', 'Description', ], 'members' => [ 'WorkteamName' => [ 'shape' => 'WorkteamName', ], 'MemberDefinitions' => [ 'shape' => 'MemberDefinitions', ], 'WorkteamArn' => [ 'shape' => 'WorkteamArn', ], 'ProductListingIds' => [ 'shape' => 'ProductListings', ], 'Description' => [ 'shape' => 'String200', ], 'SubDomain' => [ 'shape' => 'String', ], 'CreateDate' => [ 'shape' => 'Timestamp', ], 'LastUpdatedDate' => [ 'shape' => 'Timestamp', ], 'NotificationConfiguration' => [ 'shape' => 'NotificationConfiguration', ], ], ], 'WorkteamArn' => [ 'type' => 'string', 'max' => 256, 'pattern' => 'arn:aws[a-z\\-]*:sagemaker:[a-z0-9\\-]*:[0-9]{12}:workteam/.*', ], 'WorkteamName' => [ 'type' => 'string', 'max' => 63, 'min' => 1, 'pattern' => '^[a-zA-Z0-9](-*[a-zA-Z0-9])*', ], 'Workteams' => [ 'type' => 'list', 'member' => [ 'shape' => 'Workteam', ], ], ],]; +return [ 'version' => '2.0', 'metadata' => [ 'apiVersion' => '2017-07-24', 'endpointPrefix' => 'api.sagemaker', 'jsonVersion' => '1.1', 'protocol' => 'json', 'serviceAbbreviation' => 'SageMaker', 'serviceFullName' => 'Amazon SageMaker Service', 'serviceId' => 'SageMaker', 'signatureVersion' => 'v4', 'signingName' => 'sagemaker', 'targetPrefix' => 'SageMaker', 'uid' => 'sagemaker-2017-07-24', ], 'operations' => [ 'AddTags' => [ 'name' => 'AddTags', 'http' => [ 'method' => 'POST', 'requestUri' => '/', ], 'input' => [ 'shape' => 'AddTagsInput', ], 'output' => [ 'shape' => 'AddTagsOutput', ], ], 'CreateAlgorithm' => [ 'name' => 'CreateAlgorithm', 'http' => [ 'method' => 'POST', 'requestUri' => '/', ], 'input' => [ 'shape' => 'CreateAlgorithmInput', ], 'output' => [ 'shape' => 'CreateAlgorithmOutput', ], ], 'CreateCodeRepository' => [ 'name' => 'CreateCodeRepository', 'http' => [ 'method' => 'POST', 'requestUri' => '/', ], 'input' => [ 'shape' => 'CreateCodeRepositoryInput', ], 'output' => [ 'shape' => 'CreateCodeRepositoryOutput', ], ], 'CreateCompilationJob' => [ 'name' => 'CreateCompilationJob', 'http' => [ 'method' => 'POST', 'requestUri' => '/', ], 'input' => [ 'shape' => 'CreateCompilationJobRequest', ], 'output' => [ 'shape' => 'CreateCompilationJobResponse', ], 'errors' => [ [ 'shape' => 'ResourceInUse', ], [ 'shape' => 'ResourceLimitExceeded', ], ], ], 'CreateEndpoint' => [ 'name' => 'CreateEndpoint', 'http' => [ 'method' => 'POST', 'requestUri' => '/', ], 'input' => [ 'shape' => 'CreateEndpointInput', ], 'output' => [ 'shape' => 'CreateEndpointOutput', ], 'errors' => [ [ 'shape' => 'ResourceLimitExceeded', ], ], ], 'CreateEndpointConfig' => [ 'name' => 'CreateEndpointConfig', 'http' => [ 'method' => 'POST', 'requestUri' => '/', ], 'input' => [ 'shape' => 'CreateEndpointConfigInput', ], 'output' => [ 'shape' => 'CreateEndpointConfigOutput', ], 'errors' => [ [ 'shape' => 'ResourceLimitExceeded', ], ], ], 'CreateHyperParameterTuningJob' => [ 'name' => 'CreateHyperParameterTuningJob', 'http' => [ 'method' => 'POST', 'requestUri' => '/', ], 'input' => [ 'shape' => 'CreateHyperParameterTuningJobRequest', ], 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'Cents' => [ 'shape' => 'Cents', ], 'TenthFractionsOfACent' => [ 'shape' => 'TenthFractionsOfACent', ], ], ], 'UiConfig' => [ 'type' => 'structure', 'required' => [ 'UiTemplateS3Uri', ], 'members' => [ 'UiTemplateS3Uri' => [ 'shape' => 'S3Uri', ], ], ], 'UiTemplate' => [ 'type' => 'structure', 'required' => [ 'Content', ], 'members' => [ 'Content' => [ 'shape' => 'TemplateContent', ], ], ], 'UpdateCodeRepositoryInput' => [ 'type' => 'structure', 'required' => [ 'CodeRepositoryName', ], 'members' => [ 'CodeRepositoryName' => [ 'shape' => 'EntityName', ], 'GitConfig' => [ 'shape' => 'GitConfigForUpdate', ], ], ], 'UpdateCodeRepositoryOutput' => [ 'type' => 'structure', 'required' => [ 'CodeRepositoryArn', ], 'members' => [ 'CodeRepositoryArn' => [ 'shape' => 'CodeRepositoryArn', ], ], ], 'UpdateEndpointInput' => [ 'type' => 'structure', 'required' => [ 'EndpointName', 'EndpointConfigName', ], 'members' => [ 'EndpointName' => [ 'shape' => 'EndpointName', ], 'EndpointConfigName' => [ 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'NotebookInstanceLifecycleConfigName', ], 'OnCreate' => [ 'shape' => 'NotebookInstanceLifecycleConfigList', ], 'OnStart' => [ 'shape' => 'NotebookInstanceLifecycleConfigList', ], ], ], 'UpdateNotebookInstanceLifecycleConfigOutput' => [ 'type' => 'structure', 'members' => [], ], 'UpdateNotebookInstanceOutput' => [ 'type' => 'structure', 'members' => [], ], 'UpdateWorkteamRequest' => [ 'type' => 'structure', 'required' => [ 'WorkteamName', ], 'members' => [ 'WorkteamName' => [ 'shape' => 'WorkteamName', ], 'MemberDefinitions' => [ 'shape' => 'MemberDefinitions', ], 'Description' => [ 'shape' => 'String200', ], 'NotificationConfiguration' => [ 'shape' => 'NotificationConfiguration', ], ], ], 'UpdateWorkteamResponse' => [ 'type' => 'structure', 'required' => [ 'Workteam', ], 'members' => [ 'Workteam' => [ 'shape' => 'Workteam', ], ], ], 'Url' => [ 'type' => 'string', 'max' => 1024, 'pattern' => '^(https|s3)://([^/]+)/?(.*)$', ], 'VariantName' => [ 'type' => 'string', 'max' => 63, 'pattern' => '^[a-zA-Z0-9](-*[a-zA-Z0-9])*', ], 'VariantWeight' => [ 'type' => 'float', 'min' => 0, ], 'VolumeSizeInGB' => [ 'type' => 'integer', 'min' => 1, ], 'VpcConfig' => [ 'type' => 'structure', 'required' => [ 'SecurityGroupIds', 'Subnets', ], 'members' => [ 'SecurityGroupIds' => [ 'shape' => 'VpcSecurityGroupIds', ], 'Subnets' => [ 'shape' => 'Subnets', ], ], ], 'VpcSecurityGroupIds' => [ 'type' => 'list', 'member' => [ 'shape' => 'SecurityGroupId', ], 'max' => 5, 'min' => 1, ], 'Workteam' => [ 'type' => 'structure', 'required' => [ 'WorkteamName', 'MemberDefinitions', 'WorkteamArn', 'Description', ], 'members' => [ 'WorkteamName' => [ 'shape' => 'WorkteamName', ], 'MemberDefinitions' => [ 'shape' => 'MemberDefinitions', ], 'WorkteamArn' => [ 'shape' => 'WorkteamArn', ], 'ProductListingIds' => [ 'shape' => 'ProductListings', ], 'Description' => [ 'shape' => 'String200', ], 'SubDomain' => [ 'shape' => 'String', ], 'CreateDate' => [ 'shape' => 'Timestamp', ], 'LastUpdatedDate' => [ 'shape' => 'Timestamp', ], 'NotificationConfiguration' => [ 'shape' => 'NotificationConfiguration', ], ], ], 'WorkteamArn' => [ 'type' => 'string', 'max' => 256, 'pattern' => 'arn:aws[a-z\\-]*:sagemaker:[a-z0-9\\-]*:[0-9]{12}:workteam/.*', ], 'WorkteamName' => [ 'type' => 'string', 'max' => 63, 'min' => 1, 'pattern' => '^[a-zA-Z0-9](-*[a-zA-Z0-9])*', ], 'Workteams' => [ 'type' => 'list', 'member' => [ 'shape' => 'Workteam', ], ], ],]; diff --git a/src/data/sagemaker/2017-07-24/docs-2.json b/src/data/sagemaker/2017-07-24/docs-2.json index 4fae1c96c3..b355e4fbd6 100644 --- a/src/data/sagemaker/2017-07-24/docs-2.json +++ b/src/data/sagemaker/2017-07-24/docs-2.json @@ -14,8 +14,8 @@ "CreateModelPackage": "

Creates a model package that you can use to create Amazon SageMaker models or list on AWS Marketplace. Buyers can subscribe to model packages listed on AWS Marketplace to create models in Amazon SageMaker.

To create a model package by specifying a Docker container that contains your inference code and the Amazon S3 location of your model artifacts, provide values for InferenceSpecification. To create a model from an algorithm resource that you created or subscribed to in AWS Marketplace, provide a value for SourceAlgorithmSpecification.

", "CreateNotebookInstance": "

Creates an Amazon SageMaker notebook instance. A notebook instance is a machine learning (ML) compute instance running on a Jupyter notebook.

In a CreateNotebookInstance request, specify the type of ML compute instance that you want to run. Amazon SageMaker launches the instance, installs common libraries that you can use to explore datasets for model training, and attaches an ML storage volume to the notebook instance.

Amazon SageMaker also provides a set of example notebooks. Each notebook demonstrates how to use Amazon SageMaker with a specific algorithm or with a machine learning framework.

After receiving the request, Amazon SageMaker does the following:

  1. Creates a network interface in the Amazon SageMaker VPC.

  2. (Option) If you specified SubnetId, Amazon SageMaker creates a network interface in your own VPC, which is inferred from the subnet ID that you provide in the input. When creating this network interface, Amazon SageMaker attaches the security group that you specified in the request to the network interface that it creates in your VPC.

  3. Launches an EC2 instance of the type specified in the request in the Amazon SageMaker VPC. If you specified SubnetId of your VPC, Amazon SageMaker specifies both network interfaces when launching this instance. This enables inbound traffic from your own VPC to the notebook instance, assuming that the security groups allow it.

After creating the notebook instance, Amazon SageMaker returns its Amazon Resource Name (ARN).

After Amazon SageMaker creates the notebook instance, you can connect to the Jupyter server and work in Jupyter notebooks. For example, you can write code to explore a dataset that you can use for model training, train a model, host models by creating Amazon SageMaker endpoints, and validate hosted models.

For more information, see How It Works.

", "CreateNotebookInstanceLifecycleConfig": "

Creates a lifecycle configuration that you can associate with a notebook instance. A lifecycle configuration is a collection of shell scripts that run when you create or start a notebook instance.

Each lifecycle configuration script has a limit of 16384 characters.

The value of the $PATH environment variable that is available to both scripts is /sbin:bin:/usr/sbin:/usr/bin.

View CloudWatch Logs for notebook instance lifecycle configurations in log group /aws/sagemaker/NotebookInstances in log stream [notebook-instance-name]/[LifecycleConfigHook].

Lifecycle configuration scripts cannot run for longer than 5 minutes. If a script runs for longer than 5 minutes, it fails and the notebook instance is not created or started.

For information about notebook instance lifestyle configurations, see Step 2.1: (Optional) Customize a Notebook Instance.

", - "CreatePresignedNotebookInstanceUrl": "

Returns a URL that you can use to connect to the Jupyter server from a notebook instance. In the Amazon SageMaker console, when you choose Open next to a notebook instance, Amazon SageMaker opens a new tab showing the Jupyter server home page from the notebook instance. The console uses this API to get the URL and show the page.

You can restrict access to this API and to the URL that it returns to a list of IP addresses that you specify. To restrict access, attach an IAM policy that denies access to this API unless the call comes from an IP address in the specified list to every AWS Identity and Access Management user, group, or role used to access the notebook instance. Use the NotIpAddress condition operator and the aws:SourceIP condition context key to specify the list of IP addresses that you want to have access to the notebook instance. For more information, see Limit Access to a Notebook Instance by IP Address.

The URL that you get from a call to is valid only for 5 minutes. If you try to use the URL after the 5-minute limit expires, you are directed to the AWS console sign-in page.

", - "CreateTrainingJob": "

Starts a model training job. After training completes, Amazon SageMaker saves the resulting model artifacts to an Amazon S3 location that you specify.

If you choose to host your model using Amazon SageMaker hosting services, you can use the resulting model artifacts as part of the model. You can also use the artifacts in a machine learning service other than Amazon SageMaker, provided that you know how to use them for inferences.

In the request body, you provide the following:

For more information about Amazon SageMaker, see How It Works.

", + "CreatePresignedNotebookInstanceUrl": "

Returns a URL that you can use to connect to the Jupyter server from a notebook instance. In the Amazon SageMaker console, when you choose Open next to a notebook instance, Amazon SageMaker opens a new tab showing the Jupyter server home page from the notebook instance. The console uses this API to get the URL and show the page.

IAM authorization policies for this API are also enforced for every HTTP request and WebSocket frame that attempts to connect to the notebook instance.For example, you can restrict access to this API and to the URL that it returns to a list of IP addresses that you specify. Use the NotIpAddress condition operator and the aws:SourceIP condition context key to specify the list of IP addresses that you want to have access to the notebook instance. For more information, see Limit Access to a Notebook Instance by IP Address.

The URL that you get from a call to is valid only for 5 minutes. If you try to use the URL after the 5-minute limit expires, you are directed to the AWS console sign-in page.

", + "CreateTrainingJob": "

Starts a model training job. After training completes, Amazon SageMaker saves the resulting model artifacts to an Amazon S3 location that you specify.

If you choose to host your model using Amazon SageMaker hosting services, you can use the resulting model artifacts as part of the model. You can also use the artifacts in a machine learning service other than Amazon SageMaker, provided that you know how to use them for inferences.

In the request body, you provide the following:

For more information about Amazon SageMaker, see How It Works.

", "CreateTransformJob": "

Starts a transform job. A transform job uses a trained model to get inferences on a dataset and saves these results to an Amazon S3 location that you specify.

To perform batch transformations, you create a transform job and use the data that you have readily available.

In the request body, you provide the following:

For more information about how batch transformation works Amazon SageMaker, see How It Works.

", "CreateWorkteam": "

Creates a new work team for labeling your data. A work team is defined by one or more Amazon Cognito user pools. You must first create the user pools before you can create a work team.

You cannot create more than 25 work teams in an account and region.

", "DeleteAlgorithm": "

Removes the specified algorithm from your account.

", @@ -206,7 +206,7 @@ "base": null, "refs": { "AlgorithmSpecification$AlgorithmName": "

The name of the algorithm resource to use for the training job. This must be an algorithm resource that you created or subscribe to on AWS Marketplace. If you specify a value for this parameter, you can't specify a value for TrainingImage.

", - "ContainerDefinition$ModelPackageName": "

The name of the model package to use to create the model.

", + "ContainerDefinition$ModelPackageName": "

The name or Amazon Resource Name (ARN) of the model package to use to create the model.

", "DescribeAlgorithmInput$AlgorithmName": "

The name of the algorithm to describe.

", "DescribeModelPackageInput$ModelPackageName": "

The name of the model package to describe.

", "HyperParameterAlgorithmSpecification$AlgorithmName": "

The name of the resource algorithm to use for the hyperparameter tuning job. If you specify a value for this parameter, do not specify a value for TrainingImage.

", @@ -248,7 +248,7 @@ "CreateTrainingJobRequest$EnableInterContainerTrafficEncryption": "

To encrypt all communications between ML compute instances in distributed training, choose True. Encryption provides greater security for distributed training, but training might take longer. How long it takes depends on the amount of communication between compute instances, especially if you use a deep learning algorithm in distributed training. For more information, see Protect Communications Between ML Compute Instances in a Distributed Training Job.

", "DescribeModelOutput$EnableNetworkIsolation": "

If True, no inbound or outbound network calls can be made to or from the model container.

The Semantic Segmentation built-in algorithm does not support network isolation.

", "DescribeTrainingJobResponse$EnableNetworkIsolation": "

If you want to allow inbound or outbound network calls, except for calls between peers within a training cluster for distributed training, choose True. If you enable network isolation for training jobs that are configured to use a VPC, Amazon SageMaker downloads and uploads customer data and model artifacts through the specified VPC, but the training container does not have network access.

The Semantic Segmentation built-in algorithm does not support network isolation.

", - "DescribeTrainingJobResponse$EnableInterContainerTrafficEncryption": "

To encrypt all communications between ML compute instances in distributed training, choose True. Encryption provides greater security for distributed training, but training might take longer. How long it takes depends on the amount of communication between compute instances, especially if you use a deep learning algorithm in distributed training.

", + "DescribeTrainingJobResponse$EnableInterContainerTrafficEncryption": "

To encrypt all communications between ML compute instances in distributed training, choose True. Encryption provides greater security for distributed training, but training might take longer. How long it takes depends on the amount of communication between compute instances, especially if you use a deep learning algorithms in distributed training.

", "HyperParameterSpecification$IsTunable": "

Indicates whether this hyperparameter is tunable in a hyperparameter tuning job.

", "HyperParameterSpecification$IsRequired": "

Indicates whether this hyperparameter is required.

", "HyperParameterTrainingJobDefinition$EnableNetworkIsolation": "

Isolates the training container. No inbound or outbound network calls can be made, except for calls between peers within a training cluster for distributed training. If network isolation is used for training jobs that are configured to use a VPC, Amazon SageMaker downloads and uploads customer data and model artifacts through the specified VPC, but the training container does not have network access.

The Semantic Segmentation built-in algorithm does not support network isolation.

", @@ -702,6 +702,13 @@ "InputConfig$DataInputConfig": "

Specifies the name and shape of the expected data inputs for your trained model with a JSON dictionary form. The data inputs are InputConfig$Framework specific.

" } }, + "DataProcessing": { + "base": "

The data structure used to combine the input data and transformed data from the batch transform output into a joined dataset and to store it in an output file. It also contains information on how to filter the input data and the joined dataset. For more information, see Batch Transform I/O Join.

", + "refs": { + "CreateTransformJobRequest$DataProcessing": "

The data structure used for combining the input data and inference in the output file. For more information, see Batch Transform I/O Join.

", + "DescribeTransformJobResponse$DataProcessing": null + } + }, "DataSource": { "base": "

Describes the location of the channel data.

", "refs": { @@ -1258,7 +1265,7 @@ "HyperParameterScalingType": { "base": null, "refs": { - "ContinuousParameterRange$ScalingType": "

The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling. One of the following values:

Auto

Amazon SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.

Linear

Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.

Logarithmic

Hyperparemeter tuning searches the values in the hyperparameter range by using a logarithmic scale.

Logarithmic scaling works only for ranges that have only values greater than 0.

ReverseLogarithmic

Hyperparemeter tuning searches the values in the hyperparameter range by using a reverse logarithmic scale.

Reverse logarithmic scaling works only for ranges that are entirely within the range 0<=x<1.0.

", + "ContinuousParameterRange$ScalingType": "

The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling. One of the following values:

Auto

Amazon SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.

Linear

Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.

Logarithmic

Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale.

Logarithmic scaling works only for ranges that have only values greater than 0.

ReverseLogarithmic

Hyperparemeter tuning searches the values in the hyperparameter range by using a reverse logarithmic scale.

Reverse logarithmic scaling works only for ranges that are entirely within the range 0<=x<1.0.

", "IntegerParameterRange$ScalingType": "

The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling. One of the following values:

Auto

Amazon SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.

Linear

Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.

Logarithmic

Hyperparemeter tuning searches the values in the hyperparameter range by using a logarithmic scale.

Logarithmic scaling works only for ranges that have only values greater than 0.

" } }, @@ -1491,6 +1498,19 @@ "ListLabelingJobsForWorkteamRequest$JobReferenceCodeContains": "

A filter the limits jobs to only the ones whose job reference code contains the specified string.

" } }, + "JoinSource": { + "base": null, + "refs": { + "DataProcessing$JoinSource": "

Specifies the source of the data to join with the transformed data. The valid values are None and Input The default value is None which specifies not to join the input with the transformed data. If you want the batch transform job to join the original input data with the transformed data, set JoinSource to Input. To join input and output, the batch transform job must satisfy the Requirements for Using Batch Transform I/O Join.

For JSON or JSONLines objects, such as a JSON array, Amazon SageMaker adds the transformed data to the input JSON object in an attribute called SageMakerOutput. The joined result for JSON must be a key-value pair object. If the input is not a key-value pair object, Amazon SageMaker creates a new JSON file. In the new JSON file, and the input data is stored under the SageMakerInput key and the results are stored in SageMakerOutput.

For CSV files, Amazon SageMaker combines the transformed data with the input data at the end of the input data and stores it in the output file. The joined data has the joined input data followed by the transformed data and the output is a CSV file.

" + } + }, + "JsonPath": { + "base": null, + "refs": { + "DataProcessing$InputFilter": "

A JSONPath expression used to select a portion of the input data to pass to the algorithm. Use the InputFilter parameter to exclude fields, such as an ID column, from the input. If you want Amazon SageMaker to pass the entire input dataset to the algorithm, accept the default value $.

Examples: \"$\", \"$[1:]\", \"$.features\"

", + "DataProcessing$OutputFilter": "

A JSONPath expression used to select a portion of the joined dataset to save in the output file for a batch transform job. If you want Amazon SageMaker to store the entire input dataset in the output file, leave the default value, $. If you specify indexes that aren't within the dimension size of the joined dataset, you get an error.

Examples: \"$\", \"$[0,5:]\", \"$.['id','SageMakerOutput']\"

" + } + }, "KmsKeyId": { "base": null, "refs": { @@ -1498,9 +1518,9 @@ "CreateNotebookInstanceInput$KmsKeyId": "

The Amazon Resource Name (ARN) of a AWS Key Management Service key that Amazon SageMaker uses to encrypt data on the storage volume attached to your notebook instance. The KMS key you provide must be enabled. For information, see Enabling and Disabling Keys in the AWS Key Management Service Developer Guide.

", "DescribeEndpointConfigOutput$KmsKeyId": "

AWS KMS key ID Amazon SageMaker uses to encrypt data when storing it on the ML storage volume attached to the instance.

", "DescribeNotebookInstanceOutput$KmsKeyId": "

The AWS KMS key ID Amazon SageMaker uses to encrypt data when storing it on the ML storage volume attached to the instance.

", - "LabelingJobOutputConfig$KmsKeyId": "

The AWS Key Management Service ID of the key used to encrypt the output data, if any.

", + "LabelingJobOutputConfig$KmsKeyId": "

The AWS Key Management Service ID of the key used to encrypt the output data, if any.

If you use a KMS key ID or an alias of your master key, the Amazon SageMaker execution role must include permissions to call kms:Encrypt. If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. Amazon SageMaker uses server-side encryption with KMS-managed keys for LabelingJobOutputConfig. If you use a bucket policy with an s3:PutObject permission that only allows objects with server-side encryption, set the condition key of s3:x-amz-server-side-encryption to \"aws:kms\". For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.

The KMS key policy must grant permission to the IAM role that you specify in your CreateLabelingJob request. For more information, see Using Key Policies in AWS KMS in the AWS Key Management Service Developer Guide.

", "LabelingJobResourceConfig$VolumeKmsKeyId": "

The AWS Key Management Service key ID for the key used to encrypt the output data, if any.

", - "OutputDataConfig$KmsKeyId": "

The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:

If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.

The KMS key policy must grant permission to the IAM role that you specify in your CreateTramsformJob request. For more information, see Using Key Policies in AWS KMS in the AWS Key Management Service Developer Guide.

", + "OutputDataConfig$KmsKeyId": "

The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:

If you use a KMS key ID or an alias of your master key, the Amazon SageMaker execution role must include permissions to call kms:Encrypt. If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. Amazon SageMaker uses server-side encryption with KMS-managed keys for OutputDataConfig. If you use a bucket policy with an s3:PutObject permission that only allows objects with server-side encryption, set the condition key of s3:x-amz-server-side-encryption to \"aws:kms\". For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.

The KMS key policy must grant permission to the IAM role that you specify in your CreateTrainingJob, CreateTransformJob, or CreateHyperParameterTuningJob requests. For more information, see Using Key Policies in AWS KMS in the AWS Key Management Service Developer Guide.

", "ResourceConfig$VolumeKmsKeyId": "

The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the training job. The VolumeKmsKeyId can be any of the following formats:

", "TransformOutput$KmsKeyId": "

The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:

If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.

The KMS key policy must grant permission to the IAM role that you specify in your CreateTramsformJob request. For more information, see Using Key Policies in AWS KMS in the AWS Key Management Service Developer Guide.

", "TransformResources$VolumeKmsKeyId": "

The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the batch transform job. The VolumeKmsKeyId can be any of the following formats:

" @@ -1663,7 +1683,7 @@ "base": null, "refs": { "AnnotationConsolidationConfig$AnnotationConsolidationLambdaArn": "

The Amazon Resource Name (ARN) of a Lambda function implements the logic for annotation consolidation.

For the built-in bounding box, image classification, semantic segmentation, and text classification task types, Amazon SageMaker Ground Truth provides the following Lambda functions:

For more information, see Annotation Consolidation.

", - "HumanTaskConfig$PreHumanTaskLambdaArn": "

The Amazon Resource Name (ARN) of a Lambda function that is run before a data object is sent to a human worker. Use this function to provide input to a custom labeling job.

For the built-in bounding box, image classification, semantic segmentation, and text classification task types, Amazon SageMaker Ground Truth provides the following Lambda functions:

US East (Northern Virginia) (us-east-1):

US East (Ohio) (us-east-2):

US West (Oregon) (us-west-2):

EU (Ireland) (eu-west-1):

Asia Pacific (Tokyo (ap-northeast-1):

Asia Pacific (Sydney (ap-southeast-1):

", + "HumanTaskConfig$PreHumanTaskLambdaArn": "

The Amazon Resource Name (ARN) of a Lambda function that is run before a data object is sent to a human worker. Use this function to provide input to a custom labeling job.

For the built-in bounding box, image classification, semantic segmentation, and text classification task types, Amazon SageMaker Ground Truth provides the following Lambda functions:

US East (Northern Virginia) (us-east-1):

US East (Ohio) (us-east-2):

US West (Oregon) (us-west-2):

EU (Ireland) (eu-west-1):

Asia Pacific (Tokyo) (ap-northeast-1):

Asia Pacific (Sydney) (ap-southeast-1):

", "LabelingJobSummary$PreHumanTaskLambdaArn": "

The Amazon Resource Name (ARN) of a Lambda function. The function is run before each data object is sent to a worker.

", "LabelingJobSummary$AnnotationConsolidationLambdaArn": "

The Amazon Resource Name (ARN) of the Lambda function used to consolidate the annotations from individual workers into a label for a data object. For more information, see Annotation Consolidation.

" } @@ -1963,7 +1983,7 @@ "MaxRuntimeInSeconds": { "base": null, "refs": { - "StoppingCondition$MaxRuntimeInSeconds": "

The maximum length of time, in seconds, that the training job can run. If model training does not complete during this time, Amazon SageMaker ends the job. If value is not specified, default value is 1 day. Maximum value is 28 days.

" + "StoppingCondition$MaxRuntimeInSeconds": "

The maximum length of time, in seconds, that the training or compilation job can run. If job does not complete during this time, Amazon SageMaker ends the job. If value is not specified, default value is 1 day. The maximum value is 28 days.

" } }, "MemberDefinition": { @@ -2390,7 +2410,7 @@ "refs": { "CreateNotebookInstanceInput$VolumeSizeInGB": "

The size, in GB, of the ML storage volume to attach to the notebook instance. The default value is 5 GB.

", "DescribeNotebookInstanceOutput$VolumeSizeInGB": "

The size, in GB, of the ML storage volume attached to the notebook instance.

", - "UpdateNotebookInstanceInput$VolumeSizeInGB": "

The size, in GB, of the ML storage volume to attach to the notebook instance. The default value is 5 GB.

" + "UpdateNotebookInstanceInput$VolumeSizeInGB": "

The size, in GB, of the ML storage volume to attach to the notebook instance. The default value is 5 GB. ML storage volumes are encrypted, so Amazon SageMaker can't determine the amount of available free space on the volume. Because of this, you can increase the volume size when you update a notebook instance, but you can't decrease the volume size. If you want to decrease the size of the ML storage volume in use, create a new notebook instance with the desired size.

" } }, "NotificationConfiguration": { @@ -2957,7 +2977,7 @@ "StatusMessage": { "base": null, "refs": { - "SecondaryStatusTransition$StatusMessage": "

A detailed description of the progress within a secondary status.

Amazon SageMaker provides secondary statuses and status messages that apply to each of them:

Starting

  • Starting the training job.

  • Launching requested ML instances.

  • Insufficient capacity error from EC2 while launching instances, retrying!

  • Launched instance was unhealthy, replacing it!

  • Preparing the instances for training.

Training

  • Downloading the training image.

  • Training image download completed. Training in progress.

Status messages are subject to change. Therefore, we recommend not including them in code that programmatically initiates actions. For examples, don't use status messages in if statements.

To have an overview of your training job's progress, view TrainingJobStatus and SecondaryStatus in DescribeTrainingJobResponse, and StatusMessage together. For example, at the start of a training job, you might see the following:

" + "SecondaryStatusTransition$StatusMessage": "

A detailed description of the progress within a secondary status.

Amazon SageMaker provides secondary statuses and status messages that apply to each of them:

Starting

  • Starting the training job.

  • Launching requested ML instances.

  • Insufficient capacity error from EC2 while launching instances, retrying!

  • Launched instance was unhealthy, replacing it!

  • Preparing the instances for training.

Training

  • Downloading the training image.

  • Training image download completed. Training in progress.

Status messages are subject to change. Therefore, we recommend not including them in code that programmatically initiates actions. For examples, don't use status messages in if statements.

To have an overview of your training job's progress, view TrainingJobStatus and SecondaryStatus in DescribeTrainingJob, and StatusMessage together. For example, at the start of a training job, you might see the following:

" } }, "StopCompilationJobRequest": { @@ -2991,15 +3011,15 @@ } }, "StoppingCondition": { - "base": "

Specifies how long model training can run. When model training reaches the limit, Amazon SageMaker ends the training job. Use this API to cap model training cost.

To stop a job, Amazon SageMaker sends the algorithm the SIGTERM signal, which delays job termination for120 seconds. Algorithms might use this 120-second window to save the model artifacts, so the results of training is not lost.

Training algorithms provided by Amazon SageMaker automatically saves the intermediate results of a model training job (it is best effort case, as model might not be ready to save as some stages, for example training just started). This intermediate data is a valid model artifact. You can use it to create a model (CreateModel).

", + "base": "

Specifies a limit to how long a model training or compilation job can run. When the job reaches the time limit, Amazon SageMaker ends the training or compilation job. Use this API to cap model training costs.

To stop a job, Amazon SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds. Algorithms can use this 120-second window to save the model artifacts, so the results of training are not lost.

The training algorithms provided by Amazon SageMaker automatically save the intermediate results of a model training job when possible. This attempt to save artifacts is only a best effort case as model might not be in a state from which it can be saved. For example, if training has just started, the model might not be ready to save. When saved, this intermediate data is a valid model artifact. You can use it to create a model with CreateModel.

The Neural Topic Model (NTM) currently does not support saving intermediate model artifacts. When training NTMs, make sure that the maximum runtime is sufficient for the training job to complete.

", "refs": { - "CreateCompilationJobRequest$StoppingCondition": "

The duration allowed for model compilation.

", - "CreateTrainingJobRequest$StoppingCondition": "

Sets a duration for training. Use this parameter to cap model training costs. To stop a job, Amazon SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds. Algorithms might use this 120-second window to save the model artifacts.

When Amazon SageMaker terminates a job because the stopping condition has been met, training algorithms provided by Amazon SageMaker save the intermediate results of the job. This intermediate data is a valid model artifact. You can use it to create a model using the CreateModel API.

", - "DescribeCompilationJobResponse$StoppingCondition": "

The duration allowed for model compilation.

", - "DescribeTrainingJobResponse$StoppingCondition": "

The condition under which to stop the training job.

", - "HyperParameterTrainingJobDefinition$StoppingCondition": "

Sets a maximum duration for the training jobs that the tuning job launches. Use this parameter to limit model training costs.

To stop a job, Amazon SageMaker sends the algorithm the SIGTERM signal. This delays job termination for 120 seconds. Algorithms might use this 120-second window to save the model artifacts.

When Amazon SageMaker terminates a job because the stopping condition has been met, training algorithms provided by Amazon SageMaker save the intermediate results of the job.

", - "TrainingJob$StoppingCondition": "

The condition under which to stop the training job.

", - "TrainingJobDefinition$StoppingCondition": "

Sets a duration for training. Use this parameter to cap model training costs.

To stop a job, Amazon SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds. Algorithms might use this 120-second window to save the model artifacts.

" + "CreateCompilationJobRequest$StoppingCondition": "

Specifies a limit to how long a model compilation job can run. When the job reaches the time limit, Amazon SageMaker ends the compilation job. Use this API to cap model training costs.

", + "CreateTrainingJobRequest$StoppingCondition": "

Specifies a limit to how long a model training job can run. When the job reaches the time limit, Amazon SageMaker ends the training job. Use this API to cap model training costs.

To stop a job, Amazon SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds. Algorithms can use this 120-second window to save the model artifacts, so the results of training are not lost.

", + "DescribeCompilationJobResponse$StoppingCondition": "

Specifies a limit to how long a model compilation job can run. When the job reaches the time limit, Amazon SageMaker ends the compilation job. Use this API to cap model training costs.

", + "DescribeTrainingJobResponse$StoppingCondition": "

Specifies a limit to how long a model training job can run. When the job reaches the time limit, Amazon SageMaker ends the training job. Use this API to cap model training costs.

To stop a job, Amazon SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds. Algorithms can use this 120-second window to save the model artifacts, so the results of training are not lost.

", + "HyperParameterTrainingJobDefinition$StoppingCondition": "

Specifies a limit to how long a model hyperparameter training job can run. When the job reaches the time limit, Amazon SageMaker ends the training job. Use this API to cap model training costs.

", + "TrainingJob$StoppingCondition": "

Specifies a limit to how long a model training job can run. When the job reaches the time limit, Amazon SageMaker ends the training job. Use this API to cap model training costs.

To stop a job, Amazon SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds. Algorithms can use this 120-second window to save the model artifacts, so the results of training are not lost.

", + "TrainingJobDefinition$StoppingCondition": "

Specifies a limit to how long a model training job can run. When the job reaches the time limit, Amazon SageMaker ends the training job. Use this API to cap model training costs.

To stop a job, Amazon SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds. Algorithms can use this 120-second window to save the model artifacts.

" } }, "String": { @@ -3420,14 +3440,14 @@ "TransformInstanceCount": { "base": null, "refs": { - "TransformResources$InstanceCount": "

The number of ML compute instances to use in the transform job. For distributed transform, provide a value greater than 1. The default value is 1.

" + "TransformResources$InstanceCount": "

The number of ML compute instances to use in the transform job. For distributed transform jobs, specify a value greater than 1. The default value is 1.

" } }, "TransformInstanceType": { "base": null, "refs": { "TransformInstanceTypes$member": null, - "TransformResources$InstanceType": "

The ML compute instance type for the transform job. For using built-in algorithms to transform moderately sized datasets, ml.m4.xlarge or ml.m5.large should suffice. There is no default value for InstanceType.

" + "TransformResources$InstanceType": "

The ML compute instance type for the transform job. If you are using built-in algorithms to transform moderately sized datasets, we recommend using ml.m4.xlarge or ml.m5.largeinstance types.

" } }, "TransformInstanceTypes": { diff --git a/src/data/sagemaker/2017-07-24/docs-2.json.php b/src/data/sagemaker/2017-07-24/docs-2.json.php index 7a8cef3dfa..ce66e73a1e 100644 --- a/src/data/sagemaker/2017-07-24/docs-2.json.php +++ b/src/data/sagemaker/2017-07-24/docs-2.json.php @@ -1,3 +1,3 @@ '2.0', 'service' => '

Provides APIs for creating and managing Amazon SageMaker resources.

', 'operations' => [ 'AddTags' => '

Adds or overwrites one or more tags for the specified Amazon SageMaker resource. You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints.

Each tag consists of a key and an optional value. Tag keys must be unique per resource. For more information about tags, see For more information, see AWS Tagging Strategies.

Tags that you add to a hyperparameter tuning job by calling this API are also added to any training jobs that the hyperparameter tuning job launches after you call this API, but not to training jobs that the hyperparameter tuning job launched before you called this API. To make sure that the tags associated with a hyperparameter tuning job are also added to all training jobs that the hyperparameter tuning job launches, add the tags when you first create the tuning job by specifying them in the Tags parameter of CreateHyperParameterTuningJob

', 'CreateAlgorithm' => '

Create a machine learning algorithm that you can use in Amazon SageMaker and list in the AWS Marketplace.

', 'CreateCodeRepository' => '

Creates a Git repository as a resource in your Amazon SageMaker account. You can associate the repository with notebook instances so that you can use Git source control for the notebooks you create. The Git repository is a resource in your Amazon SageMaker account, so it can be associated with more than one notebook instance, and it persists independently from the lifecycle of any notebook instances it is associated with.

The repository can be hosted either in AWS CodeCommit or in any other Git repository.

', 'CreateCompilationJob' => '

Starts a model compilation job. After the model has been compiled, Amazon SageMaker saves the resulting model artifacts to an Amazon Simple Storage Service (Amazon S3) bucket that you specify.

If you choose to host your model using Amazon SageMaker hosting services, you can use the resulting model artifacts as part of the model. You can also use the artifacts with AWS IoT Greengrass. In that case, deploy them as an ML resource.

In the request body, you provide the following:

You can also provide a Tag to track the model compilation job\'s resource use and costs. The response body contains the CompilationJobArn for the compiled job.

To stop a model compilation job, use StopCompilationJob. To get information about a particular model compilation job, use DescribeCompilationJob. To get information about multiple model compilation jobs, use ListCompilationJobs.

', 'CreateEndpoint' => '

Creates an endpoint using the endpoint configuration specified in the request. Amazon SageMaker uses the endpoint to provision resources and deploy models. You create the endpoint configuration with the CreateEndpointConfig API.

Use this API only for hosting models using Amazon SageMaker hosting services.

You must not delete an EndpointConfig in use by an endpoint that is live or while the UpdateEndpoint or CreateEndpoint operations are being performed on the endpoint. To update an endpoint, you must create a new EndpointConfig.

The endpoint name must be unique within an AWS Region in your AWS account.

When it receives the request, Amazon SageMaker creates the endpoint, launches the resources (ML compute instances), and deploys the model(s) on them.

When Amazon SageMaker receives the request, it sets the endpoint status to Creating. After it creates the endpoint, it sets the status to InService. Amazon SageMaker can then process incoming requests for inferences. To check the status of an endpoint, use the DescribeEndpoint API.

For an example, see Exercise 1: Using the K-Means Algorithm Provided by Amazon SageMaker.

If any of the models hosted at this endpoint get model data from an Amazon S3 location, Amazon SageMaker uses AWS Security Token Service to download model artifacts from the S3 path you provided. AWS STS is activated in your IAM user account by default. If you previously deactivated AWS STS for a region, you need to reactivate AWS STS for that region. For more information, see Activating and Deactivating AWS STS i an AWS Region in the AWS Identity and Access Management User Guide.

', 'CreateEndpointConfig' => '

Creates an endpoint configuration that Amazon SageMaker hosting services uses to deploy models. In the configuration, you identify one or more models, created using the CreateModel API, to deploy and the resources that you want Amazon SageMaker to provision. Then you call the CreateEndpoint API.

Use this API only if you want to use Amazon SageMaker hosting services to deploy models into production.

In the request, you define one or more ProductionVariants, each of which identifies a model. Each ProductionVariant parameter also describes the resources that you want Amazon SageMaker to provision. This includes the number and type of ML compute instances to deploy.

If you are hosting multiple models, you also assign a VariantWeight to specify how much traffic you want to allocate to each model. For example, suppose that you want to host two models, A and B, and you assign traffic weight 2 for model A and 1 for model B. Amazon SageMaker distributes two-thirds of the traffic to Model A, and one-third to model B.

', 'CreateHyperParameterTuningJob' => '

Starts a hyperparameter tuning job. A hyperparameter tuning job finds the best version of a model by running many training jobs on your dataset using the algorithm you choose and values for hyperparameters within ranges that you specify. It then chooses the hyperparameter values that result in a model that performs the best, as measured by an objective metric that you choose.

', 'CreateLabelingJob' => '

Creates a job that uses workers to label the data objects in your input dataset. You can use the labeled data to train machine learning models.

You can select your workforce from one of three providers:

You can also use automated data labeling to reduce the number of data objects that need to be labeled by a human. Automated data labeling uses active learning to determine if a data object can be labeled by machine or if it needs to be sent to a human worker. For more information, see Using Automated Data Labeling.

The data objects to be labeled are contained in an Amazon S3 bucket. You create a manifest file that describes the location of each object. For more information, see Using Input and Output Data.

The output can be used as the manifest file for another labeling job or as training data for your machine learning models.

', 'CreateModel' => '

Creates a model in Amazon SageMaker. In the request, you name the model and describe a primary container. For the primary container, you specify the docker image containing inference code, artifacts (from prior training), and custom environment map that the inference code uses when you deploy the model for predictions.

Use this API to create a model if you want to use Amazon SageMaker hosting services or run a batch transform job.

To host your model, you create an endpoint configuration with the CreateEndpointConfig API, and then create an endpoint with the CreateEndpoint API. Amazon SageMaker then deploys all of the containers that you defined for the model in the hosting environment.

To run a batch transform using your model, you start a job with the CreateTransformJob API. Amazon SageMaker uses your model and your dataset to get inferences which are then saved to a specified S3 location.

In the CreateModel request, you must define a container with the PrimaryContainer parameter.

In the request, you also provide an IAM role that Amazon SageMaker can assume to access model artifacts and docker image for deployment on ML compute hosting instances or for batch transform jobs. In addition, you also use the IAM role to manage permissions the inference code needs. For example, if the inference code access any other AWS resources, you grant necessary permissions via this role.

', 'CreateModelPackage' => '

Creates a model package that you can use to create Amazon SageMaker models or list on AWS Marketplace. Buyers can subscribe to model packages listed on AWS Marketplace to create models in Amazon SageMaker.

To create a model package by specifying a Docker container that contains your inference code and the Amazon S3 location of your model artifacts, provide values for InferenceSpecification. To create a model from an algorithm resource that you created or subscribed to in AWS Marketplace, provide a value for SourceAlgorithmSpecification.

', 'CreateNotebookInstance' => '

Creates an Amazon SageMaker notebook instance. A notebook instance is a machine learning (ML) compute instance running on a Jupyter notebook.

In a CreateNotebookInstance request, specify the type of ML compute instance that you want to run. Amazon SageMaker launches the instance, installs common libraries that you can use to explore datasets for model training, and attaches an ML storage volume to the notebook instance.

Amazon SageMaker also provides a set of example notebooks. Each notebook demonstrates how to use Amazon SageMaker with a specific algorithm or with a machine learning framework.

After receiving the request, Amazon SageMaker does the following:

  1. Creates a network interface in the Amazon SageMaker VPC.

  2. (Option) If you specified SubnetId, Amazon SageMaker creates a network interface in your own VPC, which is inferred from the subnet ID that you provide in the input. When creating this network interface, Amazon SageMaker attaches the security group that you specified in the request to the network interface that it creates in your VPC.

  3. Launches an EC2 instance of the type specified in the request in the Amazon SageMaker VPC. If you specified SubnetId of your VPC, Amazon SageMaker specifies both network interfaces when launching this instance. This enables inbound traffic from your own VPC to the notebook instance, assuming that the security groups allow it.

After creating the notebook instance, Amazon SageMaker returns its Amazon Resource Name (ARN).

After Amazon SageMaker creates the notebook instance, you can connect to the Jupyter server and work in Jupyter notebooks. For example, you can write code to explore a dataset that you can use for model training, train a model, host models by creating Amazon SageMaker endpoints, and validate hosted models.

For more information, see How It Works.

', 'CreateNotebookInstanceLifecycleConfig' => '

Creates a lifecycle configuration that you can associate with a notebook instance. A lifecycle configuration is a collection of shell scripts that run when you create or start a notebook instance.

Each lifecycle configuration script has a limit of 16384 characters.

The value of the $PATH environment variable that is available to both scripts is /sbin:bin:/usr/sbin:/usr/bin.

View CloudWatch Logs for notebook instance lifecycle configurations in log group /aws/sagemaker/NotebookInstances in log stream [notebook-instance-name]/[LifecycleConfigHook].

Lifecycle configuration scripts cannot run for longer than 5 minutes. If a script runs for longer than 5 minutes, it fails and the notebook instance is not created or started.

For information about notebook instance lifestyle configurations, see Step 2.1: (Optional) Customize a Notebook Instance.

', 'CreatePresignedNotebookInstanceUrl' => '

Returns a URL that you can use to connect to the Jupyter server from a notebook instance. In the Amazon SageMaker console, when you choose Open next to a notebook instance, Amazon SageMaker opens a new tab showing the Jupyter server home page from the notebook instance. The console uses this API to get the URL and show the page.

You can restrict access to this API and to the URL that it returns to a list of IP addresses that you specify. To restrict access, attach an IAM policy that denies access to this API unless the call comes from an IP address in the specified list to every AWS Identity and Access Management user, group, or role used to access the notebook instance. Use the NotIpAddress condition operator and the aws:SourceIP condition context key to specify the list of IP addresses that you want to have access to the notebook instance. For more information, see Limit Access to a Notebook Instance by IP Address.

The URL that you get from a call to is valid only for 5 minutes. If you try to use the URL after the 5-minute limit expires, you are directed to the AWS console sign-in page.

', 'CreateTrainingJob' => '

Starts a model training job. After training completes, Amazon SageMaker saves the resulting model artifacts to an Amazon S3 location that you specify.

If you choose to host your model using Amazon SageMaker hosting services, you can use the resulting model artifacts as part of the model. You can also use the artifacts in a machine learning service other than Amazon SageMaker, provided that you know how to use them for inferences.

In the request body, you provide the following:

For more information about Amazon SageMaker, see How It Works.

', 'CreateTransformJob' => '

Starts a transform job. A transform job uses a trained model to get inferences on a dataset and saves these results to an Amazon S3 location that you specify.

To perform batch transformations, you create a transform job and use the data that you have readily available.

In the request body, you provide the following:

For more information about how batch transformation works Amazon SageMaker, see How It Works.

', 'CreateWorkteam' => '

Creates a new work team for labeling your data. A work team is defined by one or more Amazon Cognito user pools. You must first create the user pools before you can create a work team.

You cannot create more than 25 work teams in an account and region.

', 'DeleteAlgorithm' => '

Removes the specified algorithm from your account.

', 'DeleteCodeRepository' => '

Deletes the specified Git repository from your account.

', 'DeleteEndpoint' => '

Deletes an endpoint. Amazon SageMaker frees up all of the resources that were deployed when the endpoint was created.

Amazon SageMaker retires any custom KMS key grants associated with the endpoint, meaning you don\'t need to use the RevokeGrant API call.

', 'DeleteEndpointConfig' => '

Deletes an endpoint configuration. The DeleteEndpointConfig API deletes only the specified configuration. It does not delete endpoints created using the configuration.

', 'DeleteModel' => '

Deletes a model. The DeleteModel API deletes only the model entry that was created in Amazon SageMaker when you called the CreateModel API. It does not delete model artifacts, inference code, or the IAM role that you specified when creating the model.

', 'DeleteModelPackage' => '

Deletes a model package.

A model package is used to create Amazon SageMaker models or list on AWS Marketplace. Buyers can subscribe to model packages listed on AWS Marketplace to create models in Amazon SageMaker.

', 'DeleteNotebookInstance' => '

Deletes an Amazon SageMaker notebook instance. Before you can delete a notebook instance, you must call the StopNotebookInstance API.

When you delete a notebook instance, you lose all of your data. Amazon SageMaker removes the ML compute instance, and deletes the ML storage volume and the network interface associated with the notebook instance.

', 'DeleteNotebookInstanceLifecycleConfig' => '

Deletes a notebook instance lifecycle configuration.

', 'DeleteTags' => '

Deletes the specified tags from an Amazon SageMaker resource.

To list a resource\'s tags, use the ListTags API.

When you call this API to delete tags from a hyperparameter tuning job, the deleted tags are not removed from training jobs that the hyperparameter tuning job launched before you called this API.

', 'DeleteWorkteam' => '

Deletes an existing work team. This operation can\'t be undone.

', 'DescribeAlgorithm' => '

Returns a description of the specified algorithm that is in your account.

', 'DescribeCodeRepository' => '

Gets details about the specified Git repository.

', 'DescribeCompilationJob' => '

Returns information about a model compilation job.

To create a model compilation job, use CreateCompilationJob. To get information about multiple model compilation jobs, use ListCompilationJobs.

', 'DescribeEndpoint' => '

Returns the description of an endpoint.

', 'DescribeEndpointConfig' => '

Returns the description of an endpoint configuration created using the CreateEndpointConfig API.

', 'DescribeHyperParameterTuningJob' => '

Gets a description of a hyperparameter tuning job.

', 'DescribeLabelingJob' => '

Gets information about a labeling job.

', 'DescribeModel' => '

Describes a model that you created using the CreateModel API.

', 'DescribeModelPackage' => '

Returns a description of the specified model package, which is used to create Amazon SageMaker models or list them on AWS Marketplace.

To create models in Amazon SageMaker, buyers can subscribe to model packages listed on AWS Marketplace.

', 'DescribeNotebookInstance' => '

Returns information about a notebook instance.

', 'DescribeNotebookInstanceLifecycleConfig' => '

Returns a description of a notebook instance lifecycle configuration.

For information about notebook instance lifestyle configurations, see Step 2.1: (Optional) Customize a Notebook Instance.

', 'DescribeSubscribedWorkteam' => '

Gets information about a work team provided by a vendor. It returns details about the subscription with a vendor in the AWS Marketplace.

', 'DescribeTrainingJob' => '

Returns information about a training job.

', 'DescribeTransformJob' => '

Returns information about a transform job.

', 'DescribeWorkteam' => '

Gets information about a specific work team. You can see information such as the create date, the last updated date, membership information, and the work team\'s Amazon Resource Name (ARN).

', 'GetSearchSuggestions' => '

An auto-complete API for the search functionality in the Amazon SageMaker console. It returns suggestions of possible matches for the property name to use in Search queries. Provides suggestions for HyperParameters, Tags, and Metrics.

', 'ListAlgorithms' => '

Lists the machine learning algorithms that have been created.

', 'ListCodeRepositories' => '

Gets a list of the Git repositories in your account.

', 'ListCompilationJobs' => '

Lists model compilation jobs that satisfy various filters.

To create a model compilation job, use CreateCompilationJob. To get information about a particular model compilation job you have created, use DescribeCompilationJob.

', 'ListEndpointConfigs' => '

Lists endpoint configurations.

', 'ListEndpoints' => '

Lists endpoints.

', 'ListHyperParameterTuningJobs' => '

Gets a list of HyperParameterTuningJobSummary objects that describe the hyperparameter tuning jobs launched in your account.

', 'ListLabelingJobs' => '

Gets a list of labeling jobs.

', 'ListLabelingJobsForWorkteam' => '

Gets a list of labeling jobs assigned to a specified work team.

', 'ListModelPackages' => '

Lists the model packages that have been created.

', 'ListModels' => '

Lists models created with the CreateModel API.

', 'ListNotebookInstanceLifecycleConfigs' => '

Lists notebook instance lifestyle configurations created with the CreateNotebookInstanceLifecycleConfig API.

', 'ListNotebookInstances' => '

Returns a list of the Amazon SageMaker notebook instances in the requester\'s account in an AWS Region.

', 'ListSubscribedWorkteams' => '

Gets a list of the work teams that you are subscribed to in the AWS Marketplace. The list may be empty if no work team satisfies the filter specified in the NameContains parameter.

', 'ListTags' => '

Returns the tags for the specified Amazon SageMaker resource.

', 'ListTrainingJobs' => '

Lists training jobs.

', 'ListTrainingJobsForHyperParameterTuningJob' => '

Gets a list of TrainingJobSummary objects that describe the training jobs that a hyperparameter tuning job launched.

', 'ListTransformJobs' => '

Lists transform jobs.

', 'ListWorkteams' => '

Gets a list of work teams that you have defined in a region. The list may be empty if no work team satisfies the filter specified in the NameContains parameter.

', 'RenderUiTemplate' => '

Renders the UI template so that you can preview the worker\'s experience.

', 'Search' => '

Finds Amazon SageMaker resources that match a search query. Matching resource objects are returned as a list of SearchResult objects in the response. You can sort the search results by any resource property in a ascending or descending order.

You can query against the following value types: numerical, text, Booleans, and timestamps.

', 'StartNotebookInstance' => '

Launches an ML compute instance with the latest version of the libraries and attaches your ML storage volume. After configuring the notebook instance, Amazon SageMaker sets the notebook instance status to InService. A notebook instance\'s status must be InService before you can connect to your Jupyter notebook.

', 'StopCompilationJob' => '

Stops a model compilation job.

To stop a job, Amazon SageMaker sends the algorithm the SIGTERM signal. This gracefully shuts the job down. If the job hasn\'t stopped, it sends the SIGKILL signal.

When it receives a StopCompilationJob request, Amazon SageMaker changes the CompilationJobSummary$CompilationJobStatus of the job to Stopping. After Amazon SageMaker stops the job, it sets the CompilationJobSummary$CompilationJobStatus to Stopped.

', 'StopHyperParameterTuningJob' => '

Stops a running hyperparameter tuning job and all running training jobs that the tuning job launched.

All model artifacts output from the training jobs are stored in Amazon Simple Storage Service (Amazon S3). All data that the training jobs write to Amazon CloudWatch Logs are still available in CloudWatch. After the tuning job moves to the Stopped state, it releases all reserved resources for the tuning job.

', 'StopLabelingJob' => '

Stops a running labeling job. A job that is stopped cannot be restarted. Any results obtained before the job is stopped are placed in the Amazon S3 output bucket.

', 'StopNotebookInstance' => '

Terminates the ML compute instance. Before terminating the instance, Amazon SageMaker disconnects the ML storage volume from it. Amazon SageMaker preserves the ML storage volume. Amazon SageMaker stops charging you for the ML compute instance when you call StopNotebookInstance.

To access data on the ML storage volume for a notebook instance that has been terminated, call the StartNotebookInstance API. StartNotebookInstance launches another ML compute instance, configures it, and attaches the preserved ML storage volume so you can continue your work.

', 'StopTrainingJob' => '

Stops a training job. To stop a job, Amazon SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds. Algorithms might use this 120-second window to save the model artifacts, so the results of the training is not lost.

When it receives a StopTrainingJob request, Amazon SageMaker changes the status of the job to Stopping. After Amazon SageMaker stops the job, it sets the status to Stopped.

', 'StopTransformJob' => '

Stops a transform job.

When Amazon SageMaker receives a StopTransformJob request, the status of the job changes to Stopping. After Amazon SageMaker stops the job, the status is set to Stopped. When you stop a transform job before it is completed, Amazon SageMaker doesn\'t store the job\'s output in Amazon S3.

', 'UpdateCodeRepository' => '

Updates the specified Git repository with the specified values.

', 'UpdateEndpoint' => '

Deploys the new EndpointConfig specified in the request, switches to using newly created endpoint, and then deletes resources provisioned for the endpoint using the previous EndpointConfig (there is no availability loss).

When Amazon SageMaker receives the request, it sets the endpoint status to Updating. After updating the endpoint, it sets the status to InService. To check the status of an endpoint, use the DescribeEndpoint API.

You must not delete an EndpointConfig in use by an endpoint that is live or while the UpdateEndpoint or CreateEndpoint operations are being performed on the endpoint. To update an endpoint, you must create a new EndpointConfig.

', 'UpdateEndpointWeightsAndCapacities' => '

Updates variant weight of one or more variants associated with an existing endpoint, or capacity of one variant associated with an existing endpoint. When it receives the request, Amazon SageMaker sets the endpoint status to Updating. After updating the endpoint, it sets the status to InService. To check the status of an endpoint, use the DescribeEndpoint API.

', 'UpdateNotebookInstance' => '

Updates a notebook instance. NotebookInstance updates include upgrading or downgrading the ML compute instance used for your notebook instance to accommodate changes in your workload requirements.

', 'UpdateNotebookInstanceLifecycleConfig' => '

Updates a notebook instance lifecycle configuration created with the CreateNotebookInstanceLifecycleConfig API.

', 'UpdateWorkteam' => '

Updates an existing work team with new member definitions or description.

', ], 'shapes' => [ 'Accept' => [ 'base' => NULL, 'refs' => [ 'TransformOutput$Accept' => '

The MIME type used to specify the output data. Amazon SageMaker uses the MIME type with each http call to transfer data from the transform job.

', ], ], 'AccountId' => [ 'base' => NULL, 'refs' => [ 'LabelingJobForWorkteamSummary$WorkRequesterAccountId' => '

', ], ], 'AddTagsInput' => [ 'base' => NULL, 'refs' => [], ], 'AddTagsOutput' => [ 'base' => NULL, 'refs' => [], ], 'AdditionalCodeRepositoryNamesOrUrls' => [ 'base' => NULL, 'refs' => [ 'CreateNotebookInstanceInput$AdditionalCodeRepositories' => '

An array of up to three Git repositories to associate with the notebook instance. These can be either the names of Git repositories stored as resources in your account, or the URL of Git repositories in AWS CodeCommit or in any other Git repository. These repositories are cloned at the same level as the default repository of your notebook instance. For more information, see Associating Git Repositories with Amazon SageMaker Notebook Instances.

', 'DescribeNotebookInstanceOutput$AdditionalCodeRepositories' => '

An array of up to three Git repositories associated with the notebook instance. These can be either the names of Git repositories stored as resources in your account, or the URL of Git repositories in AWS CodeCommit or in any other Git repository. These repositories are cloned at the same level as the default repository of your notebook instance. For more information, see Associating Git Repositories with Amazon SageMaker Notebook Instances.

', 'NotebookInstanceSummary$AdditionalCodeRepositories' => '

An array of up to three Git repositories associated with the notebook instance. These can be either the names of Git repositories stored as resources in your account, or the URL of Git repositories in AWS CodeCommit or in any other Git repository. These repositories are cloned at the same level as the default repository of your notebook instance. For more information, see Associating Git Repositories with Amazon SageMaker Notebook Instances.

', 'UpdateNotebookInstanceInput$AdditionalCodeRepositories' => '

An array of up to three Git repositories to associate with the notebook instance. These can be either the names of Git repositories stored as resources in your account, or the URL of Git repositories in AWS CodeCommit or in any other Git repository. These repositories are cloned at the same level as the default repository of your notebook instance. For more information, see Associating Git Repositories with Amazon SageMaker Notebook Instances.

', ], ], 'AlgorithmArn' => [ 'base' => NULL, 'refs' => [ 'AlgorithmSummary$AlgorithmArn' => '

The Amazon Resource Name (ARN) of the algorithm.

', 'CreateAlgorithmOutput$AlgorithmArn' => '

The Amazon Resource Name (ARN) of the new algorithm.

', 'DescribeAlgorithmOutput$AlgorithmArn' => '

The Amazon Resource Name (ARN) of the algorithm.

', ], ], 'AlgorithmImage' => [ 'base' => NULL, 'refs' => [ 'AlgorithmSpecification$TrainingImage' => '

The registry path of the Docker image that contains the training algorithm. For information about docker registry paths for built-in algorithms, see Algorithms Provided by Amazon SageMaker: Common Parameters. Amazon SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker.

', 'HyperParameterAlgorithmSpecification$TrainingImage' => '

The registry path of the Docker image that contains the training algorithm. For information about Docker registry paths for built-in algorithms, see Algorithms Provided by Amazon SageMaker: Common Parameters. Amazon SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker.

', ], ], 'AlgorithmSortBy' => [ 'base' => NULL, 'refs' => [ 'ListAlgorithmsInput$SortBy' => '

The parameter by which to sort the results. The default is CreationTime.

', ], ], 'AlgorithmSpecification' => [ 'base' => '

Specifies the training algorithm to use in a CreateTrainingJob request.

For more information about algorithms provided by Amazon SageMaker, see Algorithms. For information about using your own algorithms, see Using Your Own Algorithms with Amazon SageMaker.

', 'refs' => [ 'CreateTrainingJobRequest$AlgorithmSpecification' => '

The registry path of the Docker image that contains the training algorithm and algorithm-specific metadata, including the input mode. For more information about algorithms provided by Amazon SageMaker, see Algorithms. For information about providing your own algorithms, see Using Your Own Algorithms with Amazon SageMaker.

', 'DescribeTrainingJobResponse$AlgorithmSpecification' => '

Information about the algorithm used for training, and algorithm metadata.

', 'TrainingJob$AlgorithmSpecification' => '

Information about the algorithm used for training, and algorithm metadata.

', ], ], 'AlgorithmStatus' => [ 'base' => NULL, 'refs' => [ 'AlgorithmSummary$AlgorithmStatus' => '

The overall status of the algorithm.

', 'DescribeAlgorithmOutput$AlgorithmStatus' => '

The current status of the algorithm.

', ], ], 'AlgorithmStatusDetails' => [ 'base' => '

Specifies the validation and image scan statuses of the algorithm.

', 'refs' => [ 'DescribeAlgorithmOutput$AlgorithmStatusDetails' => '

Details about the current status of the algorithm.

', ], ], 'AlgorithmStatusItem' => [ 'base' => '

Represents the overall status of an algorithm.

', 'refs' => [ 'AlgorithmStatusItemList$member' => NULL, ], ], 'AlgorithmStatusItemList' => [ 'base' => NULL, 'refs' => [ 'AlgorithmStatusDetails$ValidationStatuses' => '

The status of algorithm validation.

', 'AlgorithmStatusDetails$ImageScanStatuses' => '

The status of the scan of the algorithm\'s Docker image container.

', ], ], 'AlgorithmSummary' => [ 'base' => '

Provides summary information about an algorithm.

', 'refs' => [ 'AlgorithmSummaryList$member' => NULL, ], ], 'AlgorithmSummaryList' => [ 'base' => NULL, 'refs' => [ 'ListAlgorithmsOutput$AlgorithmSummaryList' => '

>An array of AlgorithmSummary objects, each of which lists an algorithm.

', ], ], 'AlgorithmValidationProfile' => [ 'base' => '

Defines a training job and a batch transform job that Amazon SageMaker runs to validate your algorithm.

The data provided in the validation profile is made available to your buyers on AWS Marketplace.

', 'refs' => [ 'AlgorithmValidationProfiles$member' => NULL, ], ], 'AlgorithmValidationProfiles' => [ 'base' => NULL, 'refs' => [ 'AlgorithmValidationSpecification$ValidationProfiles' => '

An array of AlgorithmValidationProfile objects, each of which specifies a training job and batch transform job that Amazon SageMaker runs to validate your algorithm.

', ], ], 'AlgorithmValidationSpecification' => [ 'base' => '

Specifies configurations for one or more training jobs that Amazon SageMaker runs to test the algorithm.

', 'refs' => [ 'CreateAlgorithmInput$ValidationSpecification' => '

Specifies configurations for one or more training jobs and that Amazon SageMaker runs to test the algorithm\'s training code and, optionally, one or more batch transform jobs that Amazon SageMaker runs to test the algorithm\'s inference code.

', 'DescribeAlgorithmOutput$ValidationSpecification' => '

Details about configurations for one or more training jobs that Amazon SageMaker runs to test the algorithm.

', ], ], 'AnnotationConsolidationConfig' => [ 'base' => '

Configures how labels are consolidated across human workers.

', 'refs' => [ 'HumanTaskConfig$AnnotationConsolidationConfig' => '

Configures how labels are consolidated across human workers.

', ], ], 'ArnOrName' => [ 'base' => NULL, 'refs' => [ 'AlgorithmSpecification$AlgorithmName' => '

The name of the algorithm resource to use for the training job. This must be an algorithm resource that you created or subscribe to on AWS Marketplace. If you specify a value for this parameter, you can\'t specify a value for TrainingImage.

', 'ContainerDefinition$ModelPackageName' => '

The name of the model package to use to create the model.

', 'DescribeAlgorithmInput$AlgorithmName' => '

The name of the algorithm to describe.

', 'DescribeModelPackageInput$ModelPackageName' => '

The name of the model package to describe.

', 'HyperParameterAlgorithmSpecification$AlgorithmName' => '

The name of the resource algorithm to use for the hyperparameter tuning job. If you specify a value for this parameter, do not specify a value for TrainingImage.

', 'SourceAlgorithm$AlgorithmName' => '

The name of an algorithm that was used to create the model package. The algorithm must be either an algorithm resource in your Amazon SageMaker account or an algorithm in AWS Marketplace that you are subscribed to.

', ], ], 'AssemblyType' => [ 'base' => NULL, 'refs' => [ 'TransformOutput$AssembleWith' => '

Defines how to assemble the results of the transform job as a single S3 object. Choose a format that is most convenient to you. To concatenate the results in binary format, specify None. To add a newline character at the end of every transformed record, specify Line.

', ], ], 'AttributeName' => [ 'base' => NULL, 'refs' => [ 'AttributeNames$member' => NULL, ], ], 'AttributeNames' => [ 'base' => NULL, 'refs' => [ 'S3DataSource$AttributeNames' => '

A list of one or more attribute names to use that are found in a specified augmented manifest file.

', ], ], 'BatchStrategy' => [ 'base' => NULL, 'refs' => [ 'CreateTransformJobRequest$BatchStrategy' => '

Specifies the number of records to include in a mini-batch for an HTTP inference request. A record is a single unit of input data that inference can be made on. For example, a single line in a CSV file is a record.

To enable the batch strategy, you must set SplitType to Line, RecordIO, or TFRecord.

To use only one record when making an HTTP invocation request to a container, set BatchStrategy to SingleRecord and SplitType to Line.

To fit as many records in a mini-batch as can fit within the MaxPayloadInMB limit, set BatchStrategy to MultiRecord and SplitType to Line.

', 'DescribeTransformJobResponse$BatchStrategy' => '

Specifies the number of records to include in a mini-batch for an HTTP inference request. A record is a single unit of input data that inference can be made on. For example, a single line in a CSV file is a record.

To enable the batch strategy, you must set SplitType to Line, RecordIO, or TFRecord.

', 'TransformJobDefinition$BatchStrategy' => '

A string that determines the number of records included in a single mini-batch.

SingleRecord means only one record is used per mini-batch. MultiRecord means a mini-batch is set to contain as many records that can fit within the MaxPayloadInMB limit.

', ], ], 'Boolean' => [ 'base' => NULL, 'refs' => [ 'ChannelSpecification$IsRequired' => '

Indicates whether the channel is required by the algorithm.

', 'CreateModelInput$EnableNetworkIsolation' => '

Isolates the model container. No inbound or outbound network calls can be made to or from the model container.

The Semantic Segmentation built-in algorithm does not support network isolation.

', 'CreateTrainingJobRequest$EnableNetworkIsolation' => '

Isolates the training container. No inbound or outbound network calls can be made, except for calls between peers within a training cluster for distributed training. If you enable network isolation for training jobs that are configured to use a VPC, Amazon SageMaker downloads and uploads customer data and model artifacts through the specified VPC, but the training container does not have network access.

The Semantic Segmentation built-in algorithm does not support network isolation.

', 'CreateTrainingJobRequest$EnableInterContainerTrafficEncryption' => '

To encrypt all communications between ML compute instances in distributed training, choose True. Encryption provides greater security for distributed training, but training might take longer. How long it takes depends on the amount of communication between compute instances, especially if you use a deep learning algorithm in distributed training. For more information, see Protect Communications Between ML Compute Instances in a Distributed Training Job.

', 'DescribeModelOutput$EnableNetworkIsolation' => '

If True, no inbound or outbound network calls can be made to or from the model container.

The Semantic Segmentation built-in algorithm does not support network isolation.

', 'DescribeTrainingJobResponse$EnableNetworkIsolation' => '

If you want to allow inbound or outbound network calls, except for calls between peers within a training cluster for distributed training, choose True. If you enable network isolation for training jobs that are configured to use a VPC, Amazon SageMaker downloads and uploads customer data and model artifacts through the specified VPC, but the training container does not have network access.

The Semantic Segmentation built-in algorithm does not support network isolation.

', 'DescribeTrainingJobResponse$EnableInterContainerTrafficEncryption' => '

To encrypt all communications between ML compute instances in distributed training, choose True. Encryption provides greater security for distributed training, but training might take longer. How long it takes depends on the amount of communication between compute instances, especially if you use a deep learning algorithm in distributed training.

', 'HyperParameterSpecification$IsTunable' => '

Indicates whether this hyperparameter is tunable in a hyperparameter tuning job.

', 'HyperParameterSpecification$IsRequired' => '

Indicates whether this hyperparameter is required.

', 'HyperParameterTrainingJobDefinition$EnableNetworkIsolation' => '

Isolates the training container. No inbound or outbound network calls can be made, except for calls between peers within a training cluster for distributed training. If network isolation is used for training jobs that are configured to use a VPC, Amazon SageMaker downloads and uploads customer data and model artifacts through the specified VPC, but the training container does not have network access.

The Semantic Segmentation built-in algorithm does not support network isolation.

', 'HyperParameterTrainingJobDefinition$EnableInterContainerTrafficEncryption' => '

To encrypt all communications between ML compute instances in distributed training, choose True. Encryption provides greater security for distributed training, but training might take longer. How long it takes depends on the amount of communication between compute instances, especially if you use a deep learning algorithm in distributed training.

', 'TrainingJob$EnableNetworkIsolation' => '

If the TrainingJob was created with network isolation, the value is set to true. If network isolation is enabled, nodes can\'t communicate beyond the VPC they run in.

', 'TrainingJob$EnableInterContainerTrafficEncryption' => '

To encrypt all communications between ML compute instances in distributed training, choose True. Encryption provides greater security for distributed training, but training might take longer. How long it takes depends on the amount of communication between compute instances, especially if you use a deep learning algorithm in distributed training.

', 'TrainingSpecification$SupportsDistributedTraining' => '

Indicates whether the algorithm supports distributed training. If set to false, buyers can’t request more than one instance during training.

', ], ], 'BooleanOperator' => [ 'base' => NULL, 'refs' => [ 'SearchExpression$Operator' => '

A Boolean operator used to evaluate the search expression. If you want every conditional statement in all lists to be satisfied for the entire search expression to be true, specify And. If only a single conditional statement needs to be true for the entire search expression to be true, specify Or. The default value is And.

', ], ], 'Branch' => [ 'base' => NULL, 'refs' => [ 'GitConfig$Branch' => '

The default branch for the Git repository.

', ], ], 'CategoricalParameterRange' => [ 'base' => '

A list of categorical hyperparameters to tune.

', 'refs' => [ 'CategoricalParameterRanges$member' => NULL, ], ], 'CategoricalParameterRangeSpecification' => [ 'base' => '

Defines the possible values for a categorical hyperparameter.

', 'refs' => [ 'ParameterRange$CategoricalParameterRangeSpecification' => '

A CategoricalParameterRangeSpecification object that defines the possible values for a categorical hyperparameter.

', ], ], 'CategoricalParameterRanges' => [ 'base' => NULL, 'refs' => [ 'ParameterRanges$CategoricalParameterRanges' => '

The array of CategoricalParameterRange objects that specify ranges of categorical hyperparameters that a hyperparameter tuning job searches.

', ], ], 'Cents' => [ 'base' => NULL, 'refs' => [ 'USD$Cents' => '

The fractional portion, in cents, of the amount.

', ], ], 'CertifyForMarketplace' => [ 'base' => NULL, 'refs' => [ 'CreateAlgorithmInput$CertifyForMarketplace' => '

Whether to certify the algorithm so that it can be listed in AWS Marketplace.

', 'CreateModelPackageInput$CertifyForMarketplace' => '

Whether to certify the model package for listing on AWS Marketplace.

', 'DescribeAlgorithmOutput$CertifyForMarketplace' => '

Whether the algorithm is certified to be listed in AWS Marketplace.

', 'DescribeModelPackageOutput$CertifyForMarketplace' => '

Whether the model package is certified for listing on AWS Marketplace.

', ], ], 'Channel' => [ 'base' => '

A channel is a named input source that training algorithms can consume.

', 'refs' => [ 'InputDataConfig$member' => NULL, ], ], 'ChannelName' => [ 'base' => NULL, 'refs' => [ 'Channel$ChannelName' => '

The name of the channel.

', 'ChannelSpecification$Name' => '

The name of the channel.

', ], ], 'ChannelSpecification' => [ 'base' => '

Defines a named input source, called a channel, to be used by an algorithm.

', 'refs' => [ 'ChannelSpecifications$member' => NULL, ], ], 'ChannelSpecifications' => [ 'base' => NULL, 'refs' => [ 'TrainingSpecification$TrainingChannels' => '

A list of ChannelSpecification objects, which specify the input sources to be used by the algorithm.

', ], ], 'CodeRepositoryArn' => [ 'base' => NULL, 'refs' => [ 'CodeRepositorySummary$CodeRepositoryArn' => '

The Amazon Resource Name (ARN) of the Git repository.

', 'CreateCodeRepositoryOutput$CodeRepositoryArn' => '

The Amazon Resource Name (ARN) of the new repository.

', 'DescribeCodeRepositoryOutput$CodeRepositoryArn' => '

The Amazon Resource Name (ARN) of the Git repository.

', 'UpdateCodeRepositoryOutput$CodeRepositoryArn' => '

The ARN of the Git repository.

', ], ], 'CodeRepositoryContains' => [ 'base' => NULL, 'refs' => [ 'ListNotebookInstancesInput$DefaultCodeRepositoryContains' => '

A string in the name or URL of a Git repository associated with this notebook instance. This filter returns only notebook instances associated with a git repository with a name that contains the specified string.

', ], ], 'CodeRepositoryNameContains' => [ 'base' => NULL, 'refs' => [ 'ListCodeRepositoriesInput$NameContains' => '

A string in the Git repositories name. This filter returns only repositories whose name contains the specified string.

', ], ], 'CodeRepositoryNameOrUrl' => [ 'base' => NULL, 'refs' => [ 'AdditionalCodeRepositoryNamesOrUrls$member' => NULL, 'CreateNotebookInstanceInput$DefaultCodeRepository' => '

A Git repository to associate with the notebook instance as its default code repository. This can be either the name of a Git repository stored as a resource in your account, or the URL of a Git repository in AWS CodeCommit or in any other Git repository. When you open a notebook instance, it opens in the directory that contains this repository. For more information, see Associating Git Repositories with Amazon SageMaker Notebook Instances.

', 'DescribeNotebookInstanceOutput$DefaultCodeRepository' => '

The Git repository associated with the notebook instance as its default code repository. This can be either the name of a Git repository stored as a resource in your account, or the URL of a Git repository in AWS CodeCommit or in any other Git repository. When you open a notebook instance, it opens in the directory that contains this repository. For more information, see Associating Git Repositories with Amazon SageMaker Notebook Instances.

', 'ListNotebookInstancesInput$AdditionalCodeRepositoryEquals' => '

A filter that returns only notebook instances with associated with the specified git repository.

', 'NotebookInstanceSummary$DefaultCodeRepository' => '

The Git repository associated with the notebook instance as its default code repository. This can be either the name of a Git repository stored as a resource in your account, or the URL of a Git repository in AWS CodeCommit or in any other Git repository. When you open a notebook instance, it opens in the directory that contains this repository. For more information, see Associating Git Repositories with Amazon SageMaker Notebook Instances.

', 'UpdateNotebookInstanceInput$DefaultCodeRepository' => '

The Git repository to associate with the notebook instance as its default code repository. This can be either the name of a Git repository stored as a resource in your account, or the URL of a Git repository in AWS CodeCommit or in any other Git repository. When you open a notebook instance, it opens in the directory that contains this repository. For more information, see Associating Git Repositories with Amazon SageMaker Notebook Instances.

', ], ], 'CodeRepositorySortBy' => [ 'base' => NULL, 'refs' => [ 'ListCodeRepositoriesInput$SortBy' => '

The field to sort results by. The default is Name.

', ], ], 'CodeRepositorySortOrder' => [ 'base' => NULL, 'refs' => [ 'ListCodeRepositoriesInput$SortOrder' => '

The sort order for results. The default is Ascending.

', ], ], 'CodeRepositorySummary' => [ 'base' => '

Specifies summary information about a Git repository.

', 'refs' => [ 'CodeRepositorySummaryList$member' => NULL, ], ], 'CodeRepositorySummaryList' => [ 'base' => NULL, 'refs' => [ 'ListCodeRepositoriesOutput$CodeRepositorySummaryList' => '

Gets a list of summaries of the Git repositories. Each summary specifies the following values for the repository:

  • Name

  • Amazon Resource Name (ARN)

  • Creation time

  • Last modified time

  • Configuration information, including the URL location of the repository and the ARN of the AWS Secrets Manager secret that contains the credentials used to access the repository.

', ], ], 'CognitoClientId' => [ 'base' => NULL, 'refs' => [ 'CognitoMemberDefinition$ClientId' => '

An identifier for an application client. You must create the app client ID using Amazon Cognito.

', ], ], 'CognitoMemberDefinition' => [ 'base' => '

Identifies a Amazon Cognito user group. A user group can be used in on or more work teams.

', 'refs' => [ 'MemberDefinition$CognitoMemberDefinition' => '

The Amazon Cognito user group that is part of the work team.

', ], ], 'CognitoUserGroup' => [ 'base' => NULL, 'refs' => [ 'CognitoMemberDefinition$UserGroup' => '

An identifier for a user group.

', ], ], 'CognitoUserPool' => [ 'base' => NULL, 'refs' => [ 'CognitoMemberDefinition$UserPool' => '

An identifier for a user pool. The user pool must be in the same region as the service that you are calling.

', ], ], 'CompilationJobArn' => [ 'base' => NULL, 'refs' => [ 'CompilationJobSummary$CompilationJobArn' => '

The Amazon Resource Name (ARN) of the model compilation job.

', 'CreateCompilationJobResponse$CompilationJobArn' => '

If the action is successful, the service sends back an HTTP 200 response. Amazon SageMaker returns the following data in JSON format:

  • CompilationJobArn: The Amazon Resource Name (ARN) of the compiled job.

', 'DescribeCompilationJobResponse$CompilationJobArn' => '

The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker assumes to perform the model compilation job.

', ], ], 'CompilationJobStatus' => [ 'base' => NULL, 'refs' => [ 'CompilationJobSummary$CompilationJobStatus' => '

The status of the model compilation job.

', 'DescribeCompilationJobResponse$CompilationJobStatus' => '

The status of the model compilation job.

', 'ListCompilationJobsRequest$StatusEquals' => '

A filter that retrieves model compilation jobs with a specific DescribeCompilationJobResponse$CompilationJobStatus status.

', ], ], 'CompilationJobSummaries' => [ 'base' => NULL, 'refs' => [ 'ListCompilationJobsResponse$CompilationJobSummaries' => '

An array of CompilationJobSummary objects, each describing a model compilation job.

', ], ], 'CompilationJobSummary' => [ 'base' => '

A summary of a model compilation job.

', 'refs' => [ 'CompilationJobSummaries$member' => NULL, ], ], 'CompressionType' => [ 'base' => NULL, 'refs' => [ 'Channel$CompressionType' => '

If training data is compressed, the compression type. The default value is None. CompressionType is used only in Pipe input mode. In File mode, leave this field unset or set it to None.

', 'CompressionTypes$member' => NULL, 'TransformInput$CompressionType' => '

If your transform data is compressed, specify the compression type. Amazon SageMaker automatically decompresses the data for the transform job accordingly. The default value is None.

', ], ], 'CompressionTypes' => [ 'base' => NULL, 'refs' => [ 'ChannelSpecification$SupportedCompressionTypes' => '

The allowed compression types, if data compression is used.

', ], ], 'ContainerDefinition' => [ 'base' => '

Describes the container, as part of model definition.

', 'refs' => [ 'ContainerDefinitionList$member' => NULL, 'CreateModelInput$PrimaryContainer' => '

The location of the primary docker image containing inference code, associated artifacts, and custom environment map that the inference code uses when the model is deployed for predictions.

', 'DescribeModelOutput$PrimaryContainer' => '

The location of the primary inference code, associated artifacts, and custom environment map that the inference code uses when it is deployed in production.

', ], ], 'ContainerDefinitionList' => [ 'base' => NULL, 'refs' => [ 'CreateModelInput$Containers' => '

Specifies the containers in the inference pipeline.

', 'DescribeModelOutput$Containers' => '

The containers in the inference pipeline.

', ], ], 'ContainerHostname' => [ 'base' => NULL, 'refs' => [ 'ContainerDefinition$ContainerHostname' => '

This parameter is ignored for models that contain only a PrimaryContainer.

When a ContainerDefinition is part of an inference pipeline, the value of ths parameter uniquely identifies the container for the purposes of logging and metrics. For information, see Use Logs and Metrics to Monitor an Inference Pipeline. If you don\'t specify a value for this parameter for a ContainerDefinition that is part of an inference pipeline, a unique name is automatically assigned based on the position of the ContainerDefinition in the pipeline. If you specify a value for the ContainerHostName for any ContainerDefinition that is part of an inference pipeline, you must specify a value for the ContainerHostName parameter of every ContainerDefinition in that pipeline.

', 'ModelPackageContainerDefinition$ContainerHostname' => '

The DNS host name for the Docker container.

', ], ], 'ContentClassifier' => [ 'base' => NULL, 'refs' => [ 'ContentClassifiers$member' => NULL, ], ], 'ContentClassifiers' => [ 'base' => NULL, 'refs' => [ 'LabelingJobDataAttributes$ContentClassifiers' => '

Declares that your content is free of personally identifiable information or adult content. Amazon SageMaker may restrict the Amazon Mechanical Turk workers that can view your task based on this information.

', ], ], 'ContentType' => [ 'base' => NULL, 'refs' => [ 'Channel$ContentType' => '

The MIME type of the data.

', 'ContentTypes$member' => NULL, 'TransformInput$ContentType' => '

The multipurpose internet mail extension (MIME) type of the data. Amazon SageMaker uses the MIME type with each http call to transfer data to the transform job.

', ], ], 'ContentTypes' => [ 'base' => NULL, 'refs' => [ 'ChannelSpecification$SupportedContentTypes' => '

The supported MIME types for the data.

', 'InferenceSpecification$SupportedContentTypes' => '

The supported MIME types for the input data.

', ], ], 'ContinuousParameterRange' => [ 'base' => '

A list of continuous hyperparameters to tune.

', 'refs' => [ 'ContinuousParameterRanges$member' => NULL, ], ], 'ContinuousParameterRangeSpecification' => [ 'base' => '

Defines the possible values for a continuous hyperparameter.

', 'refs' => [ 'ParameterRange$ContinuousParameterRangeSpecification' => '

A ContinuousParameterRangeSpecification object that defines the possible values for a continuous hyperparameter.

', ], ], 'ContinuousParameterRanges' => [ 'base' => NULL, 'refs' => [ 'ParameterRanges$ContinuousParameterRanges' => '

The array of ContinuousParameterRange objects that specify ranges of continuous hyperparameters that a hyperparameter tuning job searches.

', ], ], 'CreateAlgorithmInput' => [ 'base' => NULL, 'refs' => [], ], 'CreateAlgorithmOutput' => [ 'base' => NULL, 'refs' => [], ], 'CreateCodeRepositoryInput' => [ 'base' => NULL, 'refs' => [], ], 'CreateCodeRepositoryOutput' => [ 'base' => NULL, 'refs' => [], ], 'CreateCompilationJobRequest' => [ 'base' => NULL, 'refs' => [], ], 'CreateCompilationJobResponse' => [ 'base' => NULL, 'refs' => [], ], 'CreateEndpointConfigInput' => [ 'base' => NULL, 'refs' => [], ], 'CreateEndpointConfigOutput' => [ 'base' => NULL, 'refs' => [], ], 'CreateEndpointInput' => [ 'base' => NULL, 'refs' => [], ], 'CreateEndpointOutput' => [ 'base' => NULL, 'refs' => [], ], 'CreateHyperParameterTuningJobRequest' => [ 'base' => NULL, 'refs' => [], ], 'CreateHyperParameterTuningJobResponse' => [ 'base' => NULL, 'refs' => [], ], 'CreateLabelingJobRequest' => [ 'base' => NULL, 'refs' => [], ], 'CreateLabelingJobResponse' => [ 'base' => NULL, 'refs' => [], ], 'CreateModelInput' => [ 'base' => NULL, 'refs' => [], ], 'CreateModelOutput' => [ 'base' => NULL, 'refs' => [], ], 'CreateModelPackageInput' => [ 'base' => NULL, 'refs' => [], ], 'CreateModelPackageOutput' => [ 'base' => NULL, 'refs' => [], ], 'CreateNotebookInstanceInput' => [ 'base' => NULL, 'refs' => [], ], 'CreateNotebookInstanceLifecycleConfigInput' => [ 'base' => NULL, 'refs' => [], ], 'CreateNotebookInstanceLifecycleConfigOutput' => [ 'base' => NULL, 'refs' => [], ], 'CreateNotebookInstanceOutput' => [ 'base' => NULL, 'refs' => [], ], 'CreatePresignedNotebookInstanceUrlInput' => [ 'base' => NULL, 'refs' => [], ], 'CreatePresignedNotebookInstanceUrlOutput' => [ 'base' => NULL, 'refs' => [], ], 'CreateTrainingJobRequest' => [ 'base' => NULL, 'refs' => [], ], 'CreateTrainingJobResponse' => [ 'base' => NULL, 'refs' => [], ], 'CreateTransformJobRequest' => [ 'base' => NULL, 'refs' => [], ], 'CreateTransformJobResponse' => [ 'base' => NULL, 'refs' => [], ], 'CreateWorkteamRequest' => [ 'base' => NULL, 'refs' => [], ], 'CreateWorkteamResponse' => [ 'base' => NULL, 'refs' => [], ], 'CreationTime' => [ 'base' => NULL, 'refs' => [ 'AlgorithmSummary$CreationTime' => '

A timestamp that shows when the algorithm was created.

', 'CodeRepositorySummary$CreationTime' => '

The date and time that the Git repository was created.

', 'CompilationJobSummary$CreationTime' => '

The time when the model compilation job was created.

', 'DescribeAlgorithmOutput$CreationTime' => '

A timestamp specifying when the algorithm was created.

', 'DescribeCodeRepositoryOutput$CreationTime' => '

The date and time that the repository was created.

', 'DescribeCompilationJobResponse$CreationTime' => '

The time that the model compilation job was created.

', 'DescribeModelPackageOutput$CreationTime' => '

A timestamp specifying when the model package was created.

', 'DescribeNotebookInstanceLifecycleConfigOutput$CreationTime' => '

A timestamp that tells when the lifecycle configuration was created.

', 'DescribeNotebookInstanceOutput$CreationTime' => '

A timestamp. Use this parameter to return the time when the notebook instance was created

', 'ListAlgorithmsInput$CreationTimeAfter' => '

A filter that returns only algorithms created after the specified time (timestamp).

', 'ListAlgorithmsInput$CreationTimeBefore' => '

A filter that returns only algorithms created before the specified time (timestamp).

', 'ListCodeRepositoriesInput$CreationTimeAfter' => '

A filter that returns only Git repositories that were created after the specified time.

', 'ListCodeRepositoriesInput$CreationTimeBefore' => '

A filter that returns only Git repositories that were created before the specified time.

', 'ListCompilationJobsRequest$CreationTimeAfter' => '

A filter that returns the model compilation jobs that were created after a specified time.

', 'ListCompilationJobsRequest$CreationTimeBefore' => '

A filter that returns the model compilation jobs that were created before a specified time.

', 'ListModelPackagesInput$CreationTimeAfter' => '

A filter that returns only model packages created after the specified time (timestamp).

', 'ListModelPackagesInput$CreationTimeBefore' => '

A filter that returns only model packages created before the specified time (timestamp).

', 'ListNotebookInstanceLifecycleConfigsInput$CreationTimeBefore' => '

A filter that returns only lifecycle configurations that were created before the specified time (timestamp).

', 'ListNotebookInstanceLifecycleConfigsInput$CreationTimeAfter' => '

A filter that returns only lifecycle configurations that were created after the specified time (timestamp).

', 'ListNotebookInstancesInput$CreationTimeBefore' => '

A filter that returns only notebook instances that were created before the specified time (timestamp).

', 'ListNotebookInstancesInput$CreationTimeAfter' => '

A filter that returns only notebook instances that were created after the specified time (timestamp).

', 'ModelPackageSummary$CreationTime' => '

A timestamp that shows when the model package was created.

', 'NotebookInstanceLifecycleConfigSummary$CreationTime' => '

A timestamp that tells when the lifecycle configuration was created.

', 'NotebookInstanceSummary$CreationTime' => '

A timestamp that shows when the notebook instance was created.

', ], ], 'DataInputConfig' => [ 'base' => NULL, 'refs' => [ 'InputConfig$DataInputConfig' => '

Specifies the name and shape of the expected data inputs for your trained model with a JSON dictionary form. The data inputs are InputConfig$Framework specific.

  • TensorFlow: You must specify the name and shape (NHWC format) of the expected data inputs using a dictionary format for your trained model. The dictionary formats required for the console and CLI are different.

    • Examples for one input:

      • If using the console, {"input":[1,1024,1024,3]}

      • If using the CLI, {\\"input\\":[1,1024,1024,3]}

    • Examples for two inputs:

      • If using the console, {"data1": [1,28,28,1], "data2":[1,28,28,1]}

      • If using the CLI, {\\"data1\\": [1,28,28,1], \\"data2\\":[1,28,28,1]}

  • MXNET/ONNX: You must specify the name and shape (NCHW format) of the expected data inputs in order using a dictionary format for your trained model. The dictionary formats required for the console and CLI are different.

    • Examples for one input:

      • If using the console, {"data":[1,3,1024,1024]}

      • If using the CLI, {\\"data\\":[1,3,1024,1024]}

    • Examples for two inputs:

      • If using the console, {"var1": [1,1,28,28], "var2":[1,1,28,28]}

      • If using the CLI, {\\"var1\\": [1,1,28,28], \\"var2\\":[1,1,28,28]}

  • PyTorch: You can either specify the name and shape (NCHW format) of expected data inputs in order using a dictionary format for your trained model or you can specify the shape only using a list format. The dictionary formats required for the console and CLI are different. The list formats for the console and CLI are the same.

    • Examples for one input in dictionary format:

      • If using the console, {"input0":[1,3,224,224]}

      • If using the CLI, {\\"input0\\":[1,3,224,224]}

    • Example for one input in list format: [[1,3,224,224]]

    • Examples for two inputs in dictionary format:

      • If using the console, {"input0":[1,3,224,224], "input1":[1,3,224,224]}

      • If using the CLI, {\\"input0\\":[1,3,224,224], \\"input1\\":[1,3,224,224]}

    • Example for two inputs in list format: [[1,3,224,224], [1,3,224,224]]

  • XGBOOST: input data name and shape are not needed.

', ], ], 'DataSource' => [ 'base' => '

Describes the location of the channel data.

', 'refs' => [ 'Channel$DataSource' => '

The location of the channel data.

', ], ], 'DeleteAlgorithmInput' => [ 'base' => NULL, 'refs' => [], ], 'DeleteCodeRepositoryInput' => [ 'base' => NULL, 'refs' => [], ], 'DeleteEndpointConfigInput' => [ 'base' => NULL, 'refs' => [], ], 'DeleteEndpointInput' => [ 'base' => NULL, 'refs' => [], ], 'DeleteModelInput' => [ 'base' => NULL, 'refs' => [], ], 'DeleteModelPackageInput' => [ 'base' => NULL, 'refs' => [], ], 'DeleteNotebookInstanceInput' => [ 'base' => NULL, 'refs' => [], ], 'DeleteNotebookInstanceLifecycleConfigInput' => [ 'base' => NULL, 'refs' => [], ], 'DeleteTagsInput' => [ 'base' => NULL, 'refs' => [], ], 'DeleteTagsOutput' => [ 'base' => NULL, 'refs' => [], ], 'DeleteWorkteamRequest' => [ 'base' => NULL, 'refs' => [], ], 'DeleteWorkteamResponse' => [ 'base' => NULL, 'refs' => [], ], 'DeployedImage' => [ 'base' => '

Gets the Amazon EC2 Container Registry path of the docker image of the model that is hosted in this ProductionVariant.

If you used the registry/repository[:tag] form to specify the image path of the primary container when you created the model hosted in this ProductionVariant, the path resolves to a path of the form registry/repository[@digest]. A digest is a hash value that identifies a specific version of an image. For information about Amazon ECR paths, see Pulling an Image in the Amazon ECR User Guide.

', 'refs' => [ 'DeployedImages$member' => NULL, ], ], 'DeployedImages' => [ 'base' => NULL, 'refs' => [ 'ProductionVariantSummary$DeployedImages' => '

An array of DeployedImage objects that specify the Amazon EC2 Container Registry paths of the inference images deployed on instances of this ProductionVariant.

', ], ], 'DescribeAlgorithmInput' => [ 'base' => NULL, 'refs' => [], ], 'DescribeAlgorithmOutput' => [ 'base' => NULL, 'refs' => [], ], 'DescribeCodeRepositoryInput' => [ 'base' => NULL, 'refs' => [], ], 'DescribeCodeRepositoryOutput' => [ 'base' => NULL, 'refs' => [], ], 'DescribeCompilationJobRequest' => [ 'base' => NULL, 'refs' => [], ], 'DescribeCompilationJobResponse' => [ 'base' => NULL, 'refs' => [], ], 'DescribeEndpointConfigInput' => [ 'base' => NULL, 'refs' => [], ], 'DescribeEndpointConfigOutput' => [ 'base' => NULL, 'refs' => [], ], 'DescribeEndpointInput' => [ 'base' => NULL, 'refs' => [], ], 'DescribeEndpointOutput' => [ 'base' => NULL, 'refs' => [], ], 'DescribeHyperParameterTuningJobRequest' => [ 'base' => NULL, 'refs' => [], ], 'DescribeHyperParameterTuningJobResponse' => [ 'base' => NULL, 'refs' => [], ], 'DescribeLabelingJobRequest' => [ 'base' => NULL, 'refs' => [], ], 'DescribeLabelingJobResponse' => [ 'base' => NULL, 'refs' => [], ], 'DescribeModelInput' => [ 'base' => NULL, 'refs' => [], ], 'DescribeModelOutput' => [ 'base' => NULL, 'refs' => [], ], 'DescribeModelPackageInput' => [ 'base' => NULL, 'refs' => [], ], 'DescribeModelPackageOutput' => [ 'base' => NULL, 'refs' => [], ], 'DescribeNotebookInstanceInput' => [ 'base' => NULL, 'refs' => [], ], 'DescribeNotebookInstanceLifecycleConfigInput' => [ 'base' => NULL, 'refs' => [], ], 'DescribeNotebookInstanceLifecycleConfigOutput' => [ 'base' => NULL, 'refs' => [], ], 'DescribeNotebookInstanceOutput' => [ 'base' => NULL, 'refs' => [], ], 'DescribeSubscribedWorkteamRequest' => [ 'base' => NULL, 'refs' => [], ], 'DescribeSubscribedWorkteamResponse' => [ 'base' => NULL, 'refs' => [], ], 'DescribeTrainingJobRequest' => [ 'base' => NULL, 'refs' => [], ], 'DescribeTrainingJobResponse' => [ 'base' => NULL, 'refs' => [], ], 'DescribeTransformJobRequest' => [ 'base' => NULL, 'refs' => [], ], 'DescribeTransformJobResponse' => [ 'base' => NULL, 'refs' => [], ], 'DescribeWorkteamRequest' => [ 'base' => NULL, 'refs' => [], ], 'DescribeWorkteamResponse' => [ 'base' => NULL, 'refs' => [], ], 'DesiredWeightAndCapacity' => [ 'base' => '

Specifies weight and capacity values for a production variant.

', 'refs' => [ 'DesiredWeightAndCapacityList$member' => NULL, ], ], 'DesiredWeightAndCapacityList' => [ 'base' => NULL, 'refs' => [ 'UpdateEndpointWeightsAndCapacitiesInput$DesiredWeightsAndCapacities' => '

An object that provides new capacity and weight values for a variant.

', ], ], 'DetailedAlgorithmStatus' => [ 'base' => NULL, 'refs' => [ 'AlgorithmStatusItem$Status' => '

The current status.

', ], ], 'DetailedModelPackageStatus' => [ 'base' => NULL, 'refs' => [ 'ModelPackageStatusItem$Status' => '

The current status.

', ], ], 'DirectInternetAccess' => [ 'base' => NULL, 'refs' => [ 'CreateNotebookInstanceInput$DirectInternetAccess' => '

Sets whether Amazon SageMaker provides internet access to the notebook instance. If you set this to Disabled this notebook instance will be able to access resources only in your VPC, and will not be able to connect to Amazon SageMaker training and endpoint services unless your configure a NAT Gateway in your VPC.

For more information, see Notebook Instances Are Internet-Enabled by Default. You can set the value of this parameter to Disabled only if you set a value for the SubnetId parameter.

', 'DescribeNotebookInstanceOutput$DirectInternetAccess' => '

Describes whether Amazon SageMaker provides internet access to the notebook instance. If this value is set to Disabled, the notebook instance does not have internet access, and cannot connect to Amazon SageMaker training and endpoint services.

For more information, see Notebook Instances Are Internet-Enabled by Default.

', ], ], 'DisassociateAdditionalCodeRepositories' => [ 'base' => NULL, 'refs' => [ 'UpdateNotebookInstanceInput$DisassociateAdditionalCodeRepositories' => '

A list of names or URLs of the default Git repositories to remove from this notebook instance. This operation is idempotent. If you specify a Git repository that is not associated with the notebook instance when you call this method, it does not throw an error.

', ], ], 'DisassociateDefaultCodeRepository' => [ 'base' => NULL, 'refs' => [ 'UpdateNotebookInstanceInput$DisassociateDefaultCodeRepository' => '

The name or URL of the default Git repository to remove from this notebook instance. This operation is idempotent. If you specify a Git repository that is not associated with the notebook instance when you call this method, it does not throw an error.

', ], ], 'DisassociateNotebookInstanceAcceleratorTypes' => [ 'base' => NULL, 'refs' => [ 'UpdateNotebookInstanceInput$DisassociateAcceleratorTypes' => '

A list of the Elastic Inference (EI) instance types to remove from this notebook instance. This operation is idempotent. If you specify an accelerator type that is not associated with the notebook instance when you call this method, it does not throw an error.

', ], ], 'DisassociateNotebookInstanceLifecycleConfig' => [ 'base' => NULL, 'refs' => [ 'UpdateNotebookInstanceInput$DisassociateLifecycleConfig' => '

Set to true to remove the notebook instance lifecycle configuration currently associated with the notebook instance. This operation is idempotent. If you specify a lifecycle configuration that is not associated with the notebook instance when you call this method, it does not throw an error.

', ], ], 'Dollars' => [ 'base' => NULL, 'refs' => [ 'USD$Dollars' => '

The whole number of dollars in the amount.

', ], ], 'EndpointArn' => [ 'base' => NULL, 'refs' => [ 'CreateEndpointOutput$EndpointArn' => '

The Amazon Resource Name (ARN) of the endpoint.

', 'DescribeEndpointOutput$EndpointArn' => '

The Amazon Resource Name (ARN) of the endpoint.

', 'EndpointSummary$EndpointArn' => '

The Amazon Resource Name (ARN) of the endpoint.

', 'UpdateEndpointOutput$EndpointArn' => '

The Amazon Resource Name (ARN) of the endpoint.

', 'UpdateEndpointWeightsAndCapacitiesOutput$EndpointArn' => '

The Amazon Resource Name (ARN) of the updated endpoint.

', ], ], 'EndpointConfigArn' => [ 'base' => NULL, 'refs' => [ 'CreateEndpointConfigOutput$EndpointConfigArn' => '

The Amazon Resource Name (ARN) of the endpoint configuration.

', 'DescribeEndpointConfigOutput$EndpointConfigArn' => '

The Amazon Resource Name (ARN) of the endpoint configuration.

', 'EndpointConfigSummary$EndpointConfigArn' => '

The Amazon Resource Name (ARN) of the endpoint configuration.

', ], ], 'EndpointConfigName' => [ 'base' => NULL, 'refs' => [ 'CreateEndpointConfigInput$EndpointConfigName' => '

The name of the endpoint configuration. You specify this name in a CreateEndpoint request.

', 'CreateEndpointInput$EndpointConfigName' => '

The name of an endpoint configuration. For more information, see CreateEndpointConfig.

', 'DeleteEndpointConfigInput$EndpointConfigName' => '

The name of the endpoint configuration that you want to delete.

', 'DescribeEndpointConfigInput$EndpointConfigName' => '

The name of the endpoint configuration.

', 'DescribeEndpointConfigOutput$EndpointConfigName' => '

Name of the Amazon SageMaker endpoint configuration.

', 'DescribeEndpointOutput$EndpointConfigName' => '

The name of the endpoint configuration associated with this endpoint.

', 'EndpointConfigSummary$EndpointConfigName' => '

The name of the endpoint configuration.

', 'UpdateEndpointInput$EndpointConfigName' => '

The name of the new endpoint configuration.

', ], ], 'EndpointConfigNameContains' => [ 'base' => NULL, 'refs' => [ 'ListEndpointConfigsInput$NameContains' => '

A string in the endpoint configuration name. This filter returns only endpoint configurations whose name contains the specified string.

', ], ], 'EndpointConfigSortKey' => [ 'base' => NULL, 'refs' => [ 'ListEndpointConfigsInput$SortBy' => '

The field to sort results by. The default is CreationTime.

', ], ], 'EndpointConfigSummary' => [ 'base' => '

Provides summary information for an endpoint configuration.

', 'refs' => [ 'EndpointConfigSummaryList$member' => NULL, ], ], 'EndpointConfigSummaryList' => [ 'base' => NULL, 'refs' => [ 'ListEndpointConfigsOutput$EndpointConfigs' => '

An array of endpoint configurations.

', ], ], 'EndpointName' => [ 'base' => NULL, 'refs' => [ 'CreateEndpointInput$EndpointName' => '

The name of the endpoint. The name must be unique within an AWS Region in your AWS account.

', 'DeleteEndpointInput$EndpointName' => '

The name of the endpoint that you want to delete.

', 'DescribeEndpointInput$EndpointName' => '

The name of the endpoint.

', 'DescribeEndpointOutput$EndpointName' => '

Name of the endpoint.

', 'EndpointSummary$EndpointName' => '

The name of the endpoint.

', 'UpdateEndpointInput$EndpointName' => '

The name of the endpoint whose configuration you want to update.

', 'UpdateEndpointWeightsAndCapacitiesInput$EndpointName' => '

The name of an existing Amazon SageMaker endpoint.

', ], ], 'EndpointNameContains' => [ 'base' => NULL, 'refs' => [ 'ListEndpointsInput$NameContains' => '

A string in endpoint names. This filter returns only endpoints whose name contains the specified string.

', ], ], 'EndpointSortKey' => [ 'base' => NULL, 'refs' => [ 'ListEndpointsInput$SortBy' => '

Sorts the list of results. The default is CreationTime.

', ], ], 'EndpointStatus' => [ 'base' => NULL, 'refs' => [ 'DescribeEndpointOutput$EndpointStatus' => '

The status of the endpoint.

  • OutOfService: Endpoint is not available to take incoming requests.

  • Creating: CreateEndpoint is executing.

  • Updating: UpdateEndpoint or UpdateEndpointWeightsAndCapacities is executing.

  • SystemUpdating: Endpoint is undergoing maintenance and cannot be updated or deleted or re-scaled until it has completed. This maintenance operation does not change any customer-specified values such as VPC config, KMS encryption, model, instance type, or instance count.

  • RollingBack: Endpoint fails to scale up or down or change its variant weight and is in the process of rolling back to its previous configuration. Once the rollback completes, endpoint returns to an InService status. This transitional status only applies to an endpoint that has autoscaling enabled and is undergoing variant weight or capacity changes as part of an UpdateEndpointWeightsAndCapacities call or when the UpdateEndpointWeightsAndCapacities operation is called explicitly.

  • InService: Endpoint is available to process incoming requests.

  • Deleting: DeleteEndpoint is executing.

  • Failed: Endpoint could not be created, updated, or re-scaled. Use DescribeEndpointOutput$FailureReason for information about the failure. DeleteEndpoint is the only operation that can be performed on a failed endpoint.

', 'EndpointSummary$EndpointStatus' => '

The status of the endpoint.

  • OutOfService: Endpoint is not available to take incoming requests.

  • Creating: CreateEndpoint is executing.

  • Updating: UpdateEndpoint or UpdateEndpointWeightsAndCapacities is executing.

  • SystemUpdating: Endpoint is undergoing maintenance and cannot be updated or deleted or re-scaled until it has completed. This maintenance operation does not change any customer-specified values such as VPC config, KMS encryption, model, instance type, or instance count.

  • RollingBack: Endpoint fails to scale up or down or change its variant weight and is in the process of rolling back to its previous configuration. Once the rollback completes, endpoint returns to an InService status. This transitional status only applies to an endpoint that has autoscaling enabled and is undergoing variant weight or capacity changes as part of an UpdateEndpointWeightsAndCapacities call or when the UpdateEndpointWeightsAndCapacities operation is called explicitly.

  • InService: Endpoint is available to process incoming requests.

  • Deleting: DeleteEndpoint is executing.

  • Failed: Endpoint could not be created, updated, or re-scaled. Use DescribeEndpointOutput$FailureReason for information about the failure. DeleteEndpoint is the only operation that can be performed on a failed endpoint.

To get a list of endpoints with a specified status, use the ListEndpointsInput$StatusEquals filter.

', 'ListEndpointsInput$StatusEquals' => '

A filter that returns only endpoints with the specified status.

', ], ], 'EndpointSummary' => [ 'base' => '

Provides summary information for an endpoint.

', 'refs' => [ 'EndpointSummaryList$member' => NULL, ], ], 'EndpointSummaryList' => [ 'base' => NULL, 'refs' => [ 'ListEndpointsOutput$Endpoints' => '

An array or endpoint objects.

', ], ], 'EntityDescription' => [ 'base' => NULL, 'refs' => [ 'AlgorithmSummary$AlgorithmDescription' => '

A brief description of the algorithm.

', 'ChannelSpecification$Description' => '

A brief description of the channel.

', 'CreateAlgorithmInput$AlgorithmDescription' => '

A description of the algorithm.

', 'CreateModelPackageInput$ModelPackageDescription' => '

A description of the model package.

', 'DescribeAlgorithmOutput$AlgorithmDescription' => '

A brief summary about the algorithm.

', 'DescribeModelPackageOutput$ModelPackageDescription' => '

A brief summary of the model package.

', 'HyperParameterSpecification$Description' => '

A brief description of the hyperparameter.

', 'ModelPackageSummary$ModelPackageDescription' => '

A brief description of the model package.

', ], ], 'EntityName' => [ 'base' => NULL, 'refs' => [ 'AlgorithmStatusItem$Name' => '

The name of the algorithm for which the overall status is being reported.

', 'AlgorithmSummary$AlgorithmName' => '

The name of the algorithm that is described by the summary.

', 'AlgorithmValidationProfile$ProfileName' => '

The name of the profile for the algorithm. The name must have 1 to 63 characters. Valid characters are a-z, A-Z, 0-9, and - (hyphen).

', 'CodeRepositorySummary$CodeRepositoryName' => '

The name of the Git repository.

', 'CompilationJobSummary$CompilationJobName' => '

The name of the model compilation job that you want a summary for.

', 'CreateAlgorithmInput$AlgorithmName' => '

The name of the algorithm.

', 'CreateCodeRepositoryInput$CodeRepositoryName' => '

The name of the Git repository. The name must have 1 to 63 characters. Valid characters are a-z, A-Z, 0-9, and - (hyphen).

', 'CreateCompilationJobRequest$CompilationJobName' => '

A name for the model compilation job. The name must be unique within the AWS Region and within your AWS account.

', 'CreateModelPackageInput$ModelPackageName' => '

The name of the model package. The name must have 1 to 63 characters. Valid characters are a-z, A-Z, 0-9, and - (hyphen).

', 'DeleteAlgorithmInput$AlgorithmName' => '

The name of the algorithm to delete.

', 'DeleteCodeRepositoryInput$CodeRepositoryName' => '

The name of the Git repository to delete.

', 'DeleteModelPackageInput$ModelPackageName' => '

The name of the model package. The name must have 1 to 63 characters. Valid characters are a-z, A-Z, 0-9, and - (hyphen).

', 'DescribeAlgorithmOutput$AlgorithmName' => '

The name of the algorithm being described.

', 'DescribeCodeRepositoryInput$CodeRepositoryName' => '

The name of the Git repository to describe.

', 'DescribeCodeRepositoryOutput$CodeRepositoryName' => '

The name of the Git repository.

', 'DescribeCompilationJobRequest$CompilationJobName' => '

The name of the model compilation job that you want information about.

', 'DescribeCompilationJobResponse$CompilationJobName' => '

The name of the model compilation job.

', 'DescribeModelPackageOutput$ModelPackageName' => '

The name of the model package being described.

', 'ModelPackageStatusItem$Name' => '

The name of the model package for which the overall status is being reported.

', 'ModelPackageSummary$ModelPackageName' => '

The name of the model package.

', 'ModelPackageValidationProfile$ProfileName' => '

The name of the profile for the model package.

', 'StopCompilationJobRequest$CompilationJobName' => '

The name of the model compilation job to stop.

', 'UpdateCodeRepositoryInput$CodeRepositoryName' => '

The name of the Git repository to update.

', ], ], 'EnvironmentKey' => [ 'base' => NULL, 'refs' => [ 'EnvironmentMap$key' => NULL, ], ], 'EnvironmentMap' => [ 'base' => NULL, 'refs' => [ 'ContainerDefinition$Environment' => '

The environment variables to set in the Docker container. Each key and value in the Environment string to string map can have length of up to 1024. We support up to 16 entries in the map.

', ], ], 'EnvironmentValue' => [ 'base' => NULL, 'refs' => [ 'EnvironmentMap$value' => NULL, ], ], 'FailureReason' => [ 'base' => NULL, 'refs' => [ 'DescribeCompilationJobResponse$FailureReason' => '

If a model compilation job failed, the reason it failed.

', 'DescribeEndpointOutput$FailureReason' => '

If the status of the endpoint is Failed, the reason why it failed.

', 'DescribeHyperParameterTuningJobResponse$FailureReason' => '

If the tuning job failed, the reason it failed.

', 'DescribeLabelingJobResponse$FailureReason' => '

If the job failed, the reason that it failed.

', 'DescribeNotebookInstanceOutput$FailureReason' => '

If status is Failed, the reason it failed.

', 'DescribeTrainingJobResponse$FailureReason' => '

If the training job failed, the reason it failed.

', 'DescribeTransformJobResponse$FailureReason' => '

If the transform job failed, FailureReason describes why it failed. A transform job creates a log file, which includes error messages, and stores it as an Amazon S3 object. For more information, see Log Amazon SageMaker Events with Amazon CloudWatch.

', 'HyperParameterTrainingJobSummary$FailureReason' => '

The reason that the training job failed.

', 'LabelingJobSummary$FailureReason' => '

If the LabelingJobStatus field is Failed, this field contains a description of the error.

', 'ResourceInUse$Message' => NULL, 'ResourceLimitExceeded$Message' => NULL, 'ResourceNotFound$Message' => NULL, 'TrainingJob$FailureReason' => '

If the training job failed, the reason it failed.

', 'TransformJobSummary$FailureReason' => '

If the transform job failed, the reason it failed.

', ], ], 'Filter' => [ 'base' => '

A conditional statement for a search expression that includes a Boolean operator, a resource property, and a value.

If you don\'t specify an Operator and a Value, the filter searches for only the specified property. For example, defining a Filter for the FailureReason for the TrainingJob Resource searches for training job objects that have a value in the FailureReason field.

If you specify a Value, but not an Operator, Amazon SageMaker uses the equals operator as the default.

In search, there are several property types:

Metrics

To define a metric filter, enter a value using the form "Metrics.<name>", where <name> is a metric name. For example, the following filter searches for training jobs with an "accuracy" metric greater than "0.9":

{

"Name": "Metrics.accuracy",

"Operator": "GREATER_THAN",

"Value": "0.9"

}

HyperParameters

To define a hyperparameter filter, enter a value with the form "HyperParameters.<name>". Decimal hyperparameter values are treated as a decimal in a comparison if the specified Value is also a decimal value. If the specified Value is an integer, the decimal hyperparameter values are treated as integers. For example, the following filter is satisfied by training jobs with a "learning_rate" hyperparameter that is less than "0.5":

{

"Name": "HyperParameters.learning_rate",

"Operator": "LESS_THAN",

"Value": "0.5"

}

Tags

To define a tag filter, enter a value with the form "Tags.<key>".

', 'refs' => [ 'FilterList$member' => NULL, ], ], 'FilterList' => [ 'base' => NULL, 'refs' => [ 'NestedFilters$Filters' => '

A list of filters. Each filter acts on a property. Filters must contain at least one Filters value. For example, a NestedFilters call might include a filter on the PropertyName parameter of the InputDataConfig property: InputDataConfig.DataSource.S3DataSource.S3Uri.

', 'SearchExpression$Filters' => '

A list of filter objects.

', ], ], 'FilterValue' => [ 'base' => NULL, 'refs' => [ 'Filter$Value' => '

A value used with Resource and Operator to determine if objects satisfy the filter\'s condition. For numerical properties, Value must be an integer or floating-point decimal. For timestamp properties, Value must be an ISO 8601 date-time string of the following format: YYYY-mm-dd\'T\'HH:MM:SS.

', ], ], 'FinalHyperParameterTuningJobObjectiveMetric' => [ 'base' => '

Shows the final value for the objective metric for a training job that was launched by a hyperparameter tuning job. You define the objective metric in the HyperParameterTuningJobObjective parameter of HyperParameterTuningJobConfig.

', 'refs' => [ 'HyperParameterTrainingJobSummary$FinalHyperParameterTuningJobObjectiveMetric' => '

The FinalHyperParameterTuningJobObjectiveMetric object that specifies the value of the objective metric of the tuning job that launched this training job.

', ], ], 'FinalMetricDataList' => [ 'base' => NULL, 'refs' => [ 'DescribeTrainingJobResponse$FinalMetricDataList' => '

A collection of MetricData objects that specify the names, values, and dates and times that the training algorithm emitted to Amazon CloudWatch.

', 'TrainingJob$FinalMetricDataList' => '

A list of final metric values that are set when the training job completes. Used only if the training job was configured to use metrics.

', ], ], 'Float' => [ 'base' => NULL, 'refs' => [ 'MetricData$Value' => '

The value of the metric.

', ], ], 'Framework' => [ 'base' => NULL, 'refs' => [ 'InputConfig$Framework' => '

Identifies the framework in which the model was trained. For example: TENSORFLOW.

', ], ], 'GetSearchSuggestionsRequest' => [ 'base' => NULL, 'refs' => [], ], 'GetSearchSuggestionsResponse' => [ 'base' => NULL, 'refs' => [], ], 'GitConfig' => [ 'base' => '

Specifies configuration details for a Git repository in your AWS account.

', 'refs' => [ 'CodeRepositorySummary$GitConfig' => '

Configuration details for the Git repository, including the URL where it is located and the ARN of the AWS Secrets Manager secret that contains the credentials used to access the repository.

', 'CreateCodeRepositoryInput$GitConfig' => '

Specifies details about the repository, including the URL where the repository is located, the default branch, and credentials to use to access the repository.

', 'DescribeCodeRepositoryOutput$GitConfig' => '

Configuration details about the repository, including the URL where the repository is located, the default branch, and the Amazon Resource Name (ARN) of the AWS Secrets Manager secret that contains the credentials used to access the repository.

', ], ], 'GitConfigForUpdate' => [ 'base' => '

Specifies configuration details for a Git repository when the repository is updated.

', 'refs' => [ 'UpdateCodeRepositoryInput$GitConfig' => '

The configuration of the git repository, including the URL and the Amazon Resource Name (ARN) of the AWS Secrets Manager secret that contains the credentials used to access the repository. The secret must have a staging label of AWSCURRENT and must be in the following format:

{"username": UserName, "password": Password}

', ], ], 'GitConfigUrl' => [ 'base' => NULL, 'refs' => [ 'GitConfig$RepositoryUrl' => '

The URL where the Git repository is located.

', ], ], 'HumanTaskConfig' => [ 'base' => '

Information required for human workers to complete a labeling task.

', 'refs' => [ 'CreateLabelingJobRequest$HumanTaskConfig' => '

Configures the information required for human workers to complete a labeling task.

', 'DescribeLabelingJobResponse$HumanTaskConfig' => '

Configuration information required for human workers to complete a labeling task.

', ], ], 'HyperParameterAlgorithmSpecification' => [ 'base' => '

Specifies which training algorithm to use for training jobs that a hyperparameter tuning job launches and the metrics to monitor.

', 'refs' => [ 'HyperParameterTrainingJobDefinition$AlgorithmSpecification' => '

The HyperParameterAlgorithmSpecification object that specifies the resource algorithm to use for the training jobs that the tuning job launches.

', ], ], 'HyperParameterScalingType' => [ 'base' => NULL, 'refs' => [ 'ContinuousParameterRange$ScalingType' => '

The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling. One of the following values:

Auto

Amazon SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.

Linear

Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.

Logarithmic

Hyperparemeter tuning searches the values in the hyperparameter range by using a logarithmic scale.

Logarithmic scaling works only for ranges that have only values greater than 0.

ReverseLogarithmic

Hyperparemeter tuning searches the values in the hyperparameter range by using a reverse logarithmic scale.

Reverse logarithmic scaling works only for ranges that are entirely within the range 0<=x<1.0.

', 'IntegerParameterRange$ScalingType' => '

The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling. One of the following values:

Auto

Amazon SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.

Linear

Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.

Logarithmic

Hyperparemeter tuning searches the values in the hyperparameter range by using a logarithmic scale.

Logarithmic scaling works only for ranges that have only values greater than 0.

', ], ], 'HyperParameterSpecification' => [ 'base' => '

Defines a hyperparameter to be used by an algorithm.

', 'refs' => [ 'HyperParameterSpecifications$member' => NULL, ], ], 'HyperParameterSpecifications' => [ 'base' => NULL, 'refs' => [ 'TrainingSpecification$SupportedHyperParameters' => '

A list of the HyperParameterSpecification objects, that define the supported hyperparameters. This is required if the algorithm supports automatic model tuning.>

', ], ], 'HyperParameterTrainingJobDefinition' => [ 'base' => '

Defines the training jobs launched by a hyperparameter tuning job.

', 'refs' => [ 'CreateHyperParameterTuningJobRequest$TrainingJobDefinition' => '

The HyperParameterTrainingJobDefinition object that describes the training jobs that this tuning job launches, including static hyperparameters, input data configuration, output data configuration, resource configuration, and stopping condition.

', 'DescribeHyperParameterTuningJobResponse$TrainingJobDefinition' => '

The HyperParameterTrainingJobDefinition object that specifies the definition of the training jobs that this tuning job launches.

', ], ], 'HyperParameterTrainingJobSummaries' => [ 'base' => NULL, 'refs' => [ 'ListTrainingJobsForHyperParameterTuningJobResponse$TrainingJobSummaries' => '

A list of TrainingJobSummary objects that describe the training jobs that the ListTrainingJobsForHyperParameterTuningJob request returned.

', ], ], 'HyperParameterTrainingJobSummary' => [ 'base' => '

Specifies summary information about a training job.

', 'refs' => [ 'DescribeHyperParameterTuningJobResponse$BestTrainingJob' => '

A TrainingJobSummary object that describes the training job that completed with the best current HyperParameterTuningJobObjective.

', 'DescribeHyperParameterTuningJobResponse$OverallBestTrainingJob' => '

If the hyperparameter tuning job is an warm start tuning job with a WarmStartType of IDENTICAL_DATA_AND_ALGORITHM, this is the TrainingJobSummary for the training job with the best objective metric value of all training jobs launched by this tuning job and all parent jobs specified for the warm start tuning job.

', 'HyperParameterTrainingJobSummaries$member' => NULL, ], ], 'HyperParameterTuningJobArn' => [ 'base' => NULL, 'refs' => [ 'CreateHyperParameterTuningJobResponse$HyperParameterTuningJobArn' => '

The Amazon Resource Name (ARN) of the tuning job. Amazon SageMaker assigns an ARN to a hyperparameter tuning job when you create it.

', 'DescribeHyperParameterTuningJobResponse$HyperParameterTuningJobArn' => '

The Amazon Resource Name (ARN) of the tuning job.

', 'DescribeTrainingJobResponse$TuningJobArn' => '

The Amazon Resource Name (ARN) of the associated hyperparameter tuning job if the training job was launched by a hyperparameter tuning job.

', 'HyperParameterTuningJobSummary$HyperParameterTuningJobArn' => '

The Amazon Resource Name (ARN) of the tuning job.

', 'TrainingJob$TuningJobArn' => '

The Amazon Resource Name (ARN) of the associated hyperparameter tuning job if the training job was launched by a hyperparameter tuning job.

', ], ], 'HyperParameterTuningJobConfig' => [ 'base' => '

Configures a hyperparameter tuning job.

', 'refs' => [ 'CreateHyperParameterTuningJobRequest$HyperParameterTuningJobConfig' => '

The HyperParameterTuningJobConfig object that describes the tuning job, including the search strategy, the objective metric used to evaluate training jobs, ranges of parameters to search, and resource limits for the tuning job. For more information, see automatic-model-tuning

', 'DescribeHyperParameterTuningJobResponse$HyperParameterTuningJobConfig' => '

The HyperParameterTuningJobConfig object that specifies the configuration of the tuning job.

', ], ], 'HyperParameterTuningJobName' => [ 'base' => NULL, 'refs' => [ 'CreateHyperParameterTuningJobRequest$HyperParameterTuningJobName' => '

The name of the tuning job. This name is the prefix for the names of all training jobs that this tuning job launches. The name must be unique within the same AWS account and AWS Region. The name must have { } to { } characters. Valid characters are a-z, A-Z, 0-9, and : + = @ _ % - (hyphen). The name is not case sensitive.

', 'DescribeHyperParameterTuningJobRequest$HyperParameterTuningJobName' => '

The name of the tuning job to describe.

', 'DescribeHyperParameterTuningJobResponse$HyperParameterTuningJobName' => '

The name of the tuning job.

', 'HyperParameterTrainingJobSummary$TuningJobName' => '

The HyperParameter tuning job that launched the training job.

', 'HyperParameterTuningJobSummary$HyperParameterTuningJobName' => '

The name of the tuning job.

', 'ListTrainingJobsForHyperParameterTuningJobRequest$HyperParameterTuningJobName' => '

The name of the tuning job whose training jobs you want to list.

', 'ParentHyperParameterTuningJob$HyperParameterTuningJobName' => '

The name of the hyperparameter tuning job to be used as a starting point for a new hyperparameter tuning job.

', 'StopHyperParameterTuningJobRequest$HyperParameterTuningJobName' => '

The name of the tuning job to stop.

', ], ], 'HyperParameterTuningJobObjective' => [ 'base' => '

Defines the objective metric for a hyperparameter tuning job. Hyperparameter tuning uses the value of this metric to evaluate the training jobs it launches, and returns the training job that results in either the highest or lowest value for this metric, depending on the value you specify for the Type parameter.

', 'refs' => [ 'HyperParameterTuningJobConfig$HyperParameterTuningJobObjective' => '

The HyperParameterTuningJobObjective object that specifies the objective metric for this tuning job.

', 'HyperParameterTuningJobObjectives$member' => NULL, ], ], 'HyperParameterTuningJobObjectiveType' => [ 'base' => NULL, 'refs' => [ 'FinalHyperParameterTuningJobObjectiveMetric$Type' => '

Whether to minimize or maximize the objective metric. Valid values are Minimize and Maximize.

', 'HyperParameterTuningJobObjective$Type' => '

Whether to minimize or maximize the objective metric.

', ], ], 'HyperParameterTuningJobObjectives' => [ 'base' => NULL, 'refs' => [ 'TrainingSpecification$SupportedTuningJobObjectiveMetrics' => '

A list of the metrics that the algorithm emits that can be used as the objective metric in a hyperparameter tuning job.

', ], ], 'HyperParameterTuningJobSortByOptions' => [ 'base' => NULL, 'refs' => [ 'ListHyperParameterTuningJobsRequest$SortBy' => '

The field to sort results by. The default is Name.

', ], ], 'HyperParameterTuningJobStatus' => [ 'base' => NULL, 'refs' => [ 'DescribeHyperParameterTuningJobResponse$HyperParameterTuningJobStatus' => '

The status of the tuning job: InProgress, Completed, Failed, Stopping, or Stopped.

', 'HyperParameterTuningJobSummary$HyperParameterTuningJobStatus' => '

The status of the tuning job.

', 'ListHyperParameterTuningJobsRequest$StatusEquals' => '

A filter that returns only tuning jobs with the specified status.

', ], ], 'HyperParameterTuningJobStrategyType' => [ 'base' => '

The strategy hyperparameter tuning uses to find the best combination of hyperparameters for your model. Currently, the only supported value is Bayesian.

', 'refs' => [ 'HyperParameterTuningJobConfig$Strategy' => '

Specifies how hyperparameter tuning chooses the combinations of hyperparameter values to use for the training job it launches. To use the Bayesian search stategy, set this to Bayesian. To randomly search, set it to Random. For information about search strategies, see How Hyperparameter Tuning Works.

', 'HyperParameterTuningJobSummary$Strategy' => '

Specifies the search strategy hyperparameter tuning uses to choose which hyperparameters to use for each iteration. Currently, the only valid value is Bayesian.

', ], ], 'HyperParameterTuningJobSummaries' => [ 'base' => NULL, 'refs' => [ 'ListHyperParameterTuningJobsResponse$HyperParameterTuningJobSummaries' => '

A list of HyperParameterTuningJobSummary objects that describe the tuning jobs that the ListHyperParameterTuningJobs request returned.

', ], ], 'HyperParameterTuningJobSummary' => [ 'base' => '

Provides summary information about a hyperparameter tuning job.

', 'refs' => [ 'HyperParameterTuningJobSummaries$member' => NULL, ], ], 'HyperParameterTuningJobWarmStartConfig' => [ 'base' => '

Specifies the configuration for a hyperparameter tuning job that uses one or more previous hyperparameter tuning jobs as a starting point. The results of previous tuning jobs are used to inform which combinations of hyperparameters to search over in the new tuning job.

All training jobs launched by the new hyperparameter tuning job are evaluated by using the objective metric, and the training job that performs the best is compared to the best training jobs from the parent tuning jobs. From these, the training job that performs the best as measured by the objective metric is returned as the overall best training job.

All training jobs launched by parent hyperparameter tuning jobs and the new hyperparameter tuning jobs count against the limit of training jobs for the tuning job.

', 'refs' => [ 'CreateHyperParameterTuningJobRequest$WarmStartConfig' => '

Specifies the configuration for starting the hyperparameter tuning job using one or more previous tuning jobs as a starting point. The results of previous tuning jobs are used to inform which combinations of hyperparameters to search over in the new tuning job.

All training jobs launched by the new hyperparameter tuning job are evaluated by using the objective metric. If you specify IDENTICAL_DATA_AND_ALGORITHM as the WarmStartType value for the warm start configuration, the training job that performs the best in the new tuning job is compared to the best training jobs from the parent tuning jobs. From these, the training job that performs the best as measured by the objective metric is returned as the overall best training job.

All training jobs launched by parent hyperparameter tuning jobs and the new hyperparameter tuning jobs count against the limit of training jobs for the tuning job.

', 'DescribeHyperParameterTuningJobResponse$WarmStartConfig' => '

The configuration for starting the hyperparameter parameter tuning job using one or more previous tuning jobs as a starting point. The results of previous tuning jobs are used to inform which combinations of hyperparameters to search over in the new tuning job.

', ], ], 'HyperParameterTuningJobWarmStartType' => [ 'base' => NULL, 'refs' => [ 'HyperParameterTuningJobWarmStartConfig$WarmStartType' => '

Specifies one of the following:

IDENTICAL_DATA_AND_ALGORITHM

The new hyperparameter tuning job uses the same input data and training image as the parent tuning jobs. You can change the hyperparameter ranges to search and the maximum number of training jobs that the hyperparameter tuning job launches. You cannot use a new version of the training algorithm, unless the changes in the new version do not affect the algorithm itself. For example, changes that improve logging or adding support for a different data format are allowed. You can also change hyperparameters from tunable to static, and from static to tunable, but the total number of static plus tunable hyperparameters must remain the same as it is in all parent jobs. The objective metric for the new tuning job must be the same as for all parent jobs.

TRANSFER_LEARNING

The new hyperparameter tuning job can include input data, hyperparameter ranges, maximum number of concurrent training jobs, and maximum number of training jobs that are different than those of its parent hyperparameter tuning jobs. The training image can also be a different version from the version used in the parent hyperparameter tuning job. You can also change hyperparameters from tunable to static, and from static to tunable, but the total number of static plus tunable hyperparameters must remain the same as it is in all parent jobs. The objective metric for the new tuning job must be the same as for all parent jobs.

', ], ], 'HyperParameters' => [ 'base' => NULL, 'refs' => [ 'CreateTrainingJobRequest$HyperParameters' => '

Algorithm-specific parameters that influence the quality of the model. You set hyperparameters before you start the learning process. For a list of hyperparameters for each training algorithm provided by Amazon SageMaker, see Algorithms.

You can specify a maximum of 100 hyperparameters. Each hyperparameter is a key-value pair. Each key and value is limited to 256 characters, as specified by the Length Constraint.

', 'DescribeTrainingJobResponse$HyperParameters' => '

Algorithm-specific parameters.

', 'HyperParameterTrainingJobDefinition$StaticHyperParameters' => '

Specifies the values of hyperparameters that do not change for the tuning job.

', 'HyperParameterTrainingJobSummary$TunedHyperParameters' => '

A list of the hyperparameters for which you specified ranges to search.

', 'TrainingJob$HyperParameters' => '

Algorithm-specific parameters.

', 'TrainingJobDefinition$HyperParameters' => '

The hyperparameters used for the training job.

', ], ], 'Image' => [ 'base' => NULL, 'refs' => [ 'ContainerDefinition$Image' => '

The Amazon EC2 Container Registry (Amazon ECR) path where inference code is stored. If you are using your own custom algorithm instead of an algorithm provided by Amazon SageMaker, the inference code must meet Amazon SageMaker requirements. Amazon SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker

', 'DeployedImage$SpecifiedImage' => '

The image path you specified when you created the model.

', 'DeployedImage$ResolvedImage' => '

The specific digest path of the image hosted in this ProductionVariant.

', 'ModelPackageContainerDefinition$Image' => '

The Amazon EC2 Container Registry (Amazon ECR) path where inference code is stored.

If you are using your own custom algorithm instead of an algorithm provided by Amazon SageMaker, the inference code must meet Amazon SageMaker requirements. Amazon SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker.

', 'TrainingSpecification$TrainingImage' => '

The Amazon ECR registry path of the Docker image that contains the training algorithm.

', ], ], 'ImageDigest' => [ 'base' => NULL, 'refs' => [ 'ModelPackageContainerDefinition$ImageDigest' => '

An MD5 hash of the training algorithm that identifies the Docker image used for training.

', 'TrainingSpecification$TrainingImageDigest' => '

An MD5 hash of the training algorithm that identifies the Docker image used for training.

', ], ], 'InferenceSpecification' => [ 'base' => '

Defines how to perform inference generation after a training job is run.

', 'refs' => [ 'CreateAlgorithmInput$InferenceSpecification' => '

Specifies details about inference jobs that the algorithm runs, including the following:

  • The Amazon ECR paths of containers that contain the inference code and model artifacts.

  • The instance types that the algorithm supports for transform jobs and real-time endpoints used for inference.

  • The input and output content formats that the algorithm supports for inference.

', 'CreateModelPackageInput$InferenceSpecification' => '

Specifies details about inference jobs that can be run with models based on this model package, including the following:

  • The Amazon ECR paths of containers that contain the inference code and model artifacts.

  • The instance types that the model package supports for transform jobs and real-time endpoints used for inference.

  • The input and output content formats that the model package supports for inference.

', 'DescribeAlgorithmOutput$InferenceSpecification' => '

Details about inference jobs that the algorithm runs.

', 'DescribeModelPackageOutput$InferenceSpecification' => '

Details about inference jobs that can be run with models based on this model package.

', ], ], 'InputConfig' => [ 'base' => '

Contains information about the location of input model artifacts, the name and shape of the expected data inputs, and the framework in which the model was trained.

', 'refs' => [ 'CreateCompilationJobRequest$InputConfig' => '

Provides information about the location of input model artifacts, the name and shape of the expected data inputs, and the framework in which the model was trained.

', 'DescribeCompilationJobResponse$InputConfig' => '

Information about the location in Amazon S3 of the input model artifacts, the name and shape of the expected data inputs, and the framework in which the model was trained.

', ], ], 'InputDataConfig' => [ 'base' => NULL, 'refs' => [ 'CreateTrainingJobRequest$InputDataConfig' => '

An array of Channel objects. Each channel is a named input source. InputDataConfig describes the input data and its location.

Algorithms can accept input data from one or more channels. For example, an algorithm might have two channels of input data, training_data and validation_data. The configuration for each channel provides the S3 location where the input data is stored. It also provides information about the stored data: the MIME type, compression method, and whether the data is wrapped in RecordIO format.

Depending on the input mode that the algorithm supports, Amazon SageMaker either copies input data files from an S3 bucket to a local directory in the Docker container, or makes it available as input streams.

', 'DescribeTrainingJobResponse$InputDataConfig' => '

An array of Channel objects that describes each data input channel.

', 'HyperParameterTrainingJobDefinition$InputDataConfig' => '

An array of Channel objects that specify the input for the training jobs that the tuning job launches.

', 'TrainingJob$InputDataConfig' => '

An array of Channel objects that describes each data input channel.

', 'TrainingJobDefinition$InputDataConfig' => '

An array of Channel objects, each of which specifies an input source.

', ], ], 'InputModes' => [ 'base' => NULL, 'refs' => [ 'ChannelSpecification$SupportedInputModes' => '

The allowed input mode, either FILE or PIPE.

In FILE mode, Amazon SageMaker copies the data from the input source onto the local Amazon Elastic Block Store (Amazon EBS) volumes before starting your training algorithm. This is the most commonly used input mode.

In PIPE mode, Amazon SageMaker streams input data from the source directly to your algorithm without using the EBS volume.

', ], ], 'InstanceType' => [ 'base' => NULL, 'refs' => [ 'CreateNotebookInstanceInput$InstanceType' => '

The type of ML compute instance to launch for the notebook instance.

', 'DescribeNotebookInstanceOutput$InstanceType' => '

The type of ML compute instance running on the notebook instance.

', 'NotebookInstanceSummary$InstanceType' => '

The type of ML compute instance that the notebook instance is running on.

', 'UpdateNotebookInstanceInput$InstanceType' => '

The Amazon ML compute instance type.

', ], ], 'IntegerParameterRange' => [ 'base' => '

For a hyperparameter of the integer type, specifies the range that a hyperparameter tuning job searches.

', 'refs' => [ 'IntegerParameterRanges$member' => NULL, ], ], 'IntegerParameterRangeSpecification' => [ 'base' => '

Defines the possible values for an integer hyperparameter.

', 'refs' => [ 'ParameterRange$IntegerParameterRangeSpecification' => '

A IntegerParameterRangeSpecification object that defines the possible values for an integer hyperparameter.

', ], ], 'IntegerParameterRanges' => [ 'base' => NULL, 'refs' => [ 'ParameterRanges$IntegerParameterRanges' => '

The array of IntegerParameterRange objects that specify ranges of integer hyperparameters that a hyperparameter tuning job searches.

', ], ], 'JobReferenceCode' => [ 'base' => NULL, 'refs' => [ 'DescribeLabelingJobResponse$JobReferenceCode' => '

A unique identifier for work done as part of a labeling job.

', 'LabelingJobForWorkteamSummary$JobReferenceCode' => '

A unique identifier for a labeling job. You can use this to refer to a specific labeling job.

', ], ], 'JobReferenceCodeContains' => [ 'base' => NULL, 'refs' => [ 'ListLabelingJobsForWorkteamRequest$JobReferenceCodeContains' => '

A filter the limits jobs to only the ones whose job reference code contains the specified string.

', ], ], 'KmsKeyId' => [ 'base' => NULL, 'refs' => [ 'CreateEndpointConfigInput$KmsKeyId' => '

The Amazon Resource Name (ARN) of a AWS Key Management Service key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance that hosts the endpoint.

', 'CreateNotebookInstanceInput$KmsKeyId' => '

The Amazon Resource Name (ARN) of a AWS Key Management Service key that Amazon SageMaker uses to encrypt data on the storage volume attached to your notebook instance. The KMS key you provide must be enabled. For information, see Enabling and Disabling Keys in the AWS Key Management Service Developer Guide.

', 'DescribeEndpointConfigOutput$KmsKeyId' => '

AWS KMS key ID Amazon SageMaker uses to encrypt data when storing it on the ML storage volume attached to the instance.

', 'DescribeNotebookInstanceOutput$KmsKeyId' => '

The AWS KMS key ID Amazon SageMaker uses to encrypt data when storing it on the ML storage volume attached to the instance.

', 'LabelingJobOutputConfig$KmsKeyId' => '

The AWS Key Management Service ID of the key used to encrypt the output data, if any.

', 'LabelingJobResourceConfig$VolumeKmsKeyId' => '

The AWS Key Management Service key ID for the key used to encrypt the output data, if any.

', 'OutputDataConfig$KmsKeyId' => '

The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:

  • // KMS Key ID

    "1234abcd-12ab-34cd-56ef-1234567890ab"

  • // Amazon Resource Name (ARN) of a KMS Key

    "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"

  • // KMS Key Alias

    "alias/ExampleAlias"

  • // Amazon Resource Name (ARN) of a KMS Key Alias

    "arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias"

If you don\'t provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role\'s account. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.

The KMS key policy must grant permission to the IAM role that you specify in your CreateTramsformJob request. For more information, see Using Key Policies in AWS KMS in the AWS Key Management Service Developer Guide.

', 'ResourceConfig$VolumeKmsKeyId' => '

The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the training job. The VolumeKmsKeyId can be any of the following formats:

  • // KMS Key ID

    "1234abcd-12ab-34cd-56ef-1234567890ab"

  • // Amazon Resource Name (ARN) of a KMS Key

    "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"

', 'TransformOutput$KmsKeyId' => '

The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:

  • // KMS Key ID

    "1234abcd-12ab-34cd-56ef-1234567890ab"

  • // Amazon Resource Name (ARN) of a KMS Key

    "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"

  • // KMS Key Alias

    "alias/ExampleAlias"

  • // Amazon Resource Name (ARN) of a KMS Key Alias

    "arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias"

If you don\'t provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role\'s account. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.

The KMS key policy must grant permission to the IAM role that you specify in your CreateTramsformJob request. For more information, see Using Key Policies in AWS KMS in the AWS Key Management Service Developer Guide.

', 'TransformResources$VolumeKmsKeyId' => '

The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the batch transform job. The VolumeKmsKeyId can be any of the following formats:

  • // KMS Key ID

    "1234abcd-12ab-34cd-56ef-1234567890ab"

  • // Amazon Resource Name (ARN) of a KMS Key

    "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"

', ], ], 'LabelAttributeName' => [ 'base' => NULL, 'refs' => [ 'CreateLabelingJobRequest$LabelAttributeName' => '

The attribute name to use for the label in the output manifest file. This is the key for the key/value pair formed with the label that a worker assigns to the object. The name can\'t end with "-metadata". If you are running a semantic segmentation labeling job, the attribute name must end with "-ref". If you are running any other kind of labeling job, the attribute name must not end with "-ref".

', 'DescribeLabelingJobResponse$LabelAttributeName' => '

The attribute used as the label in the output manifest file.

', ], ], 'LabelCounter' => [ 'base' => NULL, 'refs' => [ 'LabelCounters$TotalLabeled' => '

The total number of objects labeled.

', 'LabelCounters$HumanLabeled' => '

The total number of objects labeled by a human worker.

', 'LabelCounters$MachineLabeled' => '

The total number of objects labeled by automated data labeling.

', 'LabelCounters$FailedNonRetryableError' => '

The total number of objects that could not be labeled due to an error.

', 'LabelCounters$Unlabeled' => '

The total number of objects not yet labeled.

', 'LabelCountersForWorkteam$HumanLabeled' => '

The total number of data objects labeled by a human worker.

', 'LabelCountersForWorkteam$PendingHuman' => '

The total number of data objects that need to be labeled by a human worker.

', 'LabelCountersForWorkteam$Total' => '

The total number of tasks in the labeling job.

', ], ], 'LabelCounters' => [ 'base' => '

Provides a breakdown of the number of objects labeled.

', 'refs' => [ 'DescribeLabelingJobResponse$LabelCounters' => '

Provides a breakdown of the number of data objects labeled by humans, the number of objects labeled by machine, the number of objects than couldn\'t be labeled, and the total number of objects labeled.

', 'LabelingJobSummary$LabelCounters' => '

Counts showing the progress of the labeling job.

', ], ], 'LabelCountersForWorkteam' => [ 'base' => '

Provides counts for human-labeled tasks in the labeling job.

', 'refs' => [ 'LabelingJobForWorkteamSummary$LabelCounters' => '

Provides information about the progress of a labeling job.

', ], ], 'LabelingJobAlgorithmSpecificationArn' => [ 'base' => NULL, 'refs' => [ 'LabelingJobAlgorithmsConfig$LabelingJobAlgorithmSpecificationArn' => '

Specifies the Amazon Resource Name (ARN) of the algorithm used for auto-labeling. You must select one of the following ARNs:

  • Image classification

    arn:aws:sagemaker:region:027400017018:labeling-job-algorithm-specification/image-classification

  • Text classification

    arn:aws:sagemaker:region:027400017018:labeling-job-algorithm-specification/text-classification

  • Object detection

    arn:aws:sagemaker:region:027400017018:labeling-job-algorithm-specification/object-detection

', ], ], 'LabelingJobAlgorithmsConfig' => [ 'base' => '

Provides configuration information for auto-labeling of your data objects. A LabelingJobAlgorithmsConfig object must be supplied in order to use auto-labeling.

', 'refs' => [ 'CreateLabelingJobRequest$LabelingJobAlgorithmsConfig' => '

Configures the information required to perform automated data labeling.

', 'DescribeLabelingJobResponse$LabelingJobAlgorithmsConfig' => '

Configuration information for automated data labeling.

', ], ], 'LabelingJobArn' => [ 'base' => NULL, 'refs' => [ 'CreateLabelingJobResponse$LabelingJobArn' => '

The Amazon Resource Name (ARN) of the labeling job. You use this ARN to identify the labeling job.

', 'DescribeLabelingJobResponse$LabelingJobArn' => '

The Amazon Resource Name (ARN) of the labeling job.

', 'DescribeTrainingJobResponse$LabelingJobArn' => '

The Amazon Resource Name (ARN) of the Amazon SageMaker Ground Truth labeling job that created the transform or training job.

', 'DescribeTransformJobResponse$LabelingJobArn' => '

The Amazon Resource Name (ARN) of the Amazon SageMaker Ground Truth labeling job that created the transform or training job.

', 'LabelingJobSummary$LabelingJobArn' => '

The Amazon Resource Name (ARN) assigned to the labeling job when it was created.

', 'TrainingJob$LabelingJobArn' => '

The Amazon Resource Name (ARN) of the labeling job.

', ], ], 'LabelingJobDataAttributes' => [ 'base' => '

Attributes of the data specified by the customer. Use these to describe the data to be labeled.

', 'refs' => [ 'LabelingJobInputConfig$DataAttributes' => '

Attributes of the data specified by the customer.

', ], ], 'LabelingJobDataSource' => [ 'base' => '

Provides information about the location of input data.

', 'refs' => [ 'LabelingJobInputConfig$DataSource' => '

The location of the input data.

', ], ], 'LabelingJobForWorkteamSummary' => [ 'base' => '

Provides summary information for a work team.

', 'refs' => [ 'LabelingJobForWorkteamSummaryList$member' => NULL, ], ], 'LabelingJobForWorkteamSummaryList' => [ 'base' => NULL, 'refs' => [ 'ListLabelingJobsForWorkteamResponse$LabelingJobSummaryList' => '

An array of LabelingJobSummary objects, each describing a labeling job.

', ], ], 'LabelingJobInputConfig' => [ 'base' => '

Input configuration information for a labeling job.

', 'refs' => [ 'CreateLabelingJobRequest$InputConfig' => '

Input data for the labeling job, such as the Amazon S3 location of the data objects and the location of the manifest file that describes the data objects.

', 'DescribeLabelingJobResponse$InputConfig' => '

Input configuration information for the labeling job, such as the Amazon S3 location of the data objects and the location of the manifest file that describes the data objects.

', 'LabelingJobSummary$InputConfig' => '

Input configuration for the labeling job.

', ], ], 'LabelingJobName' => [ 'base' => NULL, 'refs' => [ 'CreateLabelingJobRequest$LabelingJobName' => '

The name of the labeling job. This name is used to identify the job in a list of labeling jobs.

', 'DescribeLabelingJobRequest$LabelingJobName' => '

The name of the labeling job to return information for.

', 'DescribeLabelingJobResponse$LabelingJobName' => '

The name assigned to the labeling job when it was created.

', 'LabelingJobForWorkteamSummary$LabelingJobName' => '

The name of the labeling job that the work team is assigned to.

', 'LabelingJobSummary$LabelingJobName' => '

The name of the labeling job.

', 'StopLabelingJobRequest$LabelingJobName' => '

The name of the labeling job to stop.

', ], ], 'LabelingJobOutput' => [ 'base' => '

Specifies the location of the output produced by the labeling job.

', 'refs' => [ 'DescribeLabelingJobResponse$LabelingJobOutput' => '

The location of the output produced by the labeling job.

', 'LabelingJobSummary$LabelingJobOutput' => '

The location of the output produced by the labeling job.

', ], ], 'LabelingJobOutputConfig' => [ 'base' => '

Output configuration information for a labeling job.

', 'refs' => [ 'CreateLabelingJobRequest$OutputConfig' => '

The location of the output data and the AWS Key Management Service key ID for the key used to encrypt the output data, if any.

', 'DescribeLabelingJobResponse$OutputConfig' => '

The location of the job\'s output data and the AWS Key Management Service key ID for the key used to encrypt the output data, if any.

', ], ], 'LabelingJobResourceConfig' => [ 'base' => '

Provides configuration information for labeling jobs.

', 'refs' => [ 'LabelingJobAlgorithmsConfig$LabelingJobResourceConfig' => '

Provides configuration information for a labeling job.

', ], ], 'LabelingJobS3DataSource' => [ 'base' => '

The Amazon S3 location of the input data objects.

', 'refs' => [ 'LabelingJobDataSource$S3DataSource' => '

The Amazon S3 location of the input data objects.

', ], ], 'LabelingJobStatus' => [ 'base' => NULL, 'refs' => [ 'DescribeLabelingJobResponse$LabelingJobStatus' => '

The processing status of the labeling job.

', 'LabelingJobSummary$LabelingJobStatus' => '

The current status of the labeling job.

', 'ListLabelingJobsRequest$StatusEquals' => '

A filter that retrieves only labeling jobs with a specific status.

', ], ], 'LabelingJobStoppingConditions' => [ 'base' => '

A set of conditions for stopping a labeling job. If any of the conditions are met, the job is automatically stopped. You can use these conditions to control the cost of data labeling.

', 'refs' => [ 'CreateLabelingJobRequest$StoppingConditions' => '

A set of conditions for stopping the labeling job. If any of the conditions are met, the job is automatically stopped. You can use these conditions to control the cost of data labeling.

', 'DescribeLabelingJobResponse$StoppingConditions' => '

A set of conditions for stopping a labeling job. If any of the conditions are met, the job is automatically stopped.

', ], ], 'LabelingJobSummary' => [ 'base' => '

Provides summary information about a labeling job.

', 'refs' => [ 'LabelingJobSummaryList$member' => NULL, ], ], 'LabelingJobSummaryList' => [ 'base' => NULL, 'refs' => [ 'ListLabelingJobsResponse$LabelingJobSummaryList' => '

An array of LabelingJobSummary objects, each describing a labeling job.

', ], ], 'LambdaFunctionArn' => [ 'base' => NULL, 'refs' => [ 'AnnotationConsolidationConfig$AnnotationConsolidationLambdaArn' => '

The Amazon Resource Name (ARN) of a Lambda function implements the logic for annotation consolidation.

For the built-in bounding box, image classification, semantic segmentation, and text classification task types, Amazon SageMaker Ground Truth provides the following Lambda functions:

  • Bounding box - Finds the most similar boxes from different workers based on the Jaccard index of the boxes.

    arn:aws:lambda:us-east-1:432418664414:function:ACS-BoundingBox

    arn:aws:lambda:us-east-2:266458841044:function:ACS-BoundingBox

    arn:aws:lambda:us-west-2:081040173940:function:ACS-BoundingBox

    arn:aws:lambda:eu-west-1:568282634449:function:ACS-BoundingBox

    arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-BoundingBox

    arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-BoundingBox

  • Image classification - Uses a variant of the Expectation Maximization approach to estimate the true class of an image based on annotations from individual workers.

    arn:aws:lambda:us-east-1:432418664414:function:ACS-ImageMultiClass

    arn:aws:lambda:us-east-2:266458841044:function:ACS-ImageMultiClass

    arn:aws:lambda:us-west-2:081040173940:function:ACS-ImageMultiClass

    arn:aws:lambda:eu-west-1:568282634449:function:ACS-ImageMultiClass

    arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-ImageMultiClass

    arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-ImageMultiClass

  • Semantic segmentation - Treats each pixel in an image as a multi-class classification and treats pixel annotations from workers as "votes" for the correct label.

    arn:aws:lambda:us-east-1:432418664414:function:ACS-SemanticSegmentation

    arn:aws:lambda:us-east-2:266458841044:function:ACS-SemanticSegmentation

    arn:aws:lambda:us-west-2:081040173940:function:ACS-SemanticSegmentation

    arn:aws:lambda:eu-west-1:568282634449:function:ACS-SemanticSegmentation

    arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-SemanticSegmentation

    arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-SemanticSegmentation

  • Text classification - Uses a variant of the Expectation Maximization approach to estimate the true class of text based on annotations from individual workers.

    arn:aws:lambda:us-east-1:432418664414:function:ACS-TextMultiClass

    arn:aws:lambda:us-east-2:266458841044:function:ACS-TextMultiClass

    arn:aws:lambda:us-west-2:081040173940:function:ACS-TextMultiClass

    arn:aws:lambda:eu-west-1:568282634449:function:ACS-TextMultiClass

    arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-TextMultiClass

    arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-TextMultiClass

For more information, see Annotation Consolidation.

', 'HumanTaskConfig$PreHumanTaskLambdaArn' => '

The Amazon Resource Name (ARN) of a Lambda function that is run before a data object is sent to a human worker. Use this function to provide input to a custom labeling job.

For the built-in bounding box, image classification, semantic segmentation, and text classification task types, Amazon SageMaker Ground Truth provides the following Lambda functions:

US East (Northern Virginia) (us-east-1):

  • arn:aws:lambda:us-east-1:432418664414:function:PRE-BoundingBox

  • arn:aws:lambda:us-east-1:432418664414:function:PRE-ImageMultiClass

  • arn:aws:lambda:us-east-1:432418664414:function:PRE-SemanticSegmentation

  • arn:aws:lambda:us-east-1:432418664414:function:PRE-TextMultiClass

US East (Ohio) (us-east-2):

  • arn:aws:lambda:us-east-2:266458841044:function:PRE-BoundingBox

  • arn:aws:lambda:us-east-2:266458841044:function:PRE-ImageMultiClass

  • arn:aws:lambda:us-east-2:266458841044:function:PRE-SemanticSegmentation

  • arn:aws:lambda:us-east-2:266458841044:function:PRE-TextMultiClass

US West (Oregon) (us-west-2):

  • arn:aws:lambda:us-west-2:081040173940:function:PRE-BoundingBox

  • arn:aws:lambda:us-west-2:081040173940:function:PRE-ImageMultiClass

  • arn:aws:lambda:us-west-2:081040173940:function:PRE-SemanticSegmentation

  • arn:aws:lambda:us-west-2:081040173940:function:PRE-TextMultiClass

EU (Ireland) (eu-west-1):

  • arn:aws:lambda:eu-west-1:568282634449:function:PRE-BoundingBox

  • arn:aws:lambda:eu-west-1:568282634449:function:PRE-ImageMultiClass

  • arn:aws:lambda:eu-west-1:568282634449:function:PRE-SemanticSegmentation

  • arn:aws:lambda:eu-west-1:568282634449:function:PRE-TextMultiClass

Asia Pacific (Tokyo (ap-northeast-1):

  • arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-BoundingBox

  • arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-ImageMultiClass

  • arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-SemanticSegmentation

  • arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-TextMultiClass

Asia Pacific (Sydney (ap-southeast-1):

  • arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-BoundingBox

  • arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-ImageMultiClass

  • arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-SemanticSegmentation

  • arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-TextMultiClass

', 'LabelingJobSummary$PreHumanTaskLambdaArn' => '

The Amazon Resource Name (ARN) of a Lambda function. The function is run before each data object is sent to a worker.

', 'LabelingJobSummary$AnnotationConsolidationLambdaArn' => '

The Amazon Resource Name (ARN) of the Lambda function used to consolidate the annotations from individual workers into a label for a data object. For more information, see Annotation Consolidation.

', ], ], 'LastModifiedTime' => [ 'base' => NULL, 'refs' => [ 'CodeRepositorySummary$LastModifiedTime' => '

The date and time that the Git repository was last modified.

', 'CompilationJobSummary$LastModifiedTime' => '

The time when the model compilation job was last modified.

', 'DescribeCodeRepositoryOutput$LastModifiedTime' => '

The date and time that the repository was last changed.

', 'DescribeCompilationJobResponse$LastModifiedTime' => '

The time that the status of the model compilation job was last modified.

', 'DescribeNotebookInstanceLifecycleConfigOutput$LastModifiedTime' => '

A timestamp that tells when the lifecycle configuration was last modified.

', 'DescribeNotebookInstanceOutput$LastModifiedTime' => '

A timestamp. Use this parameter to retrieve the time when the notebook instance was last modified.

', 'ListCompilationJobsRequest$LastModifiedTimeAfter' => '

A filter that returns the model compilation jobs that were modified after a specified time.

', 'ListCompilationJobsRequest$LastModifiedTimeBefore' => '

A filter that returns the model compilation jobs that were modified before a specified time.

', 'ListNotebookInstanceLifecycleConfigsInput$LastModifiedTimeBefore' => '

A filter that returns only lifecycle configurations that were modified before the specified time (timestamp).

', 'ListNotebookInstanceLifecycleConfigsInput$LastModifiedTimeAfter' => '

A filter that returns only lifecycle configurations that were modified after the specified time (timestamp).

', 'ListNotebookInstancesInput$LastModifiedTimeBefore' => '

A filter that returns only notebook instances that were modified before the specified time (timestamp).

', 'ListNotebookInstancesInput$LastModifiedTimeAfter' => '

A filter that returns only notebook instances that were modified after the specified time (timestamp).

', 'NotebookInstanceLifecycleConfigSummary$LastModifiedTime' => '

A timestamp that tells when the lifecycle configuration was last modified.

', 'NotebookInstanceSummary$LastModifiedTime' => '

A timestamp that shows when the notebook instance was last modified.

', ], ], 'ListAlgorithmsInput' => [ 'base' => NULL, 'refs' => [], ], 'ListAlgorithmsOutput' => [ 'base' => NULL, 'refs' => [], ], 'ListCodeRepositoriesInput' => [ 'base' => NULL, 'refs' => [], ], 'ListCodeRepositoriesOutput' => [ 'base' => NULL, 'refs' => [], ], 'ListCompilationJobsRequest' => [ 'base' => NULL, 'refs' => [], ], 'ListCompilationJobsResponse' => [ 'base' => NULL, 'refs' => [], ], 'ListCompilationJobsSortBy' => [ 'base' => NULL, 'refs' => [ 'ListCompilationJobsRequest$SortBy' => '

The field by which to sort results. The default is CreationTime.

', ], ], 'ListEndpointConfigsInput' => [ 'base' => NULL, 'refs' => [], ], 'ListEndpointConfigsOutput' => [ 'base' => NULL, 'refs' => [], ], 'ListEndpointsInput' => [ 'base' => NULL, 'refs' => [], ], 'ListEndpointsOutput' => [ 'base' => NULL, 'refs' => [], ], 'ListHyperParameterTuningJobsRequest' => [ 'base' => NULL, 'refs' => [], ], 'ListHyperParameterTuningJobsResponse' => [ 'base' => NULL, 'refs' => [], ], 'ListLabelingJobsForWorkteamRequest' => [ 'base' => NULL, 'refs' => [], ], 'ListLabelingJobsForWorkteamResponse' => [ 'base' => NULL, 'refs' => [], ], 'ListLabelingJobsForWorkteamSortByOptions' => [ 'base' => NULL, 'refs' => [ 'ListLabelingJobsForWorkteamRequest$SortBy' => '

The field to sort results by. The default is CreationTime.

', ], ], 'ListLabelingJobsRequest' => [ 'base' => NULL, 'refs' => [], ], 'ListLabelingJobsResponse' => [ 'base' => NULL, 'refs' => [], ], 'ListModelPackagesInput' => [ 'base' => NULL, 'refs' => [], ], 'ListModelPackagesOutput' => [ 'base' => NULL, 'refs' => [], ], 'ListModelsInput' => [ 'base' => NULL, 'refs' => [], ], 'ListModelsOutput' => [ 'base' => NULL, 'refs' => [], ], 'ListNotebookInstanceLifecycleConfigsInput' => [ 'base' => NULL, 'refs' => [], ], 'ListNotebookInstanceLifecycleConfigsOutput' => [ 'base' => NULL, 'refs' => [], ], 'ListNotebookInstancesInput' => [ 'base' => NULL, 'refs' => [], ], 'ListNotebookInstancesOutput' => [ 'base' => NULL, 'refs' => [], ], 'ListSubscribedWorkteamsRequest' => [ 'base' => NULL, 'refs' => [], ], 'ListSubscribedWorkteamsResponse' => [ 'base' => NULL, 'refs' => [], ], 'ListTagsInput' => [ 'base' => NULL, 'refs' => [], ], 'ListTagsMaxResults' => [ 'base' => NULL, 'refs' => [ 'ListTagsInput$MaxResults' => '

Maximum number of tags to return.

', ], ], 'ListTagsOutput' => [ 'base' => NULL, 'refs' => [], ], 'ListTrainingJobsForHyperParameterTuningJobRequest' => [ 'base' => NULL, 'refs' => [], ], 'ListTrainingJobsForHyperParameterTuningJobResponse' => [ 'base' => NULL, 'refs' => [], ], 'ListTrainingJobsRequest' => [ 'base' => NULL, 'refs' => [], ], 'ListTrainingJobsResponse' => [ 'base' => NULL, 'refs' => [], ], 'ListTransformJobsRequest' => [ 'base' => NULL, 'refs' => [], ], 'ListTransformJobsResponse' => [ 'base' => NULL, 'refs' => [], ], 'ListWorkteamsRequest' => [ 'base' => NULL, 'refs' => [], ], 'ListWorkteamsResponse' => [ 'base' => NULL, 'refs' => [], ], 'ListWorkteamsSortByOptions' => [ 'base' => NULL, 'refs' => [ 'ListWorkteamsRequest$SortBy' => '

The field to sort results by. The default is CreationTime.

', ], ], 'MaxConcurrentTaskCount' => [ 'base' => NULL, 'refs' => [ 'HumanTaskConfig$MaxConcurrentTaskCount' => '

Defines the maximum number of data objects that can be labeled by human workers at the same time. Each object may have more than one worker at one time.

', ], ], 'MaxConcurrentTransforms' => [ 'base' => NULL, 'refs' => [ 'CreateTransformJobRequest$MaxConcurrentTransforms' => '

The maximum number of parallel requests that can be sent to each instance in a transform job. If MaxConcurrentTransforms is set to 0 or left unset, Amazon SageMaker checks the optional execution-parameters to determine the optimal settings for your chosen algorithm. If the execution-parameters endpoint is not enabled, the default value is 1. For more information on execution-parameters, see How Containers Serve Requests. For built-in algorithms, you don\'t need to set a value for MaxConcurrentTransforms.

', 'DescribeTransformJobResponse$MaxConcurrentTransforms' => '

The maximum number of parallel requests on each instance node that can be launched in a transform job. The default value is 1.

', 'TransformJobDefinition$MaxConcurrentTransforms' => '

The maximum number of parallel requests that can be sent to each instance in a transform job. The default value is 1.

', ], ], 'MaxHumanLabeledObjectCount' => [ 'base' => NULL, 'refs' => [ 'LabelingJobStoppingConditions$MaxHumanLabeledObjectCount' => '

The maximum number of objects that can be labeled by human workers.

', ], ], 'MaxNumberOfTrainingJobs' => [ 'base' => NULL, 'refs' => [ 'ResourceLimits$MaxNumberOfTrainingJobs' => '

The maximum number of training jobs that a hyperparameter tuning job can launch.

', ], ], 'MaxParallelTrainingJobs' => [ 'base' => NULL, 'refs' => [ 'ResourceLimits$MaxParallelTrainingJobs' => '

The maximum number of concurrent training jobs that a hyperparameter tuning job can launch.

', ], ], 'MaxPayloadInMB' => [ 'base' => NULL, 'refs' => [ 'CreateTransformJobRequest$MaxPayloadInMB' => '

The maximum allowed size of the payload, in MB. A payload is the data portion of a record (without metadata). The value in MaxPayloadInMB must be greater than, or equal to, the size of a single record. To estimate the size of a record in MB, divide the size of your dataset by the number of records. To ensure that the records fit within the maximum payload size, we recommend using a slightly larger value. The default value is 6 MB.

For cases where the payload might be arbitrarily large and is transmitted using HTTP chunked encoding, set the value to 0. This feature works only in supported algorithms. Currently, Amazon SageMaker built-in algorithms do not support HTTP chunked encoding.

', 'DescribeTransformJobResponse$MaxPayloadInMB' => '

The maximum payload size, in MB, used in the transform job.

', 'TransformJobDefinition$MaxPayloadInMB' => '

The maximum payload size allowed, in MB. A payload is the data portion of a record (without metadata).

', ], ], 'MaxPercentageOfInputDatasetLabeled' => [ 'base' => NULL, 'refs' => [ 'LabelingJobStoppingConditions$MaxPercentageOfInputDatasetLabeled' => '

The maximum number of input data objects that should be labeled.

', ], ], 'MaxResults' => [ 'base' => NULL, 'refs' => [ 'ListAlgorithmsInput$MaxResults' => '

The maximum number of algorithms to return in the response.

', 'ListCodeRepositoriesInput$MaxResults' => '

The maximum number of Git repositories to return in the response.

', 'ListCompilationJobsRequest$MaxResults' => '

The maximum number of model compilation jobs to return in the response.

', 'ListEndpointConfigsInput$MaxResults' => '

The maximum number of training jobs to return in the response.

', 'ListEndpointsInput$MaxResults' => '

The maximum number of endpoints to return in the response.

', 'ListHyperParameterTuningJobsRequest$MaxResults' => '

The maximum number of tuning jobs to return. The default value is 10.

', 'ListLabelingJobsForWorkteamRequest$MaxResults' => '

The maximum number of labeling jobs to return in each page of the response.

', 'ListLabelingJobsRequest$MaxResults' => '

The maximum number of labeling jobs to return in each page of the response.

', 'ListModelPackagesInput$MaxResults' => '

The maximum number of model packages to return in the response.

', 'ListModelsInput$MaxResults' => '

The maximum number of models to return in the response.

', 'ListNotebookInstanceLifecycleConfigsInput$MaxResults' => '

The maximum number of lifecycle configurations to return in the response.

', 'ListNotebookInstancesInput$MaxResults' => '

The maximum number of notebook instances to return.

', 'ListSubscribedWorkteamsRequest$MaxResults' => '

The maximum number of work teams to return in each page of the response.

', 'ListTrainingJobsForHyperParameterTuningJobRequest$MaxResults' => '

The maximum number of training jobs to return. The default value is 10.

', 'ListTrainingJobsRequest$MaxResults' => '

The maximum number of training jobs to return in the response.

', 'ListTransformJobsRequest$MaxResults' => '

The maximum number of transform jobs to return in the response. The default value is 10.

', 'ListWorkteamsRequest$MaxResults' => '

The maximum number of work teams to return in each page of the response.

', 'SearchRequest$MaxResults' => '

The maximum number of results to return in a SearchResponse.

', ], ], 'MaxRuntimeInSeconds' => [ 'base' => NULL, 'refs' => [ 'StoppingCondition$MaxRuntimeInSeconds' => '

The maximum length of time, in seconds, that the training job can run. If model training does not complete during this time, Amazon SageMaker ends the job. If value is not specified, default value is 1 day. Maximum value is 28 days.

', ], ], 'MemberDefinition' => [ 'base' => '

Defines the Amazon Cognito user group that is part of a work team.

', 'refs' => [ 'MemberDefinitions$member' => NULL, ], ], 'MemberDefinitions' => [ 'base' => NULL, 'refs' => [ 'CreateWorkteamRequest$MemberDefinitions' => '

A list of MemberDefinition objects that contains objects that identify the Amazon Cognito user pool that makes up the work team. For more information, see Amazon Cognito User Pools.

All of the CognitoMemberDefinition objects that make up the member definition must have the same ClientId and UserPool values.

', 'UpdateWorkteamRequest$MemberDefinitions' => '

A list of MemberDefinition objects that contain the updated work team members.

', 'Workteam$MemberDefinitions' => '

The Amazon Cognito user groups that make up the work team.

', ], ], 'MetricData' => [ 'base' => '

The name, value, and date and time of a metric that was emitted to Amazon CloudWatch.

', 'refs' => [ 'FinalMetricDataList$member' => NULL, ], ], 'MetricDefinition' => [ 'base' => '

Specifies a metric that the training algorithm writes to stderr or stdout. Amazon SageMakerhyperparameter tuning captures all defined metrics. You specify one metric that a hyperparameter tuning job uses as its objective metric to choose the best training job.

', 'refs' => [ 'MetricDefinitionList$member' => NULL, ], ], 'MetricDefinitionList' => [ 'base' => NULL, 'refs' => [ 'AlgorithmSpecification$MetricDefinitions' => '

A list of metric definition objects. Each object specifies the metric name and regular expressions used to parse algorithm logs. Amazon SageMaker publishes each metric to Amazon CloudWatch.

', 'HyperParameterAlgorithmSpecification$MetricDefinitions' => '

An array of MetricDefinition objects that specify the metrics that the algorithm emits.

', 'TrainingSpecification$MetricDefinitions' => '

A list of MetricDefinition objects, which are used for parsing metrics generated by the algorithm.

', ], ], 'MetricName' => [ 'base' => NULL, 'refs' => [ 'FinalHyperParameterTuningJobObjectiveMetric$MetricName' => '

The name of the objective metric.

', 'HyperParameterTuningJobObjective$MetricName' => '

The name of the metric to use for the objective metric.

', 'MetricData$MetricName' => '

The name of the metric.

', 'MetricDefinition$Name' => '

The name of the metric.

', ], ], 'MetricRegex' => [ 'base' => NULL, 'refs' => [ 'MetricDefinition$Regex' => '

A regular expression that searches the output of a training job and gets the value of the metric. For more information about using regular expressions to define metrics, see Defining Objective Metrics.

', ], ], 'MetricValue' => [ 'base' => NULL, 'refs' => [ 'FinalHyperParameterTuningJobObjectiveMetric$Value' => '

The value of the objective metric.

', ], ], 'ModelArn' => [ 'base' => NULL, 'refs' => [ 'CreateModelOutput$ModelArn' => '

The ARN of the model created in Amazon SageMaker.

', 'DescribeModelOutput$ModelArn' => '

The Amazon Resource Name (ARN) of the model.

', 'LabelingJobAlgorithmsConfig$InitialActiveLearningModelArn' => '

At the end of an auto-label job Amazon SageMaker Ground Truth sends the Amazon Resource Nam (ARN) of the final model used for auto-labeling. You can use this model as the starting point for subsequent similar jobs by providing the ARN of the model here.

', 'LabelingJobOutput$FinalActiveLearningModelArn' => '

The Amazon Resource Name (ARN) for the most recent Amazon SageMaker model trained as part of automated data labeling.

', 'ModelSummary$ModelArn' => '

The Amazon Resource Name (ARN) of the model.

', ], ], 'ModelArtifacts' => [ 'base' => '

Provides information about the location that is configured for storing model artifacts.

', 'refs' => [ 'DescribeCompilationJobResponse$ModelArtifacts' => '

Information about the location in Amazon S3 that has been configured for storing the model artifacts used in the compilation job.

', 'DescribeTrainingJobResponse$ModelArtifacts' => '

Information about the Amazon S3 location that is configured for storing model artifacts.

', 'TrainingJob$ModelArtifacts' => '

Information about the Amazon S3 location that is configured for storing model artifacts.

', ], ], 'ModelName' => [ 'base' => NULL, 'refs' => [ 'CreateModelInput$ModelName' => '

The name of the new model.

', 'CreateTransformJobRequest$ModelName' => '

The name of the model that you want to use for the transform job. ModelName must be the name of an existing Amazon SageMaker model within an AWS Region in an AWS account.

', 'DeleteModelInput$ModelName' => '

The name of the model to delete.

', 'DescribeModelInput$ModelName' => '

The name of the model.

', 'DescribeModelOutput$ModelName' => '

Name of the Amazon SageMaker model.

', 'DescribeTransformJobResponse$ModelName' => '

The name of the model used in the transform job.

', 'ModelSummary$ModelName' => '

The name of the model that you want a summary for.

', 'ProductionVariant$ModelName' => '

The name of the model that you want to host. This is the name that you specified when creating the model.

', ], ], 'ModelNameContains' => [ 'base' => NULL, 'refs' => [ 'ListModelsInput$NameContains' => '

A string in the training job name. This filter returns only models in the training job whose name contains the specified string.

', ], ], 'ModelPackageArn' => [ 'base' => NULL, 'refs' => [ 'CreateModelPackageOutput$ModelPackageArn' => '

The Amazon Resource Name (ARN) of the new model package.

', 'DescribeModelPackageOutput$ModelPackageArn' => '

The Amazon Resource Name (ARN) of the model package.

', 'ModelPackageSummary$ModelPackageArn' => '

The Amazon Resource Name (ARN) of the model package.

', ], ], 'ModelPackageContainerDefinition' => [ 'base' => '

Describes the Docker container for the model package.

', 'refs' => [ 'ModelPackageContainerDefinitionList$member' => NULL, ], ], 'ModelPackageContainerDefinitionList' => [ 'base' => NULL, 'refs' => [ 'InferenceSpecification$Containers' => '

The Amazon ECR registry path of the Docker image that contains the inference code.

', ], ], 'ModelPackageSortBy' => [ 'base' => NULL, 'refs' => [ 'ListModelPackagesInput$SortBy' => '

The parameter by which to sort the results. The default is CreationTime.

', ], ], 'ModelPackageStatus' => [ 'base' => NULL, 'refs' => [ 'DescribeModelPackageOutput$ModelPackageStatus' => '

The current status of the model package.

', 'ModelPackageSummary$ModelPackageStatus' => '

The overall status of the model package.

', ], ], 'ModelPackageStatusDetails' => [ 'base' => '

Specifies the validation and image scan statuses of the model package.

', 'refs' => [ 'DescribeModelPackageOutput$ModelPackageStatusDetails' => '

Details about the current status of the model package.

', ], ], 'ModelPackageStatusItem' => [ 'base' => '

Represents the overall status of a model package.

', 'refs' => [ 'ModelPackageStatusItemList$member' => NULL, ], ], 'ModelPackageStatusItemList' => [ 'base' => NULL, 'refs' => [ 'ModelPackageStatusDetails$ValidationStatuses' => '

The validation status of the model package.

', 'ModelPackageStatusDetails$ImageScanStatuses' => '

The status of the scan of the Docker image container for the model package.

', ], ], 'ModelPackageSummary' => [ 'base' => '

Provides summary information about a model package.

', 'refs' => [ 'ModelPackageSummaryList$member' => NULL, ], ], 'ModelPackageSummaryList' => [ 'base' => NULL, 'refs' => [ 'ListModelPackagesOutput$ModelPackageSummaryList' => '

An array of ModelPackageSummary objects, each of which lists a model package.

', ], ], 'ModelPackageValidationProfile' => [ 'base' => '

Contains data, such as the inputs and targeted instance types that are used in the process of validating the model package.

The data provided in the validation profile is made available to your buyers on AWS Marketplace.

', 'refs' => [ 'ModelPackageValidationProfiles$member' => NULL, ], ], 'ModelPackageValidationProfiles' => [ 'base' => NULL, 'refs' => [ 'ModelPackageValidationSpecification$ValidationProfiles' => '

An array of ModelPackageValidationProfile objects, each of which specifies a batch transform job that Amazon SageMaker runs to validate your model package.

', ], ], 'ModelPackageValidationSpecification' => [ 'base' => '

Specifies batch transform jobs that Amazon SageMaker runs to validate your model package.

', 'refs' => [ 'CreateModelPackageInput$ValidationSpecification' => '

Specifies configurations for one or more transform jobs that Amazon SageMaker runs to test the model package.

', 'DescribeModelPackageOutput$ValidationSpecification' => '

Configurations for one or more transform jobs that Amazon SageMaker runs to test the model package.

', ], ], 'ModelSortKey' => [ 'base' => NULL, 'refs' => [ 'ListModelsInput$SortBy' => '

Sorts the list of results. The default is CreationTime.

', ], ], 'ModelSummary' => [ 'base' => '

Provides summary information about a model.

', 'refs' => [ 'ModelSummaryList$member' => NULL, ], ], 'ModelSummaryList' => [ 'base' => NULL, 'refs' => [ 'ListModelsOutput$Models' => '

An array of ModelSummary objects, each of which lists a model.

', ], ], 'NameContains' => [ 'base' => NULL, 'refs' => [ 'ListAlgorithmsInput$NameContains' => '

A string in the algorithm name. This filter returns only algorithms whose name contains the specified string.

', 'ListCompilationJobsRequest$NameContains' => '

A filter that returns the model compilation jobs whose name contains a specified string.

', 'ListHyperParameterTuningJobsRequest$NameContains' => '

A string in the tuning job name. This filter returns only tuning jobs whose name contains the specified string.

', 'ListLabelingJobsRequest$NameContains' => '

A string in the labeling job name. This filter returns only labeling jobs whose name contains the specified string.

', 'ListModelPackagesInput$NameContains' => '

A string in the model package name. This filter returns only model packages whose name contains the specified string.

', 'ListTrainingJobsRequest$NameContains' => '

A string in the training job name. This filter returns only training jobs whose name contains the specified string.

', 'ListTransformJobsRequest$NameContains' => '

A string in the transform job name. This filter returns only transform jobs whose name contains the specified string.

', ], ], 'NestedFilters' => [ 'base' => '

Defines a list of NestedFilters objects. To satisfy the conditions specified in the NestedFilters call, a resource must satisfy the conditions of all of the filters.

For example, you could define a NestedFilters using the training job\'s InputDataConfig property to filter on Channel objects.

A NestedFilters object contains multiple filters. For example, to find all training jobs whose name contains train and that have cat/data in their S3Uri (specified in InputDataConfig), you need to create a NestedFilters object that specifies the InputDataConfig property with the following Filter objects:

  • \'{Name:"InputDataConfig.ChannelName", "Operator":"EQUALS", "Value":"train"}\',

  • \'{Name:"InputDataConfig.DataSource.S3DataSource.S3Uri", "Operator":"CONTAINS", "Value":"cat/data"}\'

', 'refs' => [ 'NestedFiltersList$member' => NULL, ], ], 'NestedFiltersList' => [ 'base' => NULL, 'refs' => [ 'SearchExpression$NestedFilters' => '

A list of nested filter objects.

', ], ], 'NetworkInterfaceId' => [ 'base' => NULL, 'refs' => [ 'DescribeNotebookInstanceOutput$NetworkInterfaceId' => '

The network interface IDs that Amazon SageMaker created at the time of creating the instance.

', ], ], 'NextToken' => [ 'base' => NULL, 'refs' => [ 'ListAlgorithmsInput$NextToken' => '

If the response to a previous ListAlgorithms request was truncated, the response includes a NextToken. To retrieve the next set of algorithms, use the token in the next request.

', 'ListAlgorithmsOutput$NextToken' => '

If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of algorithms, use it in the subsequent request.

', 'ListCodeRepositoriesInput$NextToken' => '

If the result of a ListCodeRepositoriesOutput request was truncated, the response includes a NextToken. To get the next set of Git repositories, use the token in the next request.

', 'ListCodeRepositoriesOutput$NextToken' => '

If the result of a ListCodeRepositoriesOutput request was truncated, the response includes a NextToken. To get the next set of Git repositories, use the token in the next request.

', 'ListCompilationJobsRequest$NextToken' => '

If the result of the previous ListCompilationJobs request was truncated, the response includes a NextToken. To retrieve the next set of model compilation jobs, use the token in the next request.

', 'ListCompilationJobsResponse$NextToken' => '

If the response is truncated, Amazon SageMaker returns this NextToken. To retrieve the next set of model compilation jobs, use this token in the next request.

', 'ListHyperParameterTuningJobsRequest$NextToken' => '

If the result of the previous ListHyperParameterTuningJobs request was truncated, the response includes a NextToken. To retrieve the next set of tuning jobs, use the token in the next request.

', 'ListHyperParameterTuningJobsResponse$NextToken' => '

If the result of this ListHyperParameterTuningJobs request was truncated, the response includes a NextToken. To retrieve the next set of tuning jobs, use the token in the next request.

', 'ListLabelingJobsForWorkteamRequest$NextToken' => '

If the result of the previous ListLabelingJobsForWorkteam request was truncated, the response includes a NextToken. To retrieve the next set of labeling jobs, use the token in the next request.

', 'ListLabelingJobsForWorkteamResponse$NextToken' => '

If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of labeling jobs, use it in the subsequent request.

', 'ListLabelingJobsRequest$NextToken' => '

If the result of the previous ListLabelingJobs request was truncated, the response includes a NextToken. To retrieve the next set of labeling jobs, use the token in the next request.

', 'ListLabelingJobsResponse$NextToken' => '

If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of labeling jobs, use it in the subsequent request.

', 'ListModelPackagesInput$NextToken' => '

If the response to a previous ListModelPackages request was truncated, the response includes a NextToken. To retrieve the next set of model packages, use the token in the next request.

', 'ListModelPackagesOutput$NextToken' => '

If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of model packages, use it in the subsequent request.

', 'ListNotebookInstanceLifecycleConfigsInput$NextToken' => '

If the result of a ListNotebookInstanceLifecycleConfigs request was truncated, the response includes a NextToken. To get the next set of lifecycle configurations, use the token in the next request.

', 'ListNotebookInstanceLifecycleConfigsOutput$NextToken' => '

If the response is truncated, Amazon SageMaker returns this token. To get the next set of lifecycle configurations, use it in the next request.

', 'ListNotebookInstancesInput$NextToken' => '

If the previous call to the ListNotebookInstances is truncated, the response includes a NextToken. You can use this token in your subsequent ListNotebookInstances request to fetch the next set of notebook instances.

You might specify a filter or a sort order in your request. When response is truncated, you must use the same values for the filer and sort order in the next request.

', 'ListNotebookInstancesOutput$NextToken' => '

If the response to the previous ListNotebookInstances request was truncated, Amazon SageMaker returns this token. To retrieve the next set of notebook instances, use the token in the next request.

', 'ListSubscribedWorkteamsRequest$NextToken' => '

If the result of the previous ListSubscribedWorkteams request was truncated, the response includes a NextToken. To retrieve the next set of labeling jobs, use the token in the next request.

', 'ListSubscribedWorkteamsResponse$NextToken' => '

If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of work teams, use it in the subsequent request.

', 'ListTagsInput$NextToken' => '

If the response to the previous ListTags request is truncated, Amazon SageMaker returns this token. To retrieve the next set of tags, use it in the subsequent request.

', 'ListTagsOutput$NextToken' => '

If response is truncated, Amazon SageMaker includes a token in the response. You can use this token in your subsequent request to fetch next set of tokens.

', 'ListTrainingJobsForHyperParameterTuningJobRequest$NextToken' => '

If the result of the previous ListTrainingJobsForHyperParameterTuningJob request was truncated, the response includes a NextToken. To retrieve the next set of training jobs, use the token in the next request.

', 'ListTrainingJobsForHyperParameterTuningJobResponse$NextToken' => '

If the result of this ListTrainingJobsForHyperParameterTuningJob request was truncated, the response includes a NextToken. To retrieve the next set of training jobs, use the token in the next request.

', 'ListTrainingJobsRequest$NextToken' => '

If the result of the previous ListTrainingJobs request was truncated, the response includes a NextToken. To retrieve the next set of training jobs, use the token in the next request.

', 'ListTrainingJobsResponse$NextToken' => '

If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of training jobs, use it in the subsequent request.

', 'ListTransformJobsRequest$NextToken' => '

If the result of the previous ListTransformJobs request was truncated, the response includes a NextToken. To retrieve the next set of transform jobs, use the token in the next request.

', 'ListTransformJobsResponse$NextToken' => '

If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of transform jobs, use it in the next request.

', 'ListWorkteamsRequest$NextToken' => '

If the result of the previous ListWorkteams request was truncated, the response includes a NextToken. To retrieve the next set of labeling jobs, use the token in the next request.

', 'ListWorkteamsResponse$NextToken' => '

If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of work teams, use it in the subsequent request.

', 'SearchRequest$NextToken' => '

If more than MaxResults resource objects match the specified SearchExpression, the SearchResponse includes a NextToken. The NextToken can be passed to the next SearchRequest to continue retrieving results for the specified SearchExpression and Sort parameters.

', 'SearchResponse$NextToken' => '

If the result of the previous Search request was truncated, the response includes a NextToken. To retrieve the next set of results, use the token in the next request.

', ], ], 'NotebookInstanceAcceleratorType' => [ 'base' => NULL, 'refs' => [ 'NotebookInstanceAcceleratorTypes$member' => NULL, ], ], 'NotebookInstanceAcceleratorTypes' => [ 'base' => NULL, 'refs' => [ 'CreateNotebookInstanceInput$AcceleratorTypes' => '

A list of Elastic Inference (EI) instance types to associate with this notebook instance. Currently, only one instance type can be associated with a notebook instance. For more information, see Using Elastic Inference in Amazon SageMaker.

', 'DescribeNotebookInstanceOutput$AcceleratorTypes' => '

A list of the Elastic Inference (EI) instance types associated with this notebook instance. Currently only one EI instance type can be associated with a notebook instance. For more information, see Using Elastic Inference in Amazon SageMaker.

', 'UpdateNotebookInstanceInput$AcceleratorTypes' => '

A list of the Elastic Inference (EI) instance types to associate with this notebook instance. Currently only one EI instance type can be associated with a notebook instance. For more information, see Using Elastic Inference in Amazon SageMaker.

', ], ], 'NotebookInstanceArn' => [ 'base' => NULL, 'refs' => [ 'CreateNotebookInstanceOutput$NotebookInstanceArn' => '

The Amazon Resource Name (ARN) of the notebook instance.

', 'DescribeNotebookInstanceOutput$NotebookInstanceArn' => '

The Amazon Resource Name (ARN) of the notebook instance.

', 'NotebookInstanceSummary$NotebookInstanceArn' => '

The Amazon Resource Name (ARN) of the notebook instance.

', ], ], 'NotebookInstanceLifecycleConfigArn' => [ 'base' => NULL, 'refs' => [ 'CreateNotebookInstanceLifecycleConfigOutput$NotebookInstanceLifecycleConfigArn' => '

The Amazon Resource Name (ARN) of the lifecycle configuration.

', 'DescribeNotebookInstanceLifecycleConfigOutput$NotebookInstanceLifecycleConfigArn' => '

The Amazon Resource Name (ARN) of the lifecycle configuration.

', 'NotebookInstanceLifecycleConfigSummary$NotebookInstanceLifecycleConfigArn' => '

The Amazon Resource Name (ARN) of the lifecycle configuration.

', ], ], 'NotebookInstanceLifecycleConfigContent' => [ 'base' => NULL, 'refs' => [ 'NotebookInstanceLifecycleHook$Content' => '

A base64-encoded string that contains a shell script for a notebook instance lifecycle configuration.

', ], ], 'NotebookInstanceLifecycleConfigList' => [ 'base' => NULL, 'refs' => [ 'CreateNotebookInstanceLifecycleConfigInput$OnCreate' => '

A shell script that runs only once, when you create a notebook instance. The shell script must be a base64-encoded string.

', 'CreateNotebookInstanceLifecycleConfigInput$OnStart' => '

A shell script that runs every time you start a notebook instance, including when you create the notebook instance. The shell script must be a base64-encoded string.

', 'DescribeNotebookInstanceLifecycleConfigOutput$OnCreate' => '

The shell script that runs only once, when you create a notebook instance.

', 'DescribeNotebookInstanceLifecycleConfigOutput$OnStart' => '

The shell script that runs every time you start a notebook instance, including when you create the notebook instance.

', 'UpdateNotebookInstanceLifecycleConfigInput$OnCreate' => '

The shell script that runs only once, when you create a notebook instance

', 'UpdateNotebookInstanceLifecycleConfigInput$OnStart' => '

The shell script that runs every time you start a notebook instance, including when you create the notebook instance.

', ], ], 'NotebookInstanceLifecycleConfigName' => [ 'base' => NULL, 'refs' => [ 'CreateNotebookInstanceInput$LifecycleConfigName' => '

The name of a lifecycle configuration to associate with the notebook instance. For information about lifestyle configurations, see Step 2.1: (Optional) Customize a Notebook Instance.

', 'CreateNotebookInstanceLifecycleConfigInput$NotebookInstanceLifecycleConfigName' => '

The name of the lifecycle configuration.

', 'DeleteNotebookInstanceLifecycleConfigInput$NotebookInstanceLifecycleConfigName' => '

The name of the lifecycle configuration to delete.

', 'DescribeNotebookInstanceLifecycleConfigInput$NotebookInstanceLifecycleConfigName' => '

The name of the lifecycle configuration to describe.

', 'DescribeNotebookInstanceLifecycleConfigOutput$NotebookInstanceLifecycleConfigName' => '

The name of the lifecycle configuration.

', 'DescribeNotebookInstanceOutput$NotebookInstanceLifecycleConfigName' => '

Returns the name of a notebook instance lifecycle configuration.

For information about notebook instance lifestyle configurations, see Step 2.1: (Optional) Customize a Notebook Instance

', 'ListNotebookInstancesInput$NotebookInstanceLifecycleConfigNameContains' => '

A string in the name of a notebook instances lifecycle configuration associated with this notebook instance. This filter returns only notebook instances associated with a lifecycle configuration with a name that contains the specified string.

', 'NotebookInstanceLifecycleConfigSummary$NotebookInstanceLifecycleConfigName' => '

The name of the lifecycle configuration.

', 'NotebookInstanceSummary$NotebookInstanceLifecycleConfigName' => '

The name of a notebook instance lifecycle configuration associated with this notebook instance.

For information about notebook instance lifestyle configurations, see Step 2.1: (Optional) Customize a Notebook Instance.

', 'UpdateNotebookInstanceInput$LifecycleConfigName' => '

The name of a lifecycle configuration to associate with the notebook instance. For information about lifestyle configurations, see Step 2.1: (Optional) Customize a Notebook Instance.

', 'UpdateNotebookInstanceLifecycleConfigInput$NotebookInstanceLifecycleConfigName' => '

The name of the lifecycle configuration.

', ], ], 'NotebookInstanceLifecycleConfigNameContains' => [ 'base' => NULL, 'refs' => [ 'ListNotebookInstanceLifecycleConfigsInput$NameContains' => '

A string in the lifecycle configuration name. This filter returns only lifecycle configurations whose name contains the specified string.

', ], ], 'NotebookInstanceLifecycleConfigSortKey' => [ 'base' => NULL, 'refs' => [ 'ListNotebookInstanceLifecycleConfigsInput$SortBy' => '

Sorts the list of results. The default is CreationTime.

', ], ], 'NotebookInstanceLifecycleConfigSortOrder' => [ 'base' => NULL, 'refs' => [ 'ListNotebookInstanceLifecycleConfigsInput$SortOrder' => '

The sort order for results.

', ], ], 'NotebookInstanceLifecycleConfigSummary' => [ 'base' => '

Provides a summary of a notebook instance lifecycle configuration.

', 'refs' => [ 'NotebookInstanceLifecycleConfigSummaryList$member' => NULL, ], ], 'NotebookInstanceLifecycleConfigSummaryList' => [ 'base' => NULL, 'refs' => [ 'ListNotebookInstanceLifecycleConfigsOutput$NotebookInstanceLifecycleConfigs' => '

An array of NotebookInstanceLifecycleConfiguration objects, each listing a lifecycle configuration.

', ], ], 'NotebookInstanceLifecycleHook' => [ 'base' => '

Contains the notebook instance lifecycle configuration script.

Each lifecycle configuration script has a limit of 16384 characters.

The value of the $PATH environment variable that is available to both scripts is /sbin:bin:/usr/sbin:/usr/bin.

View CloudWatch Logs for notebook instance lifecycle configurations in log group /aws/sagemaker/NotebookInstances in log stream [notebook-instance-name]/[LifecycleConfigHook].

Lifecycle configuration scripts cannot run for longer than 5 minutes. If a script runs for longer than 5 minutes, it fails and the notebook instance is not created or started.

For information about notebook instance lifestyle configurations, see Step 2.1: (Optional) Customize a Notebook Instance.

', 'refs' => [ 'NotebookInstanceLifecycleConfigList$member' => NULL, ], ], 'NotebookInstanceName' => [ 'base' => NULL, 'refs' => [ 'CreateNotebookInstanceInput$NotebookInstanceName' => '

The name of the new notebook instance.

', 'CreatePresignedNotebookInstanceUrlInput$NotebookInstanceName' => '

The name of the notebook instance.

', 'DeleteNotebookInstanceInput$NotebookInstanceName' => '

The name of the Amazon SageMaker notebook instance to delete.

', 'DescribeNotebookInstanceInput$NotebookInstanceName' => '

The name of the notebook instance that you want information about.

', 'DescribeNotebookInstanceOutput$NotebookInstanceName' => '

The name of the Amazon SageMaker notebook instance.

', 'NotebookInstanceSummary$NotebookInstanceName' => '

The name of the notebook instance that you want a summary for.

', 'StartNotebookInstanceInput$NotebookInstanceName' => '

The name of the notebook instance to start.

', 'StopNotebookInstanceInput$NotebookInstanceName' => '

The name of the notebook instance to terminate.

', 'UpdateNotebookInstanceInput$NotebookInstanceName' => '

The name of the notebook instance to update.

', ], ], 'NotebookInstanceNameContains' => [ 'base' => NULL, 'refs' => [ 'ListNotebookInstancesInput$NameContains' => '

A string in the notebook instances\' name. This filter returns only notebook instances whose name contains the specified string.

', ], ], 'NotebookInstanceSortKey' => [ 'base' => NULL, 'refs' => [ 'ListNotebookInstancesInput$SortBy' => '

The field to sort results by. The default is Name.

', ], ], 'NotebookInstanceSortOrder' => [ 'base' => NULL, 'refs' => [ 'ListNotebookInstancesInput$SortOrder' => '

The sort order for results.

', ], ], 'NotebookInstanceStatus' => [ 'base' => NULL, 'refs' => [ 'DescribeNotebookInstanceOutput$NotebookInstanceStatus' => '

The status of the notebook instance.

', 'ListNotebookInstancesInput$StatusEquals' => '

A filter that returns only notebook instances with the specified status.

', 'NotebookInstanceSummary$NotebookInstanceStatus' => '

The status of the notebook instance.

', ], ], 'NotebookInstanceSummary' => [ 'base' => '

Provides summary information for an Amazon SageMaker notebook instance.

', 'refs' => [ 'NotebookInstanceSummaryList$member' => NULL, ], ], 'NotebookInstanceSummaryList' => [ 'base' => NULL, 'refs' => [ 'ListNotebookInstancesOutput$NotebookInstances' => '

An array of NotebookInstanceSummary objects, one for each notebook instance.

', ], ], 'NotebookInstanceUrl' => [ 'base' => NULL, 'refs' => [ 'CreatePresignedNotebookInstanceUrlOutput$AuthorizedUrl' => '

A JSON object that contains the URL string.

', 'DescribeNotebookInstanceOutput$Url' => '

The URL that you use to connect to the Jupyter notebook that is running in your notebook instance.

', 'NotebookInstanceSummary$Url' => '

The URL that you use to connect to the Jupyter instance running in your notebook instance.

', ], ], 'NotebookInstanceVolumeSizeInGB' => [ 'base' => NULL, 'refs' => [ 'CreateNotebookInstanceInput$VolumeSizeInGB' => '

The size, in GB, of the ML storage volume to attach to the notebook instance. The default value is 5 GB.

', 'DescribeNotebookInstanceOutput$VolumeSizeInGB' => '

The size, in GB, of the ML storage volume attached to the notebook instance.

', 'UpdateNotebookInstanceInput$VolumeSizeInGB' => '

The size, in GB, of the ML storage volume to attach to the notebook instance. The default value is 5 GB.

', ], ], 'NotificationConfiguration' => [ 'base' => '

Configures SNS notifications of available or expiring work items for work teams.

', 'refs' => [ 'CreateWorkteamRequest$NotificationConfiguration' => '

Configures notification of workers regarding available or expiring work items.

', 'UpdateWorkteamRequest$NotificationConfiguration' => '

Configures SNS topic notifications for available or expiring work items

', 'Workteam$NotificationConfiguration' => NULL, ], ], 'NotificationTopicArn' => [ 'base' => NULL, 'refs' => [ 'NotificationConfiguration$NotificationTopicArn' => '

The ARN for the SNS topic to which notifications should be published.

', ], ], 'NumberOfHumanWorkersPerDataObject' => [ 'base' => NULL, 'refs' => [ 'HumanTaskConfig$NumberOfHumanWorkersPerDataObject' => '

The number of human workers that will label an object.

', 'LabelingJobForWorkteamSummary$NumberOfHumanWorkersPerDataObject' => '

The configured number of workers per data object.

', ], ], 'ObjectiveStatus' => [ 'base' => NULL, 'refs' => [ 'HyperParameterTrainingJobSummary$ObjectiveStatus' => '

The status of the objective metric for the training job:

  • Succeeded: The final objective metric for the training job was evaluated by the hyperparameter tuning job and used in the hyperparameter tuning process.

  • Pending: The training job is in progress and evaluation of its final objective metric is pending.

  • Failed: The final objective metric for the training job was not evaluated, and was not used in the hyperparameter tuning process. This typically occurs when the training job failed or did not emit an objective metric.

', ], ], 'ObjectiveStatusCounter' => [ 'base' => NULL, 'refs' => [ 'ObjectiveStatusCounters$Succeeded' => '

The number of training jobs whose final objective metric was evaluated by the hyperparameter tuning job and used in the hyperparameter tuning process.

', 'ObjectiveStatusCounters$Pending' => '

The number of training jobs that are in progress and pending evaluation of their final objective metric.

', 'ObjectiveStatusCounters$Failed' => '

The number of training jobs whose final objective metric was not evaluated and used in the hyperparameter tuning process. This typically occurs when the training job failed or did not emit an objective metric.

', ], ], 'ObjectiveStatusCounters' => [ 'base' => '

Specifies the number of training jobs that this hyperparameter tuning job launched, categorized by the status of their objective metric. The objective metric status shows whether the final objective metric for the training job has been evaluated by the tuning job and used in the hyperparameter tuning process.

', 'refs' => [ 'DescribeHyperParameterTuningJobResponse$ObjectiveStatusCounters' => '

The ObjectiveStatusCounters object that specifies the number of training jobs, categorized by the status of their final objective metric, that this tuning job launched.

', 'HyperParameterTuningJobSummary$ObjectiveStatusCounters' => '

The ObjectiveStatusCounters object that specifies the numbers of training jobs, categorized by objective metric status, that this tuning job launched.

', ], ], 'Operator' => [ 'base' => NULL, 'refs' => [ 'Filter$Operator' => '

A Boolean binary operator that is used to evaluate the filter. The operator field contains one of the following values:

Equals

The specified resource in Name equals the specified Value.

NotEquals

The specified resource in Name does not equal the specified Value.

GreaterThan

The specified resource in Name is greater than the specified Value. Not supported for text-based properties.

GreaterThanOrEqualTo

The specified resource in Name is greater than or equal to the specified Value. Not supported for text-based properties.

LessThan

The specified resource in Name is less than the specified Value. Not supported for text-based properties.

LessThanOrEqualTo

The specified resource in Name is less than or equal to the specified Value. Not supported for text-based properties.

Contains

Only supported for text-based properties. The word-list of the property contains the specified Value.

If you have specified a filter Value, the default is Equals.

', ], ], 'OrderKey' => [ 'base' => NULL, 'refs' => [ 'ListEndpointConfigsInput$SortOrder' => '

The sort order for results. The default is Descending.

', 'ListEndpointsInput$SortOrder' => '

The sort order for results. The default is Descending.

', 'ListModelsInput$SortOrder' => '

The sort order for results. The default is Descending.

', ], ], 'OutputConfig' => [ 'base' => '

Contains information about the output location for the compiled model and the device (target) that the model runs on.

', 'refs' => [ 'CreateCompilationJobRequest$OutputConfig' => '

Provides information about the output location for the compiled model and the target device the model runs on.

', 'DescribeCompilationJobResponse$OutputConfig' => '

Information about the output location for the compiled model and the target device that the model runs on.

', ], ], 'OutputDataConfig' => [ 'base' => '

Provides information about how to store model training results (model artifacts).

', 'refs' => [ 'CreateTrainingJobRequest$OutputDataConfig' => '

Specifies the path to the S3 bucket where you want to store model artifacts. Amazon SageMaker creates subfolders for the artifacts.

', 'DescribeTrainingJobResponse$OutputDataConfig' => '

The S3 path where model artifacts that you configured when creating the job are stored. Amazon SageMaker creates subfolders for model artifacts.

', 'HyperParameterTrainingJobDefinition$OutputDataConfig' => '

Specifies the path to the Amazon S3 bucket where you store model artifacts from the training jobs that the tuning job launches.

', 'TrainingJob$OutputDataConfig' => '

The S3 path where model artifacts that you configured when creating the job are stored. Amazon SageMaker creates subfolders for model artifacts.

', 'TrainingJobDefinition$OutputDataConfig' => '

the path to the S3 bucket where you want to store model artifacts. Amazon SageMaker creates subfolders for the artifacts.

', ], ], 'PaginationToken' => [ 'base' => NULL, 'refs' => [ 'ListEndpointConfigsInput$NextToken' => '

If the result of the previous ListEndpointConfig request was truncated, the response includes a NextToken. To retrieve the next set of endpoint configurations, use the token in the next request.

', 'ListEndpointConfigsOutput$NextToken' => '

If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of endpoint configurations, use it in the subsequent request

', 'ListEndpointsInput$NextToken' => '

If the result of a ListEndpoints request was truncated, the response includes a NextToken. To retrieve the next set of endpoints, use the token in the next request.

', 'ListEndpointsOutput$NextToken' => '

If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of training jobs, use it in the subsequent request.

', 'ListModelsInput$NextToken' => '

If the response to a previous ListModels request was truncated, the response includes a NextToken. To retrieve the next set of models, use the token in the next request.

', 'ListModelsOutput$NextToken' => '

If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of models, use it in the subsequent request.

', ], ], 'ParameterKey' => [ 'base' => NULL, 'refs' => [ 'CategoricalParameterRange$Name' => '

The name of the categorical hyperparameter to tune.

', 'ContinuousParameterRange$Name' => '

The name of the continuous hyperparameter to tune.

', 'HyperParameters$key' => NULL, 'IntegerParameterRange$Name' => '

The name of the hyperparameter to search.

', ], ], 'ParameterName' => [ 'base' => NULL, 'refs' => [ 'HyperParameterSpecification$Name' => '

The name of this hyperparameter. The name must be unique.

', ], ], 'ParameterRange' => [ 'base' => '

Defines the possible values for categorical, continuous, and integer hyperparameters to be used by an algorithm.

', 'refs' => [ 'HyperParameterSpecification$Range' => '

The allowed range for this hyperparameter.

', ], ], 'ParameterRanges' => [ 'base' => '

Specifies ranges of integer, continuous, and categorical hyperparameters that a hyperparameter tuning job searches. The hyperparameter tuning job launches training jobs with hyperparameter values within these ranges to find the combination of values that result in the training job with the best performance as measured by the objective metric of the hyperparameter tuning job.

You can specify a maximum of 20 hyperparameters that a hyperparameter tuning job can search over. Every possible value of a categorical parameter range counts against this limit.

', 'refs' => [ 'HyperParameterTuningJobConfig$ParameterRanges' => '

The ParameterRanges object that specifies the ranges of hyperparameters that this tuning job searches.

', ], ], 'ParameterType' => [ 'base' => NULL, 'refs' => [ 'HyperParameterSpecification$Type' => '

The type of this hyperparameter. The valid types are Integer, Continuous, Categorical, and FreeText.

', ], ], 'ParameterValue' => [ 'base' => NULL, 'refs' => [ 'ContinuousParameterRange$MinValue' => '

The minimum value for the hyperparameter. The tuning job uses floating-point values between this value and MaxValuefor tuning.

', 'ContinuousParameterRange$MaxValue' => '

The maximum value for the hyperparameter. The tuning job uses floating-point values between MinValue value and this value for tuning.

', 'ContinuousParameterRangeSpecification$MinValue' => '

The minimum floating-point value allowed.

', 'ContinuousParameterRangeSpecification$MaxValue' => '

The maximum floating-point value allowed.

', 'HyperParameterSpecification$DefaultValue' => '

The default value for this hyperparameter. If a default value is specified, a hyperparameter cannot be required.

', 'HyperParameters$value' => NULL, 'IntegerParameterRange$MinValue' => '

The minimum value of the hyperparameter to search.

', 'IntegerParameterRange$MaxValue' => '

The maximum value of the hyperparameter to search.

', 'IntegerParameterRangeSpecification$MinValue' => '

The minimum integer value allowed.

', 'IntegerParameterRangeSpecification$MaxValue' => '

The maximum integer value allowed.

', 'ParameterValues$member' => NULL, ], ], 'ParameterValues' => [ 'base' => NULL, 'refs' => [ 'CategoricalParameterRange$Values' => '

A list of the categories for the hyperparameter.

', 'CategoricalParameterRangeSpecification$Values' => '

The allowed categories for the hyperparameter.

', ], ], 'ParentHyperParameterTuningJob' => [ 'base' => '

A previously completed or stopped hyperparameter tuning job to be used as a starting point for a new hyperparameter tuning job.

', 'refs' => [ 'ParentHyperParameterTuningJobs$member' => NULL, ], ], 'ParentHyperParameterTuningJobs' => [ 'base' => NULL, 'refs' => [ 'HyperParameterTuningJobWarmStartConfig$ParentHyperParameterTuningJobs' => '

An array of hyperparameter tuning jobs that are used as the starting point for the new hyperparameter tuning job. For more information about warm starting a hyperparameter tuning job, see Using a Previous Hyperparameter Tuning Job as a Starting Point.

Hyperparameter tuning jobs created before October 1, 2018 cannot be used as parent jobs for warm start tuning jobs.

', ], ], 'ProductId' => [ 'base' => NULL, 'refs' => [ 'DescribeAlgorithmOutput$ProductId' => '

The product identifier of the algorithm.

', 'ModelPackageContainerDefinition$ProductId' => '

The AWS Marketplace product ID of the model package.

', ], ], 'ProductListings' => [ 'base' => NULL, 'refs' => [ 'Workteam$ProductListingIds' => '

The Amazon Marketplace identifier for a vendor\'s work team.

', ], ], 'ProductionVariant' => [ 'base' => '

Identifies a model that you want to host and the resources to deploy for hosting it. If you are deploying multiple models, tell Amazon SageMaker how to distribute traffic among the models by specifying variant weights.

', 'refs' => [ 'ProductionVariantList$member' => NULL, ], ], 'ProductionVariantAcceleratorType' => [ 'base' => NULL, 'refs' => [ 'ProductionVariant$AcceleratorType' => '

The size of the Elastic Inference (EI) instance to use for the production variant. EI instances provide on-demand GPU computing for inference. For more information, see Using Elastic Inference in Amazon SageMaker. For more information, see Using Elastic Inference in Amazon SageMaker.

', ], ], 'ProductionVariantInstanceType' => [ 'base' => NULL, 'refs' => [ 'ProductionVariant$InstanceType' => '

The ML compute instance type.

', 'RealtimeInferenceInstanceTypes$member' => NULL, ], ], 'ProductionVariantList' => [ 'base' => NULL, 'refs' => [ 'CreateEndpointConfigInput$ProductionVariants' => '

An list of ProductionVariant objects, one for each model that you want to host at this endpoint.

', 'DescribeEndpointConfigOutput$ProductionVariants' => '

An array of ProductionVariant objects, one for each model that you want to host at this endpoint.

', ], ], 'ProductionVariantSummary' => [ 'base' => '

Describes weight and capacities for a production variant associated with an endpoint. If you sent a request to the UpdateEndpointWeightsAndCapacities API and the endpoint status is Updating, you get different desired and current values.

', 'refs' => [ 'ProductionVariantSummaryList$member' => NULL, ], ], 'ProductionVariantSummaryList' => [ 'base' => NULL, 'refs' => [ 'DescribeEndpointOutput$ProductionVariants' => '

An array of ProductionVariantSummary objects, one for each model hosted behind this endpoint.

', ], ], 'PropertyNameHint' => [ 'base' => NULL, 'refs' => [ 'PropertyNameQuery$PropertyNameHint' => '

Text that is part of a property\'s name. The property names of hyperparameter, metric, and tag key names that begin with the specified text in the PropertyNameHint.

', ], ], 'PropertyNameQuery' => [ 'base' => '

A type of SuggestionQuery. A suggestion query for retrieving property names that match the specified hint.

', 'refs' => [ 'SuggestionQuery$PropertyNameQuery' => '

A type of SuggestionQuery. Defines a property name hint. Only property names that match the specified hint are included in the response.

', ], ], 'PropertyNameSuggestion' => [ 'base' => '

A property name returned from a GetSearchSuggestions call that specifies a value in the PropertyNameQuery field.

', 'refs' => [ 'PropertyNameSuggestionList$member' => NULL, ], ], 'PropertyNameSuggestionList' => [ 'base' => NULL, 'refs' => [ 'GetSearchSuggestionsResponse$PropertyNameSuggestions' => '

A list of property names for a Resource that match a SuggestionQuery.

', ], ], 'PublicWorkforceTaskPrice' => [ 'base' => '

Defines the amount of money paid to an Amazon Mechanical Turk worker for each task performed.

Use one of the following prices for bounding box tasks. Prices are in US dollars.

  • 0.036

  • 0.048

  • 0.060

  • 0.072

  • 0.120

  • 0.240

  • 0.360

  • 0.480

  • 0.600

  • 0.720

  • 0.840

  • 0.960

  • 1.080

  • 1.200

Use one of the following prices for image classification, text classification, and custom tasks. Prices are in US dollars.

  • 0.012

  • 0.024

  • 0.036

  • 0.048

  • 0.060

  • 0.072

  • 0.120

  • 0.240

  • 0.360

  • 0.480

  • 0.600

  • 0.720

  • 0.840

  • 0.960

  • 1.080

  • 1.200

Use one of the following prices for semantic segmentation tasks. Prices are in US dollars.

  • 0.840

  • 0.960

  • 1.080

  • 1.200

', 'refs' => [ 'HumanTaskConfig$PublicWorkforceTaskPrice' => '

The price that you pay for each task performed by a public worker.

', ], ], 'RealtimeInferenceInstanceTypes' => [ 'base' => NULL, 'refs' => [ 'InferenceSpecification$SupportedRealtimeInferenceInstanceTypes' => '

A list of the instance types that are used to generate inferences in real-time.

', ], ], 'RecordWrapper' => [ 'base' => NULL, 'refs' => [ 'Channel$RecordWrapperType' => '

Specify RecordIO as the value when input data is in raw format but the training algorithm requires the RecordIO format. In this case, Amazon SageMaker wraps each individual S3 object in a RecordIO record. If the input data is already in RecordIO format, you don\'t need to set this attribute. For more information, see Create a Dataset Using RecordIO.

In File mode, leave this field unset or set it to None.

', ], ], 'RenderUiTemplateRequest' => [ 'base' => NULL, 'refs' => [], ], 'RenderUiTemplateResponse' => [ 'base' => NULL, 'refs' => [], ], 'RenderableTask' => [ 'base' => '

Contains input values for a task.

', 'refs' => [ 'RenderUiTemplateRequest$Task' => '

A RenderableTask object containing a representative task to render.

', ], ], 'RenderingError' => [ 'base' => '

A description of an error that occurred while rendering the template.

', 'refs' => [ 'RenderingErrorList$member' => NULL, ], ], 'RenderingErrorList' => [ 'base' => NULL, 'refs' => [ 'RenderUiTemplateResponse$Errors' => '

A list of one or more RenderingError objects if any were encountered while rendering the template. If there were no errors, the list is empty.

', ], ], 'ResourceArn' => [ 'base' => NULL, 'refs' => [ 'AddTagsInput$ResourceArn' => '

The Amazon Resource Name (ARN) of the resource that you want to tag.

', 'DeleteTagsInput$ResourceArn' => '

The Amazon Resource Name (ARN) of the resource whose tags you want to delete.

', 'ListTagsInput$ResourceArn' => '

The Amazon Resource Name (ARN) of the resource whose tags you want to retrieve.

', ], ], 'ResourceConfig' => [ 'base' => '

Describes the resources, including ML compute instances and ML storage volumes, to use for model training.

', 'refs' => [ 'CreateTrainingJobRequest$ResourceConfig' => '

The resources, including the ML compute instances and ML storage volumes, to use for model training.

ML storage volumes store model artifacts and incremental states. Training algorithms might also use ML storage volumes for scratch space. If you want Amazon SageMaker to use the ML storage volume to store the training data, choose File as the TrainingInputMode in the algorithm specification. For distributed training algorithms, specify an instance count greater than 1.

', 'DescribeTrainingJobResponse$ResourceConfig' => '

Resources, including ML compute instances and ML storage volumes, that are configured for model training.

', 'HyperParameterTrainingJobDefinition$ResourceConfig' => '

The resources, including the compute instances and storage volumes, to use for the training jobs that the tuning job launches.

Storage volumes store model artifacts and incremental states. Training algorithms might also use storage volumes for scratch space. If you want Amazon SageMaker to use the storage volume to store the training data, choose File as the TrainingInputMode in the algorithm specification. For distributed training algorithms, specify an instance count greater than 1.

', 'TrainingJob$ResourceConfig' => '

Resources, including ML compute instances and ML storage volumes, that are configured for model training.

', 'TrainingJobDefinition$ResourceConfig' => '

The resources, including the ML compute instances and ML storage volumes, to use for model training.

', ], ], 'ResourceInUse' => [ 'base' => '

Resource being accessed is in use.

', 'refs' => [], ], 'ResourceLimitExceeded' => [ 'base' => '

You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.

', 'refs' => [], ], 'ResourceLimits' => [ 'base' => '

Specifies the maximum number of training jobs and parallel training jobs that a hyperparameter tuning job can launch.

', 'refs' => [ 'HyperParameterTuningJobConfig$ResourceLimits' => '

The ResourceLimits object that specifies the maximum number of training jobs and parallel training jobs for this tuning job.

', 'HyperParameterTuningJobSummary$ResourceLimits' => '

The ResourceLimits object that specifies the maximum number of training jobs and parallel training jobs allowed for this tuning job.

', ], ], 'ResourceNotFound' => [ 'base' => '

Resource being access is not found.

', 'refs' => [], ], 'ResourcePropertyName' => [ 'base' => NULL, 'refs' => [ 'Filter$Name' => '

A property name. For example, TrainingJobName. For the list of valid property names returned in a search result for each supported resource, see TrainingJob properties. You must specify a valid property name for the resource.

', 'NestedFilters$NestedPropertyName' => '

The name of the property to use in the nested filters. The value must match a listed property name, such as InputDataConfig.

', 'PropertyNameSuggestion$PropertyName' => '

A suggested property name based on what you entered in the search textbox in the Amazon SageMaker console.

', 'SearchRequest$SortBy' => '

The name of the resource property used to sort the SearchResults. The default is LastModifiedTime.

', ], ], 'ResourceType' => [ 'base' => NULL, 'refs' => [ 'GetSearchSuggestionsRequest$Resource' => '

The name of the Amazon SageMaker resource to Search for. The only valid Resource value is TrainingJob.

', 'SearchRequest$Resource' => '

The name of the Amazon SageMaker resource to search for. Currently, the only valid Resource value is TrainingJob.

', ], ], 'ResponseMIMEType' => [ 'base' => NULL, 'refs' => [ 'ResponseMIMETypes$member' => NULL, ], ], 'ResponseMIMETypes' => [ 'base' => NULL, 'refs' => [ 'InferenceSpecification$SupportedResponseMIMETypes' => '

The supported MIME types for the output data.

', ], ], 'RoleArn' => [ 'base' => NULL, 'refs' => [ 'AlgorithmValidationSpecification$ValidationRole' => '

The IAM roles that Amazon SageMaker uses to run the training jobs.

', 'CreateCompilationJobRequest$RoleArn' => '

The Amazon Resource Name (ARN) of an IAM role that enables Amazon SageMaker to perform tasks on your behalf.

During model compilation, Amazon SageMaker needs your permission to:

  • Read input data from an S3 bucket

  • Write model artifacts to an S3 bucket

  • Write logs to Amazon CloudWatch Logs

  • Publish metrics to Amazon CloudWatch

You grant permissions for all of these tasks to an IAM role. To pass this role to Amazon SageMaker, the caller of this API must have the iam:PassRole permission. For more information, see Amazon SageMaker Roles.

', 'CreateLabelingJobRequest$RoleArn' => '

The Amazon Resource Number (ARN) that Amazon SageMaker assumes to perform tasks on your behalf during data labeling. You must grant this role the necessary permissions so that Amazon SageMaker can successfully complete data labeling.

', 'CreateModelInput$ExecutionRoleArn' => '

The Amazon Resource Name (ARN) of the IAM role that Amazon SageMaker can assume to access model artifacts and docker image for deployment on ML compute instances or for batch transform jobs. Deploying on ML compute instances is part of model hosting. For more information, see Amazon SageMaker Roles.

To be able to pass this role to Amazon SageMaker, the caller of this API must have the iam:PassRole permission.

', 'CreateNotebookInstanceInput$RoleArn' => '

When you send any requests to AWS resources from the notebook instance, Amazon SageMaker assumes this role to perform tasks on your behalf. You must grant this role necessary permissions so Amazon SageMaker can perform these tasks. The policy must allow the Amazon SageMaker service principal (sagemaker.amazonaws.com) permissions to assume this role. For more information, see Amazon SageMaker Roles.

To be able to pass this role to Amazon SageMaker, the caller of this API must have the iam:PassRole permission.

', 'CreateTrainingJobRequest$RoleArn' => '

The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.

During model training, Amazon SageMaker needs your permission to read input data from an S3 bucket, download a Docker image that contains training code, write model artifacts to an S3 bucket, write logs to Amazon CloudWatch Logs, and publish metrics to Amazon CloudWatch. You grant permissions for all of these tasks to an IAM role. For more information, see Amazon SageMaker Roles.

To be able to pass this role to Amazon SageMaker, the caller of this API must have the iam:PassRole permission.

', 'DescribeCompilationJobResponse$RoleArn' => '

The Amazon Resource Name (ARN) of the model compilation job.

', 'DescribeLabelingJobResponse$RoleArn' => '

The Amazon Resource Name (ARN) that Amazon SageMaker assumes to perform tasks on your behalf during data labeling.

', 'DescribeModelOutput$ExecutionRoleArn' => '

The Amazon Resource Name (ARN) of the IAM role that you specified for the model.

', 'DescribeNotebookInstanceOutput$RoleArn' => '

The Amazon Resource Name (ARN) of the IAM role associated with the instance.

', 'DescribeTrainingJobResponse$RoleArn' => '

The AWS Identity and Access Management (IAM) role configured for the training job.

', 'HyperParameterTrainingJobDefinition$RoleArn' => '

The Amazon Resource Name (ARN) of the IAM role associated with the training jobs that the tuning job launches.

', 'ModelPackageValidationSpecification$ValidationRole' => '

The IAM roles to be used for the validation of the model package.

', 'RenderUiTemplateRequest$RoleArn' => '

The Amazon Resource Name (ARN) that has access to the S3 objects that are used by the template.

', 'TrainingJob$RoleArn' => '

The AWS Identity and Access Management (IAM) role configured for the training job.

', 'UpdateNotebookInstanceInput$RoleArn' => '

The Amazon Resource Name (ARN) of the IAM role that Amazon SageMaker can assume to access the notebook instance. For more information, see Amazon SageMaker Roles.

To be able to pass this role to Amazon SageMaker, the caller of this API must have the iam:PassRole permission.

', ], ], 'RootAccess' => [ 'base' => NULL, 'refs' => [ 'CreateNotebookInstanceInput$RootAccess' => '

Whether root access is enabled or disabled for users of the notebook instance. The default value is Enabled.

Lifecycle configurations need root access to be able to set up a notebook instance. Because of this, lifecycle configurations associated with a notebook instance always run with root access even if you disable root access for users.

', 'DescribeNotebookInstanceOutput$RootAccess' => '

Whether root access is enabled or disabled for users of the notebook instance.

Lifecycle configurations need root access to be able to set up a notebook instance. Because of this, lifecycle configurations associated with a notebook instance always run with root access even if you disable root access for users.

', 'UpdateNotebookInstanceInput$RootAccess' => '

Whether root access is enabled or disabled for users of the notebook instance. The default value is Enabled.

If you set this to Disabled, users don\'t have root access on the notebook instance, but lifecycle configuration scripts still run with root permissions.

', ], ], 'S3DataDistribution' => [ 'base' => NULL, 'refs' => [ 'S3DataSource$S3DataDistributionType' => '

If you want Amazon SageMaker to replicate the entire dataset on each ML compute instance that is launched for model training, specify FullyReplicated.

If you want Amazon SageMaker to replicate a subset of data on each ML compute instance that is launched for model training, specify ShardedByS3Key. If there are n ML compute instances launched for a training job, each instance gets approximately 1/n of the number of S3 objects. In this case, model training on each machine uses only the subset of training data.

Don\'t choose more ML compute instances for training than available S3 objects. If you do, some nodes won\'t get any data and you will pay for nodes that aren\'t getting any training data. This applies in both File and Pipe modes. Keep this in mind when developing algorithms.

In distributed training, where you use multiple ML compute EC2 instances, you might choose ShardedByS3Key. If the algorithm requires copying training data to the ML storage volume (when TrainingInputMode is set to File), this copies 1/n of the number of objects.

', ], ], 'S3DataSource' => [ 'base' => '

Describes the S3 data source.

', 'refs' => [ 'DataSource$S3DataSource' => '

The S3 location of the data source that is associated with a channel.

', ], ], 'S3DataType' => [ 'base' => NULL, 'refs' => [ 'S3DataSource$S3DataType' => '

If you choose S3Prefix, S3Uri identifies a key name prefix. Amazon SageMaker uses all objects that match the specified key name prefix for model training.

If you choose ManifestFile, S3Uri identifies an object that is a manifest file containing a list of object keys that you want Amazon SageMaker to use for model training.

If you choose AugmentedManifestFile, S3Uri identifies an object that is an augmented manifest file in JSON lines format. This file contains the data you want to use for model training. AugmentedManifestFile can only be used if the Channel\'s input mode is Pipe.

', 'TransformS3DataSource$S3DataType' => '

If you choose S3Prefix, S3Uri identifies a key name prefix. Amazon SageMaker uses all objects with the specified key name prefix for batch transform.

If you choose ManifestFile, S3Uri identifies an object that is a manifest file containing a list of object keys that you want Amazon SageMaker to use for batch transform.

The following values are compatible: ManifestFile, S3Prefix

The following value is not compatible: AugmentedManifestFile

', ], ], 'S3Uri' => [ 'base' => NULL, 'refs' => [ 'CreateLabelingJobRequest$LabelCategoryConfigS3Uri' => '

The S3 URL of the file that defines the categories used to label the data objects.

The file is a JSON structure in the following format:

{

"document-version": "2018-11-28"

"labels": [

{

"label": "label 1"

},

{

"label": "label 2"

},

...

{

"label": "label n"

}

]

}

', 'DescribeLabelingJobResponse$LabelCategoryConfigS3Uri' => '

The S3 location of the JSON file that defines the categories used to label data objects.

The file is a JSON structure in the following format:

{

"document-version": "2018-11-28"

"labels": [

{

"label": "label 1"

},

{

"label": "label 2"

},

...

{

"label": "label n"

}

]

}

', 'InputConfig$S3Uri' => '

The S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).

', 'LabelingJobOutput$OutputDatasetS3Uri' => '

The Amazon S3 bucket location of the manifest file for labeled data.

', 'LabelingJobOutputConfig$S3OutputPath' => '

The Amazon S3 location to write output data.

', 'LabelingJobS3DataSource$ManifestS3Uri' => '

The Amazon S3 location of the manifest file that describes the input data objects.

', 'ModelArtifacts$S3ModelArtifacts' => '

The path of the S3 object that contains the model artifacts. For example, s3://bucket-name/keynameprefix/model.tar.gz.

', 'OutputConfig$S3OutputLocation' => '

Identifies the S3 path where you want Amazon SageMaker to store the model artifacts. For example, s3://bucket-name/key-name-prefix.

', 'OutputDataConfig$S3OutputPath' => '

Identifies the S3 path where you want Amazon SageMaker to store the model artifacts. For example, s3://bucket-name/key-name-prefix.

', 'S3DataSource$S3Uri' => '

Depending on the value specified for the S3DataType, identifies either a key name prefix or a manifest. For example:

  • A key name prefix might look like this: s3://bucketname/exampleprefix.

  • A manifest might look like this: s3://bucketname/example.manifest

    The manifest is an S3 object which is a JSON file with the following format:

    [

    {"prefix": "s3://customer_bucket/some/prefix/"},

    "relative/path/to/custdata-1",

    "relative/path/custdata-2",

    ...

    ]

    The preceding JSON matches the following s3Uris:

    s3://customer_bucket/some/prefix/relative/path/to/custdata-1

    s3://customer_bucket/some/prefix/relative/path/custdata-2

    ...

    The complete set of s3uris in this manifest is the input data for the channel for this datasource. The object that each s3uris points to must be readable by the IAM role that Amazon SageMaker uses to perform tasks on your behalf.

', 'TransformOutput$S3OutputPath' => '

The Amazon S3 path where you want Amazon SageMaker to store the results of the transform job. For example, s3://bucket-name/key-name-prefix.

For every S3 object used as input for the transform job, batch transform stores the transformed data with an .out suffix in a corresponding subfolder in the location in the output prefix. For example, for the input data stored at s3://bucket-name/input-name-prefix/dataset01/data.csv, batch transform stores the transformed data at s3://bucket-name/output-name-prefix/input-name-prefix/data.csv.out. Batch transform doesn\'t upload partially processed objects. For an input S3 object that contains multiple records, it creates an .out file only if the transform job succeeds on the entire file. When the input contains multiple S3 objects, the batch transform job processes the listed S3 objects and uploads only the output for successfully processed objects. If any object fails in the transform job batch transform marks the job as failed to prompt investigation.

', 'TransformS3DataSource$S3Uri' => '

Depending on the value specified for the S3DataType, identifies either a key name prefix or a manifest. For example:

  • A key name prefix might look like this: s3://bucketname/exampleprefix.

  • A manifest might look like this: s3://bucketname/example.manifest

    The manifest is an S3 object which is a JSON file with the following format:

    [

    {"prefix": "s3://customer_bucket/some/prefix/"},

    "relative/path/to/custdata-1",

    "relative/path/custdata-2",

    ...

    ]

    The preceding JSON matches the following S3Uris:

    s3://customer_bucket/some/prefix/relative/path/to/custdata-1

    s3://customer_bucket/some/prefix/relative/path/custdata-1

    ...

    The complete set of S3Uris in this manifest constitutes the input data for the channel for this datasource. The object that each S3Uris points to must be readable by the IAM role that Amazon SageMaker uses to perform tasks on your behalf.

', 'UiConfig$UiTemplateS3Uri' => '

The Amazon S3 bucket location of the UI template. For more information about the contents of a UI template, see Creating Your Custom Labeling Task Template.

', ], ], 'SearchExpression' => [ 'base' => '

A multi-expression that searches for the specified resource or resources in a search. All resource objects that satisfy the expression\'s condition are included in the search results. You must specify at least one subexpression, filter, or nested filter. A SearchExpression can contain up to twenty elements.

A SearchExpression contains the following components:

  • A list of Filter objects. Each filter defines a simple Boolean expression comprised of a resource property name, Boolean operator, and value.

  • A list of NestedFilter objects. Each nested filter defines a list of Boolean expressions using a list of resource properties. A nested filter is satisfied if a single object in the list satisfies all Boolean expressions.

  • A list of SearchExpression objects. A search expression object can be nested in a list of search expression objects.

  • A Boolean operator: And or Or.

', 'refs' => [ 'SearchExpressionList$member' => NULL, 'SearchRequest$SearchExpression' => '

A Boolean conditional statement. Resource objects must satisfy this condition to be included in search results. You must provide at least one subexpression, filter, or nested filter. The maximum number of recursive SubExpressions, NestedFilters, and Filters that can be included in a SearchExpression object is 50.

', ], ], 'SearchExpressionList' => [ 'base' => NULL, 'refs' => [ 'SearchExpression$SubExpressions' => '

A list of search expression objects.

', ], ], 'SearchRecord' => [ 'base' => '

An individual search result record that contains a single resource object.

', 'refs' => [ 'SearchResultsList$member' => NULL, ], ], 'SearchRequest' => [ 'base' => NULL, 'refs' => [], ], 'SearchResponse' => [ 'base' => NULL, 'refs' => [], ], 'SearchResultsList' => [ 'base' => NULL, 'refs' => [ 'SearchResponse$Results' => '

A list of SearchResult objects.

', ], ], 'SearchSortOrder' => [ 'base' => NULL, 'refs' => [ 'SearchRequest$SortOrder' => '

How SearchResults are ordered. Valid values are Ascending or Descending. The default is Descending.

', ], ], 'SecondaryStatus' => [ 'base' => NULL, 'refs' => [ 'DescribeTrainingJobResponse$SecondaryStatus' => '

Provides detailed information about the state of the training job. For detailed information on the secondary status of the training job, see StatusMessage under SecondaryStatusTransition.

Amazon SageMaker provides primary statuses and secondary statuses that apply to each of them:

InProgress

  • Starting - Starting the training job.

  • Downloading - An optional stage for algorithms that support File training input mode. It indicates that data is being downloaded to the ML storage volumes.

  • Training - Training is in progress.

  • Uploading - Training is complete and the model artifacts are being uploaded to the S3 location.

Completed

  • Completed - The training job has completed.

Failed

  • Failed - The training job has failed. The reason for the failure is returned in the FailureReason field of DescribeTrainingJobResponse.

Stopped

  • MaxRuntimeExceeded - The job stopped because it exceeded the maximum allowed runtime.

  • Stopped - The training job has stopped.

Stopping

  • Stopping - Stopping the training job.

Valid values for SecondaryStatus are subject to change.

We no longer support the following secondary statuses:

  • LaunchingMLInstances

  • PreparingTrainingStack

  • DownloadingTrainingImage

', 'SecondaryStatusTransition$Status' => '

Contains a secondary status information from a training job.

Status might be one of the following secondary statuses:

InProgress

  • Starting - Starting the training job.

  • Downloading - An optional stage for algorithms that support File training input mode. It indicates that data is being downloaded to the ML storage volumes.

  • Training - Training is in progress.

  • Uploading - Training is complete and the model artifacts are being uploaded to the S3 location.

Completed

  • Completed - The training job has completed.

Failed

  • Failed - The training job has failed. The reason for the failure is returned in the FailureReason field of DescribeTrainingJobResponse.

Stopped

  • MaxRuntimeExceeded - The job stopped because it exceeded the maximum allowed runtime.

  • Stopped - The training job has stopped.

Stopping

  • Stopping - Stopping the training job.

We no longer support the following secondary statuses:

  • LaunchingMLInstances

  • PreparingTrainingStack

  • DownloadingTrainingImage

', 'TrainingJob$SecondaryStatus' => '

Provides detailed information about the state of the training job. For detailed information about the secondary status of the training job, see StatusMessage under SecondaryStatusTransition.

Amazon SageMaker provides primary statuses and secondary statuses that apply to each of them:

InProgress

  • Starting - Starting the training job.

  • Downloading - An optional stage for algorithms that support File training input mode. It indicates that data is being downloaded to the ML storage volumes.

  • Training - Training is in progress.

  • Uploading - Training is complete and the model artifacts are being uploaded to the S3 location.

Completed

  • Completed - The training job has completed.

Failed

  • Failed - The training job has failed. The reason for the failure is returned in the FailureReason field of DescribeTrainingJobResponse.

Stopped

  • MaxRuntimeExceeded - The job stopped because it exceeded the maximum allowed runtime.

  • Stopped - The training job has stopped.

Stopping

  • Stopping - Stopping the training job.

Valid values for SecondaryStatus are subject to change.

We no longer support the following secondary statuses:

  • LaunchingMLInstances

  • PreparingTrainingStack

  • DownloadingTrainingImage

', ], ], 'SecondaryStatusTransition' => [ 'base' => '

An array element of DescribeTrainingJobResponse$SecondaryStatusTransitions. It provides additional details about a status that the training job has transitioned through. A training job can be in one of several states, for example, starting, downloading, training, or uploading. Within each state, there are a number of intermediate states. For example, within the starting state, Amazon SageMaker could be starting the training job or launching the ML instances. These transitional states are referred to as the job\'s secondary status.

', 'refs' => [ 'SecondaryStatusTransitions$member' => NULL, ], ], 'SecondaryStatusTransitions' => [ 'base' => NULL, 'refs' => [ 'DescribeTrainingJobResponse$SecondaryStatusTransitions' => '

A history of all of the secondary statuses that the training job has transitioned through.

', 'TrainingJob$SecondaryStatusTransitions' => '

A history of all of the secondary statuses that the training job has transitioned through.

', ], ], 'SecretArn' => [ 'base' => NULL, 'refs' => [ 'GitConfig$SecretArn' => '

The Amazon Resource Name (ARN) of the AWS Secrets Manager secret that contains the credentials used to access the git repository. The secret must have a staging label of AWSCURRENT and must be in the following format:

{"username": UserName, "password": Password}

', 'GitConfigForUpdate$SecretArn' => '

The Amazon Resource Name (ARN) of the AWS Secrets Manager secret that contains the credentials used to access the git repository. The secret must have a staging label of AWSCURRENT and must be in the following format:

{"username": UserName, "password": Password}

', ], ], 'SecurityGroupId' => [ 'base' => NULL, 'refs' => [ 'SecurityGroupIds$member' => NULL, 'VpcSecurityGroupIds$member' => NULL, ], ], 'SecurityGroupIds' => [ 'base' => NULL, 'refs' => [ 'CreateNotebookInstanceInput$SecurityGroupIds' => '

The VPC security group IDs, in the form sg-xxxxxxxx. The security groups must be for the same VPC as specified in the subnet.

', 'DescribeNotebookInstanceOutput$SecurityGroups' => '

The IDs of the VPC security groups.

', ], ], 'Seed' => [ 'base' => NULL, 'refs' => [ 'ShuffleConfig$Seed' => '

Determines the shuffling order in ShuffleConfig value.

', ], ], 'SessionExpirationDurationInSeconds' => [ 'base' => NULL, 'refs' => [ 'CreatePresignedNotebookInstanceUrlInput$SessionExpirationDurationInSeconds' => '

The duration of the session, in seconds. The default is 12 hours.

', ], ], 'ShuffleConfig' => [ 'base' => '

A configuration for a shuffle option for input data in a channel. If you use S3Prefix for S3DataType, the results of the S3 key prefix matches are shuffled. If you use ManifestFile, the order of the S3 object references in the ManifestFile is shuffled. If you use AugmentedManifestFile, the order of the JSON lines in the AugmentedManifestFile is shuffled. The shuffling order is determined using the Seed value.

For Pipe input mode, shuffling is done at the start of every epoch. With large datasets, this ensures that the order of the training data is different for each epoch, and it helps reduce bias and possible overfitting. In a multi-node training job when ShuffleConfig is combined with S3DataDistributionType of ShardedByS3Key, the data is shuffled across nodes so that the content sent to a particular node on the first epoch might be sent to a different node on the second epoch.

', 'refs' => [ 'Channel$ShuffleConfig' => '

A configuration for a shuffle option for input data in a channel. If you use S3Prefix for S3DataType, this shuffles the results of the S3 key prefix matches. If you use ManifestFile, the order of the S3 object references in the ManifestFile is shuffled. If you use AugmentedManifestFile, the order of the JSON lines in the AugmentedManifestFile is shuffled. The shuffling order is determined using the Seed value.

For Pipe input mode, shuffling is done at the start of every epoch. With large datasets this ensures that the order of the training data is different for each epoch, it helps reduce bias and possible overfitting. In a multi-node training job when ShuffleConfig is combined with S3DataDistributionType of ShardedByS3Key, the data is shuffled across nodes so that the content sent to a particular node on the first epoch might be sent to a different node on the second epoch.

', ], ], 'SortBy' => [ 'base' => NULL, 'refs' => [ 'ListLabelingJobsRequest$SortBy' => '

The field to sort results by. The default is CreationTime.

', 'ListTrainingJobsRequest$SortBy' => '

The field to sort results by. The default is CreationTime.

', 'ListTransformJobsRequest$SortBy' => '

The field to sort results by. The default is CreationTime.

', ], ], 'SortOrder' => [ 'base' => NULL, 'refs' => [ 'ListAlgorithmsInput$SortOrder' => '

The sort order for the results. The default is Ascending.

', 'ListCompilationJobsRequest$SortOrder' => '

The sort order for results. The default is Ascending.

', 'ListHyperParameterTuningJobsRequest$SortOrder' => '

The sort order for results. The default is Ascending.

', 'ListLabelingJobsForWorkteamRequest$SortOrder' => '

The sort order for results. The default is Ascending.

', 'ListLabelingJobsRequest$SortOrder' => '

The sort order for results. The default is Ascending.

', 'ListModelPackagesInput$SortOrder' => '

The sort order for the results. The default is Ascending.

', 'ListTrainingJobsForHyperParameterTuningJobRequest$SortOrder' => '

The sort order for results. The default is Ascending.

', 'ListTrainingJobsRequest$SortOrder' => '

The sort order for results. The default is Ascending.

', 'ListTransformJobsRequest$SortOrder' => '

The sort order for results. The default is Descending.

', 'ListWorkteamsRequest$SortOrder' => '

The sort order for results. The default is Ascending.

', ], ], 'SourceAlgorithm' => [ 'base' => '

Specifies an algorithm that was used to create the model package. The algorithm must be either an algorithm resource in your Amazon SageMaker account or an algorithm in AWS Marketplace that you are subscribed to.

', 'refs' => [ 'SourceAlgorithmList$member' => NULL, ], ], 'SourceAlgorithmList' => [ 'base' => NULL, 'refs' => [ 'SourceAlgorithmSpecification$SourceAlgorithms' => '

A list of the algorithms that were used to create a model package.

', ], ], 'SourceAlgorithmSpecification' => [ 'base' => '

A list of algorithms that were used to create a model package.

', 'refs' => [ 'CreateModelPackageInput$SourceAlgorithmSpecification' => '

Details about the algorithm that was used to create the model package.

', 'DescribeModelPackageOutput$SourceAlgorithmSpecification' => '

Details about the algorithm that was used to create the model package.

', ], ], 'SplitType' => [ 'base' => NULL, 'refs' => [ 'TransformInput$SplitType' => '

The method to use to split the transform job\'s data files into smaller batches. Splitting is necessary when the total size of each object is too large to fit in a single request. You can also use data splitting to improve performance by processing multiple concurrent mini-batches. The default value for SplitType is None, which indicates that input data files are not split, and request payloads contain the entire contents of an input object. Set the value of this parameter to Line to split records on a newline character boundary. SplitType also supports a number of record-oriented binary data formats.

When splitting is enabled, the size of a mini-batch depends on the values of the BatchStrategy and MaxPayloadInMB parameters. When the value of BatchStrategy is MultiRecord, Amazon SageMaker sends the maximum number of records in each request, up to the MaxPayloadInMB limit. If the value of BatchStrategy is SingleRecord, Amazon SageMaker sends individual records in each request.

Some data formats represent a record as a binary payload wrapped with extra padding bytes. When splitting is applied to a binary data format, padding is removed if the value of BatchStrategy is set to SingleRecord. Padding is not removed if the value of BatchStrategy is set to MultiRecord.

For more information about the RecordIO, see Data Format in the MXNet documentation. For more information about the TFRecord, see Consuming TFRecord data in the TensorFlow documentation.

', ], ], 'StartNotebookInstanceInput' => [ 'base' => NULL, 'refs' => [], ], 'StatusMessage' => [ 'base' => NULL, 'refs' => [ 'SecondaryStatusTransition$StatusMessage' => '

A detailed description of the progress within a secondary status.

Amazon SageMaker provides secondary statuses and status messages that apply to each of them:

Starting

  • Starting the training job.

  • Launching requested ML instances.

  • Insufficient capacity error from EC2 while launching instances, retrying!

  • Launched instance was unhealthy, replacing it!

  • Preparing the instances for training.

Training

  • Downloading the training image.

  • Training image download completed. Training in progress.

Status messages are subject to change. Therefore, we recommend not including them in code that programmatically initiates actions. For examples, don\'t use status messages in if statements.

To have an overview of your training job\'s progress, view TrainingJobStatus and SecondaryStatus in DescribeTrainingJobResponse, and StatusMessage together. For example, at the start of a training job, you might see the following:

  • TrainingJobStatus - InProgress

  • SecondaryStatus - Training

  • StatusMessage - Downloading the training image

', ], ], 'StopCompilationJobRequest' => [ 'base' => NULL, 'refs' => [], ], 'StopHyperParameterTuningJobRequest' => [ 'base' => NULL, 'refs' => [], ], 'StopLabelingJobRequest' => [ 'base' => NULL, 'refs' => [], ], 'StopNotebookInstanceInput' => [ 'base' => NULL, 'refs' => [], ], 'StopTrainingJobRequest' => [ 'base' => NULL, 'refs' => [], ], 'StopTransformJobRequest' => [ 'base' => NULL, 'refs' => [], ], 'StoppingCondition' => [ 'base' => '

Specifies how long model training can run. When model training reaches the limit, Amazon SageMaker ends the training job. Use this API to cap model training cost.

To stop a job, Amazon SageMaker sends the algorithm the SIGTERM signal, which delays job termination for120 seconds. Algorithms might use this 120-second window to save the model artifacts, so the results of training is not lost.

Training algorithms provided by Amazon SageMaker automatically saves the intermediate results of a model training job (it is best effort case, as model might not be ready to save as some stages, for example training just started). This intermediate data is a valid model artifact. You can use it to create a model (CreateModel).

', 'refs' => [ 'CreateCompilationJobRequest$StoppingCondition' => '

The duration allowed for model compilation.

', 'CreateTrainingJobRequest$StoppingCondition' => '

Sets a duration for training. Use this parameter to cap model training costs. To stop a job, Amazon SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds. Algorithms might use this 120-second window to save the model artifacts.

When Amazon SageMaker terminates a job because the stopping condition has been met, training algorithms provided by Amazon SageMaker save the intermediate results of the job. This intermediate data is a valid model artifact. You can use it to create a model using the CreateModel API.

', 'DescribeCompilationJobResponse$StoppingCondition' => '

The duration allowed for model compilation.

', 'DescribeTrainingJobResponse$StoppingCondition' => '

The condition under which to stop the training job.

', 'HyperParameterTrainingJobDefinition$StoppingCondition' => '

Sets a maximum duration for the training jobs that the tuning job launches. Use this parameter to limit model training costs.

To stop a job, Amazon SageMaker sends the algorithm the SIGTERM signal. This delays job termination for 120 seconds. Algorithms might use this 120-second window to save the model artifacts.

When Amazon SageMaker terminates a job because the stopping condition has been met, training algorithms provided by Amazon SageMaker save the intermediate results of the job.

', 'TrainingJob$StoppingCondition' => '

The condition under which to stop the training job.

', 'TrainingJobDefinition$StoppingCondition' => '

Sets a duration for training. Use this parameter to cap model training costs.

To stop a job, Amazon SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds. Algorithms might use this 120-second window to save the model artifacts.

', ], ], 'String' => [ 'base' => NULL, 'refs' => [ 'AlgorithmStatusItem$FailureReason' => '

if the overall status is Failed, the reason for the failure.

', 'ModelPackageStatusItem$FailureReason' => '

if the overall status is Failed, the reason for the failure.

', 'ProductListings$member' => NULL, 'RenderUiTemplateResponse$RenderedContent' => '

A Liquid template that renders the HTML for the worker UI.

', 'RenderingError$Code' => '

A unique identifier for a specific class of errors.

', 'RenderingError$Message' => '

A human-readable message describing the error.

', 'SubscribedWorkteam$SellerName' => '

The name of the vendor in the Amazon Marketplace.

', 'SubscribedWorkteam$ListingId' => '

', 'Workteam$SubDomain' => '

The URI of the labeling job\'s user interface. Workers open this URI to start labeling your data objects.

', ], ], 'String200' => [ 'base' => NULL, 'refs' => [ 'CreateWorkteamRequest$Description' => '

A description of the work team.

', 'SubscribedWorkteam$MarketplaceTitle' => '

The title of the service provided by the vendor in the Amazon Marketplace.

', 'SubscribedWorkteam$MarketplaceDescription' => '

The description of the vendor from the Amazon Marketplace.

', 'UpdateWorkteamRequest$Description' => '

An updated description for the work team.

', 'Workteam$Description' => '

A description of the work team.

', ], ], 'SubnetId' => [ 'base' => NULL, 'refs' => [ 'CreateNotebookInstanceInput$SubnetId' => '

The ID of the subnet in a VPC to which you would like to have a connectivity from your ML compute instance.

', 'DescribeNotebookInstanceOutput$SubnetId' => '

The ID of the VPC subnet.

', 'Subnets$member' => NULL, ], ], 'Subnets' => [ 'base' => NULL, 'refs' => [ 'VpcConfig$Subnets' => '

The ID of the subnets in the VPC to which you want to connect your training job or model.

Amazon EC2 P3 accelerated computing instances are not available in the c/d/e availability zones of region us-east-1. If you want to create endpoints with P3 instances in VPC mode in region us-east-1, create subnets in a/b/f availability zones instead.

', ], ], 'SubscribedWorkteam' => [ 'base' => '

Describes a work team of a vendor that does the a labelling job.

', 'refs' => [ 'DescribeSubscribedWorkteamResponse$SubscribedWorkteam' => '

A Workteam instance that contains information about the work team.

', 'SubscribedWorkteams$member' => NULL, ], ], 'SubscribedWorkteams' => [ 'base' => NULL, 'refs' => [ 'ListSubscribedWorkteamsResponse$SubscribedWorkteams' => '

An array of Workteam objects, each describing a work team.

', ], ], 'Success' => [ 'base' => NULL, 'refs' => [ 'DeleteWorkteamResponse$Success' => '

Returns true if the work team was successfully deleted; otherwise, returns false.

', ], ], 'SuggestionQuery' => [ 'base' => '

Limits the property names that are included in the response.

', 'refs' => [ 'GetSearchSuggestionsRequest$SuggestionQuery' => '

Limits the property names that are included in the response.

', ], ], 'Tag' => [ 'base' => '

Describes a tag.

', 'refs' => [ 'TagList$member' => NULL, ], ], 'TagKey' => [ 'base' => NULL, 'refs' => [ 'Tag$Key' => '

The tag key.

', 'TagKeyList$member' => NULL, ], ], 'TagKeyList' => [ 'base' => NULL, 'refs' => [ 'DeleteTagsInput$TagKeys' => '

An array or one or more tag keys to delete.

', ], ], 'TagList' => [ 'base' => NULL, 'refs' => [ 'AddTagsInput$Tags' => '

An array of Tag objects. Each tag is a key-value pair. Only the key parameter is required. If you don\'t specify a value, Amazon SageMaker sets the value to an empty string.

', 'AddTagsOutput$Tags' => '

A list of tags associated with the Amazon SageMaker resource.

', 'CreateEndpointConfigInput$Tags' => '

A list of key-value pairs. For more information, see Using Cost Allocation Tags in the AWS Billing and Cost Management User Guide.

', 'CreateEndpointInput$Tags' => '

An array of key-value pairs. For more information, see Using Cost Allocation Tagsin the AWS Billing and Cost Management User Guide.

', 'CreateHyperParameterTuningJobRequest$Tags' => '

An array of key-value pairs. You can use tags to categorize your AWS resources in different ways, for example, by purpose, owner, or environment. For more information, see AWS Tagging Strategies.

Tags that you specify for the tuning job are also added to all training jobs that the tuning job launches.

', 'CreateLabelingJobRequest$Tags' => '

An array of key/value pairs. For more information, see Using Cost Allocation Tags in the AWS Billing and Cost Management User Guide.

', 'CreateModelInput$Tags' => '

An array of key-value pairs. For more information, see Using Cost Allocation Tags in the AWS Billing and Cost Management User Guide.

', 'CreateNotebookInstanceInput$Tags' => '

A list of tags to associate with the notebook instance. You can add tags later by using the CreateTags API.

', 'CreateTrainingJobRequest$Tags' => '

An array of key-value pairs. For more information, see Using Cost Allocation Tags in the AWS Billing and Cost Management User Guide.

', 'CreateTransformJobRequest$Tags' => '

(Optional) An array of key-value pairs. For more information, see Using Cost Allocation Tags in the AWS Billing and Cost Management User Guide.

', 'CreateWorkteamRequest$Tags' => '

', 'DescribeLabelingJobResponse$Tags' => '

An array of key/value pairs. For more information, see Using Cost Allocation Tags in the AWS Billing and Cost Management User Guide.

', 'ListTagsOutput$Tags' => '

An array of Tag objects, each with a tag key and a value.

', 'TrainingJob$Tags' => '

An array of key-value pairs. For more information, see Using Cost Allocation Tags in the AWS Billing and Cost Management User Guide.

', ], ], 'TagValue' => [ 'base' => NULL, 'refs' => [ 'Tag$Value' => '

The tag value.

', ], ], 'TargetDevice' => [ 'base' => NULL, 'refs' => [ 'CompilationJobSummary$CompilationTargetDevice' => '

The type of device that the model will run on after compilation has completed.

', 'OutputConfig$TargetDevice' => '

Identifies the device that you want to run your model on after it has been compiled. For example: ml_c5.

', ], ], 'TaskAvailabilityLifetimeInSeconds' => [ 'base' => NULL, 'refs' => [ 'HumanTaskConfig$TaskAvailabilityLifetimeInSeconds' => '

The length of time that a task remains available for labelling by human workers.

', ], ], 'TaskCount' => [ 'base' => NULL, 'refs' => [ 'DesiredWeightAndCapacity$DesiredInstanceCount' => '

The variant\'s capacity.

', 'ProductionVariant$InitialInstanceCount' => '

Number of instances to launch initially.

', 'ProductionVariantSummary$CurrentInstanceCount' => '

The number of instances associated with the variant.

', 'ProductionVariantSummary$DesiredInstanceCount' => '

The number of instances requested in the UpdateEndpointWeightsAndCapacities request.

', ], ], 'TaskDescription' => [ 'base' => NULL, 'refs' => [ 'HumanTaskConfig$TaskDescription' => '

A description of the task for your human workers.

', ], ], 'TaskInput' => [ 'base' => NULL, 'refs' => [ 'RenderableTask$Input' => '

A JSON object that contains values for the variables defined in the template. It is made available to the template under the substitution variable task.input. For example, if you define a variable task.input.text in your template, you can supply the variable in the JSON object as "text": "sample text".

', ], ], 'TaskKeyword' => [ 'base' => NULL, 'refs' => [ 'TaskKeywords$member' => NULL, ], ], 'TaskKeywords' => [ 'base' => NULL, 'refs' => [ 'HumanTaskConfig$TaskKeywords' => '

Keywords used to describe the task so that workers on Amazon Mechanical Turk can discover the task.

', ], ], 'TaskTimeLimitInSeconds' => [ 'base' => NULL, 'refs' => [ 'HumanTaskConfig$TaskTimeLimitInSeconds' => '

The amount of time that a worker has to complete a task.

', ], ], 'TaskTitle' => [ 'base' => NULL, 'refs' => [ 'HumanTaskConfig$TaskTitle' => '

A title for the task for your human workers.

', ], ], 'TemplateContent' => [ 'base' => NULL, 'refs' => [ 'UiTemplate$Content' => '

The content of the Liquid template for the worker user interface.

', ], ], 'TenthFractionsOfACent' => [ 'base' => NULL, 'refs' => [ 'USD$TenthFractionsOfACent' => '

Fractions of a cent, in tenths.

', ], ], 'Timestamp' => [ 'base' => NULL, 'refs' => [ 'CompilationJobSummary$CompilationStartTime' => '

The time when the model compilation job started.

', 'CompilationJobSummary$CompilationEndTime' => '

The time when the model compilation job completed.

', 'DeployedImage$ResolutionTime' => '

The date and time when the image path for the model resolved to the ResolvedImage

', 'DescribeCompilationJobResponse$CompilationStartTime' => '

The time when the model compilation job started the CompilationJob instances.

You are billed for the time between this timestamp and the timestamp in the DescribeCompilationJobResponse$CompilationEndTime field. In Amazon CloudWatch Logs, the start time might be later than this time. That\'s because it takes time to download the compilation job, which depends on the size of the compilation job container.

', 'DescribeCompilationJobResponse$CompilationEndTime' => '

The time when the model compilation job on a compilation job instance ended. For a successful or stopped job, this is when the job\'s model artifacts have finished uploading. For a failed job, this is when Amazon SageMaker detected that the job failed.

', 'DescribeEndpointConfigOutput$CreationTime' => '

A timestamp that shows when the endpoint configuration was created.

', 'DescribeEndpointOutput$CreationTime' => '

A timestamp that shows when the endpoint was created.

', 'DescribeEndpointOutput$LastModifiedTime' => '

A timestamp that shows when the endpoint was last modified.

', 'DescribeHyperParameterTuningJobResponse$CreationTime' => '

The date and time that the tuning job started.

', 'DescribeHyperParameterTuningJobResponse$HyperParameterTuningEndTime' => '

The date and time that the tuning job ended.

', 'DescribeHyperParameterTuningJobResponse$LastModifiedTime' => '

The date and time that the status of the tuning job was modified.

', 'DescribeLabelingJobResponse$CreationTime' => '

The date and time that the labeling job was created.

', 'DescribeLabelingJobResponse$LastModifiedTime' => '

The date and time that the labeling job was last updated.

', 'DescribeModelOutput$CreationTime' => '

A timestamp that shows when the model was created.

', 'DescribeTrainingJobResponse$CreationTime' => '

A timestamp that indicates when the training job was created.

', 'DescribeTrainingJobResponse$TrainingStartTime' => '

Indicates the time when the training job starts on training instances. You are billed for the time interval between this time and the value of TrainingEndTime. The start time in CloudWatch Logs might be later than this time. The difference is due to the time it takes to download the training data and to the size of the training container.

', 'DescribeTrainingJobResponse$TrainingEndTime' => '

Indicates the time when the training job ends on training instances. You are billed for the time interval between the value of TrainingStartTime and this time. For successful jobs and stopped jobs, this is the time after model artifacts are uploaded. For failed jobs, this is the time when Amazon SageMaker detects a job failure.

', 'DescribeTrainingJobResponse$LastModifiedTime' => '

A timestamp that indicates when the status of the training job was last modified.

', 'DescribeTransformJobResponse$CreationTime' => '

A timestamp that shows when the transform Job was created.

', 'DescribeTransformJobResponse$TransformStartTime' => '

Indicates when the transform job starts on ML instances. You are billed for the time interval between this time and the value of TransformEndTime.

', 'DescribeTransformJobResponse$TransformEndTime' => '

Indicates when the transform job has been completed, or has stopped or failed. You are billed for the time interval between this time and the value of TransformStartTime.

', 'EndpointConfigSummary$CreationTime' => '

A timestamp that shows when the endpoint configuration was created.

', 'EndpointSummary$CreationTime' => '

A timestamp that shows when the endpoint was created.

', 'EndpointSummary$LastModifiedTime' => '

A timestamp that shows when the endpoint was last modified.

', 'HyperParameterTrainingJobSummary$CreationTime' => '

The date and time that the training job was created.

', 'HyperParameterTrainingJobSummary$TrainingStartTime' => '

The date and time that the training job started.

', 'HyperParameterTrainingJobSummary$TrainingEndTime' => '

Specifies the time when the training job ends on training instances. You are billed for the time interval between the value of TrainingStartTime and this time. For successful jobs and stopped jobs, this is the time after model artifacts are uploaded. For failed jobs, this is the time when Amazon SageMaker detects a job failure.

', 'HyperParameterTuningJobSummary$CreationTime' => '

The date and time that the tuning job was created.

', 'HyperParameterTuningJobSummary$HyperParameterTuningEndTime' => '

The date and time that the tuning job ended.

', 'HyperParameterTuningJobSummary$LastModifiedTime' => '

The date and time that the tuning job was modified.

', 'LabelingJobForWorkteamSummary$CreationTime' => '

The date and time that the labeling job was created.

', 'LabelingJobSummary$CreationTime' => '

The date and time that the job was created (timestamp).

', 'LabelingJobSummary$LastModifiedTime' => '

The date and time that the job was last modified (timestamp).

', 'ListCodeRepositoriesInput$LastModifiedTimeAfter' => '

A filter that returns only Git repositories that were last modified after the specified time.

', 'ListCodeRepositoriesInput$LastModifiedTimeBefore' => '

A filter that returns only Git repositories that were last modified before the specified time.

', 'ListEndpointConfigsInput$CreationTimeBefore' => '

A filter that returns only endpoint configurations created before the specified time (timestamp).

', 'ListEndpointConfigsInput$CreationTimeAfter' => '

A filter that returns only endpoint configurations with a creation time greater than or equal to the specified time (timestamp).

', 'ListEndpointsInput$CreationTimeBefore' => '

A filter that returns only endpoints that were created before the specified time (timestamp).

', 'ListEndpointsInput$CreationTimeAfter' => '

A filter that returns only endpoints with a creation time greater than or equal to the specified time (timestamp).

', 'ListEndpointsInput$LastModifiedTimeBefore' => '

A filter that returns only endpoints that were modified before the specified timestamp.

', 'ListEndpointsInput$LastModifiedTimeAfter' => '

A filter that returns only endpoints that were modified after the specified timestamp.

', 'ListHyperParameterTuningJobsRequest$CreationTimeAfter' => '

A filter that returns only tuning jobs that were created after the specified time.

', 'ListHyperParameterTuningJobsRequest$CreationTimeBefore' => '

A filter that returns only tuning jobs that were created before the specified time.

', 'ListHyperParameterTuningJobsRequest$LastModifiedTimeAfter' => '

A filter that returns only tuning jobs that were modified after the specified time.

', 'ListHyperParameterTuningJobsRequest$LastModifiedTimeBefore' => '

A filter that returns only tuning jobs that were modified before the specified time.

', 'ListLabelingJobsForWorkteamRequest$CreationTimeAfter' => '

A filter that returns only labeling jobs created after the specified time (timestamp).

', 'ListLabelingJobsForWorkteamRequest$CreationTimeBefore' => '

A filter that returns only labeling jobs created before the specified time (timestamp).

', 'ListLabelingJobsRequest$CreationTimeAfter' => '

A filter that returns only labeling jobs created after the specified time (timestamp).

', 'ListLabelingJobsRequest$CreationTimeBefore' => '

A filter that returns only labeling jobs created before the specified time (timestamp).

', 'ListLabelingJobsRequest$LastModifiedTimeAfter' => '

A filter that returns only labeling jobs modified after the specified time (timestamp).

', 'ListLabelingJobsRequest$LastModifiedTimeBefore' => '

A filter that returns only labeling jobs modified before the specified time (timestamp).

', 'ListModelsInput$CreationTimeBefore' => '

A filter that returns only models created before the specified time (timestamp).

', 'ListModelsInput$CreationTimeAfter' => '

A filter that returns only models with a creation time greater than or equal to the specified time (timestamp).

', 'ListTrainingJobsRequest$CreationTimeAfter' => '

A filter that returns only training jobs created after the specified time (timestamp).

', 'ListTrainingJobsRequest$CreationTimeBefore' => '

A filter that returns only training jobs created before the specified time (timestamp).

', 'ListTrainingJobsRequest$LastModifiedTimeAfter' => '

A filter that returns only training jobs modified after the specified time (timestamp).

', 'ListTrainingJobsRequest$LastModifiedTimeBefore' => '

A filter that returns only training jobs modified before the specified time (timestamp).

', 'ListTransformJobsRequest$CreationTimeAfter' => '

A filter that returns only transform jobs created after the specified time.

', 'ListTransformJobsRequest$CreationTimeBefore' => '

A filter that returns only transform jobs created before the specified time.

', 'ListTransformJobsRequest$LastModifiedTimeAfter' => '

A filter that returns only transform jobs modified after the specified time.

', 'ListTransformJobsRequest$LastModifiedTimeBefore' => '

A filter that returns only transform jobs modified before the specified time.

', 'MetricData$Timestamp' => '

The date and time that the algorithm emitted the metric.

', 'ModelSummary$CreationTime' => '

A timestamp that indicates when the model was created.

', 'SecondaryStatusTransition$StartTime' => '

A timestamp that shows when the training job transitioned to the current secondary status state.

', 'SecondaryStatusTransition$EndTime' => '

A timestamp that shows when the training job transitioned out of this secondary status state into another secondary status state or when the training job has ended.

', 'TrainingJob$CreationTime' => '

A timestamp that indicates when the training job was created.

', 'TrainingJob$TrainingStartTime' => '

Indicates the time when the training job starts on training instances. You are billed for the time interval between this time and the value of TrainingEndTime. The start time in CloudWatch Logs might be later than this time. The difference is due to the time it takes to download the training data and to the size of the training container.

', 'TrainingJob$TrainingEndTime' => '

Indicates the time when the training job ends on training instances. You are billed for the time interval between the value of TrainingStartTime and this time. For successful jobs and stopped jobs, this is the time after model artifacts are uploaded. For failed jobs, this is the time when Amazon SageMaker detects a job failure.

', 'TrainingJob$LastModifiedTime' => '

A timestamp that indicates when the status of the training job was last modified.

', 'TrainingJobSummary$CreationTime' => '

A timestamp that shows when the training job was created.

', 'TrainingJobSummary$TrainingEndTime' => '

A timestamp that shows when the training job ended. This field is set only if the training job has one of the terminal statuses (Completed, Failed, or Stopped).

', 'TrainingJobSummary$LastModifiedTime' => '

Timestamp when the training job was last modified.

', 'TransformJobSummary$CreationTime' => '

A timestamp that shows when the transform Job was created.

', 'TransformJobSummary$TransformEndTime' => '

Indicates when the transform job ends on compute instances. For successful jobs and stopped jobs, this is the exact time recorded after the results are uploaded. For failed jobs, this is when Amazon SageMaker detected that the job failed.

', 'TransformJobSummary$LastModifiedTime' => '

Indicates when the transform job was last modified.

', 'Workteam$CreateDate' => '

The date and time that the work team was created (timestamp).

', 'Workteam$LastUpdatedDate' => '

The date and time that the work team was last updated (timestamp).

', ], ], 'TrainingInputMode' => [ 'base' => NULL, 'refs' => [ 'AlgorithmSpecification$TrainingInputMode' => '

The input mode that the algorithm supports. For the input modes that Amazon SageMaker algorithms support, see Algorithms. If an algorithm supports the File input mode, Amazon SageMaker downloads the training data from S3 to the provisioned ML storage Volume, and mounts the directory to docker volume for training container. If an algorithm supports the Pipe input mode, Amazon SageMaker streams data directly from S3 to the container.

In File mode, make sure you provision ML storage volume with sufficient capacity to accommodate the data download from S3. In addition to the training data, the ML storage volume also stores the output model. The algorithm container use ML storage volume to also store intermediate information, if any.

For distributed algorithms using File mode, training data is distributed uniformly, and your training duration is predictable if the input data objects size is approximately same. Amazon SageMaker does not split the files any further for model training. If the object sizes are skewed, training won\'t be optimal as the data distribution is also skewed where one host in a training cluster is overloaded, thus becoming bottleneck in training.

', 'Channel$InputMode' => '

(Optional) The input mode to use for the data channel in a training job. If you don\'t set a value for InputMode, Amazon SageMaker uses the value set for TrainingInputMode. Use this parameter to override the TrainingInputMode setting in a AlgorithmSpecification request when you have a channel that needs a different input mode from the training job\'s general setting. To download the data from Amazon Simple Storage Service (Amazon S3) to the provisioned ML storage volume, and mount the directory to a Docker volume, use File input mode. To stream data directly from Amazon S3 to the container, choose Pipe input mode.

To use a model for incremental training, choose File input model.

', 'HyperParameterAlgorithmSpecification$TrainingInputMode' => '

The input mode that the algorithm supports: File or Pipe. In File input mode, Amazon SageMaker downloads the training data from Amazon S3 to the storage volume that is attached to the training instance and mounts the directory to the Docker volume for the training container. In Pipe input mode, Amazon SageMaker streams data directly from Amazon S3 to the container.

If you specify File mode, make sure that you provision the storage volume that is attached to the training instance with enough capacity to accommodate the training data downloaded from Amazon S3, the model artifacts, and intermediate information.

For more information about input modes, see Algorithms.

', 'InputModes$member' => NULL, 'TrainingJobDefinition$TrainingInputMode' => '

The input mode used by the algorithm for the training job. For the input modes that Amazon SageMaker algorithms support, see Algorithms.

If an algorithm supports the File input mode, Amazon SageMaker downloads the training data from S3 to the provisioned ML storage Volume, and mounts the directory to docker volume for training container. If an algorithm supports the Pipe input mode, Amazon SageMaker streams data directly from S3 to the container.

', ], ], 'TrainingInstanceCount' => [ 'base' => NULL, 'refs' => [ 'ResourceConfig$InstanceCount' => '

The number of ML compute instances to use. For distributed training, provide a value greater than 1.

', ], ], 'TrainingInstanceType' => [ 'base' => NULL, 'refs' => [ 'ResourceConfig$InstanceType' => '

The ML compute instance type.

', 'TrainingInstanceTypes$member' => NULL, ], ], 'TrainingInstanceTypes' => [ 'base' => NULL, 'refs' => [ 'TrainingSpecification$SupportedTrainingInstanceTypes' => '

A list of the instance types that this algorithm can use for training.

', ], ], 'TrainingJob' => [ 'base' => '

Contains information about a training job.

', 'refs' => [ 'SearchRecord$TrainingJob' => '

A TrainingJob object that is returned as part of a Search request.

', ], ], 'TrainingJobArn' => [ 'base' => NULL, 'refs' => [ 'CreateTrainingJobResponse$TrainingJobArn' => '

The Amazon Resource Name (ARN) of the training job.

', 'DescribeTrainingJobResponse$TrainingJobArn' => '

The Amazon Resource Name (ARN) of the training job.

', 'HyperParameterTrainingJobSummary$TrainingJobArn' => '

The Amazon Resource Name (ARN) of the training job.

', 'TrainingJob$TrainingJobArn' => '

The Amazon Resource Name (ARN) of the training job.

', 'TrainingJobSummary$TrainingJobArn' => '

The Amazon Resource Name (ARN) of the training job.

', ], ], 'TrainingJobDefinition' => [ 'base' => '

Defines the input needed to run a training job using the algorithm.

', 'refs' => [ 'AlgorithmValidationProfile$TrainingJobDefinition' => '

The TrainingJobDefinition object that describes the training job that Amazon SageMaker runs to validate your algorithm.

', ], ], 'TrainingJobEarlyStoppingType' => [ 'base' => NULL, 'refs' => [ 'HyperParameterTuningJobConfig$TrainingJobEarlyStoppingType' => '

Specifies whether to use early stopping for training jobs launched by the hyperparameter tuning job. This can be one of the following values (the default value is OFF):

OFF

Training jobs launched by the hyperparameter tuning job do not use early stopping.

AUTO

Amazon SageMaker stops training jobs launched by the hyperparameter tuning job when they are unlikely to perform better than previously completed training jobs. For more information, see Stop Training Jobs Early.

', ], ], 'TrainingJobName' => [ 'base' => NULL, 'refs' => [ 'CreateTrainingJobRequest$TrainingJobName' => '

The name of the training job. The name must be unique within an AWS Region in an AWS account.

', 'DescribeTrainingJobRequest$TrainingJobName' => '

The name of the training job.

', 'DescribeTrainingJobResponse$TrainingJobName' => '

Name of the model training job.

', 'HyperParameterTrainingJobSummary$TrainingJobName' => '

The name of the training job.

', 'StopTrainingJobRequest$TrainingJobName' => '

The name of the training job to stop.

', 'TrainingJob$TrainingJobName' => '

The name of the training job.

', 'TrainingJobSummary$TrainingJobName' => '

The name of the training job that you want a summary for.

', ], ], 'TrainingJobSortByOptions' => [ 'base' => NULL, 'refs' => [ 'ListTrainingJobsForHyperParameterTuningJobRequest$SortBy' => '

The field to sort results by. The default is Name.

If the value of this field is FinalObjectiveMetricValue, any training jobs that did not return an objective metric are not listed.

', ], ], 'TrainingJobStatus' => [ 'base' => NULL, 'refs' => [ 'DescribeTrainingJobResponse$TrainingJobStatus' => '

The status of the training job.

Amazon SageMaker provides the following training job statuses:

  • InProgress - The training is in progress.

  • Completed - The training job has completed.

  • Failed - The training job has failed. To see the reason for the failure, see the FailureReason field in the response to a DescribeTrainingJobResponse call.

  • Stopping - The training job is stopping.

  • Stopped - The training job has stopped.

For more detailed information, see SecondaryStatus.

', 'HyperParameterTrainingJobSummary$TrainingJobStatus' => '

The status of the training job.

', 'ListTrainingJobsForHyperParameterTuningJobRequest$StatusEquals' => '

A filter that returns only training jobs with the specified status.

', 'ListTrainingJobsRequest$StatusEquals' => '

A filter that retrieves only training jobs with a specific status.

', 'TrainingJob$TrainingJobStatus' => '

The status of the training job.

Training job statuses are:

  • InProgress - The training is in progress.

  • Completed - The training job has completed.

  • Failed - The training job has failed. To see the reason for the failure, see the FailureReason field in the response to a DescribeTrainingJobResponse call.

  • Stopping - The training job is stopping.

  • Stopped - The training job has stopped.

For more detailed information, see SecondaryStatus.

', 'TrainingJobSummary$TrainingJobStatus' => '

The status of the training job.

', ], ], 'TrainingJobStatusCounter' => [ 'base' => NULL, 'refs' => [ 'TrainingJobStatusCounters$Completed' => '

The number of completed training jobs launched by the hyperparameter tuning job.

', 'TrainingJobStatusCounters$InProgress' => '

The number of in-progress training jobs launched by a hyperparameter tuning job.

', 'TrainingJobStatusCounters$RetryableError' => '

The number of training jobs that failed, but can be retried. A failed training job can be retried only if it failed because an internal service error occurred.

', 'TrainingJobStatusCounters$NonRetryableError' => '

The number of training jobs that failed and can\'t be retried. A failed training job can\'t be retried if it failed because a client error occurred.

', 'TrainingJobStatusCounters$Stopped' => '

The number of training jobs launched by a hyperparameter tuning job that were manually stopped.

', ], ], 'TrainingJobStatusCounters' => [ 'base' => '

The numbers of training jobs launched by a hyperparameter tuning job, categorized by status.

', 'refs' => [ 'DescribeHyperParameterTuningJobResponse$TrainingJobStatusCounters' => '

The TrainingJobStatusCounters object that specifies the number of training jobs, categorized by status, that this tuning job launched.

', 'HyperParameterTuningJobSummary$TrainingJobStatusCounters' => '

The TrainingJobStatusCounters object that specifies the numbers of training jobs, categorized by status, that this tuning job launched.

', ], ], 'TrainingJobSummaries' => [ 'base' => NULL, 'refs' => [ 'ListTrainingJobsResponse$TrainingJobSummaries' => '

An array of TrainingJobSummary objects, each listing a training job.

', ], ], 'TrainingJobSummary' => [ 'base' => '

Provides summary information about a training job.

', 'refs' => [ 'TrainingJobSummaries$member' => NULL, ], ], 'TrainingSpecification' => [ 'base' => '

Defines how the algorithm is used for a training job.

', 'refs' => [ 'CreateAlgorithmInput$TrainingSpecification' => '

Specifies details about training jobs run by this algorithm, including the following:

  • The Amazon ECR path of the container and the version digest of the algorithm.

  • The hyperparameters that the algorithm supports.

  • The instance types that the algorithm supports for training.

  • Whether the algorithm supports distributed training.

  • The metrics that the algorithm emits to Amazon CloudWatch.

  • Which metrics that the algorithm emits can be used as the objective metric for hyperparameter tuning jobs.

  • The input channels that the algorithm supports for training data. For example, an algorithm might support train, validation, and test channels.

', 'DescribeAlgorithmOutput$TrainingSpecification' => '

Details about training jobs run by this algorithm.

', ], ], 'TransformDataSource' => [ 'base' => '

Describes the location of the channel data.

', 'refs' => [ 'TransformInput$DataSource' => '

Describes the location of the channel data, which is, the S3 location of the input data that the model can consume.

', ], ], 'TransformEnvironmentKey' => [ 'base' => NULL, 'refs' => [ 'TransformEnvironmentMap$key' => NULL, ], ], 'TransformEnvironmentMap' => [ 'base' => NULL, 'refs' => [ 'CreateTransformJobRequest$Environment' => '

The environment variables to set in the Docker container. We support up to 16 key and values entries in the map.

', 'DescribeTransformJobResponse$Environment' => '

The environment variables to set in the Docker container. We support up to 16 key and values entries in the map.

', 'TransformJobDefinition$Environment' => '

The environment variables to set in the Docker container. We support up to 16 key and values entries in the map.

', ], ], 'TransformEnvironmentValue' => [ 'base' => NULL, 'refs' => [ 'TransformEnvironmentMap$value' => NULL, ], ], 'TransformInput' => [ 'base' => '

Describes the input source of a transform job and the way the transform job consumes it.

', 'refs' => [ 'CreateTransformJobRequest$TransformInput' => '

Describes the input source and the way the transform job consumes it.

', 'DescribeTransformJobResponse$TransformInput' => '

Describes the dataset to be transformed and the Amazon S3 location where it is stored.

', 'TransformJobDefinition$TransformInput' => '

A description of the input source and the way the transform job consumes it.

', ], ], 'TransformInstanceCount' => [ 'base' => NULL, 'refs' => [ 'TransformResources$InstanceCount' => '

The number of ML compute instances to use in the transform job. For distributed transform, provide a value greater than 1. The default value is 1.

', ], ], 'TransformInstanceType' => [ 'base' => NULL, 'refs' => [ 'TransformInstanceTypes$member' => NULL, 'TransformResources$InstanceType' => '

The ML compute instance type for the transform job. For using built-in algorithms to transform moderately sized datasets, ml.m4.xlarge or ml.m5.large should suffice. There is no default value for InstanceType.

', ], ], 'TransformInstanceTypes' => [ 'base' => NULL, 'refs' => [ 'InferenceSpecification$SupportedTransformInstanceTypes' => '

A list of the instance types on which a transformation job can be run or on which an endpoint can be deployed.

', ], ], 'TransformJobArn' => [ 'base' => NULL, 'refs' => [ 'CreateTransformJobResponse$TransformJobArn' => '

The Amazon Resource Name (ARN) of the transform job.

', 'DescribeTransformJobResponse$TransformJobArn' => '

The Amazon Resource Name (ARN) of the transform job.

', 'TransformJobSummary$TransformJobArn' => '

The Amazon Resource Name (ARN) of the transform job.

', ], ], 'TransformJobDefinition' => [ 'base' => '

Defines the input needed to run a transform job using the inference specification specified in the algorithm.

', 'refs' => [ 'AlgorithmValidationProfile$TransformJobDefinition' => '

The TransformJobDefinition object that describes the transform job that Amazon SageMaker runs to validate your algorithm.

', 'ModelPackageValidationProfile$TransformJobDefinition' => '

The TransformJobDefinition object that describes the transform job used for the validation of the model package.

', ], ], 'TransformJobName' => [ 'base' => NULL, 'refs' => [ 'CreateTransformJobRequest$TransformJobName' => '

The name of the transform job. The name must be unique within an AWS Region in an AWS account.

', 'DescribeTransformJobRequest$TransformJobName' => '

The name of the transform job that you want to view details of.

', 'DescribeTransformJobResponse$TransformJobName' => '

The name of the transform job.

', 'StopTransformJobRequest$TransformJobName' => '

The name of the transform job to stop.

', 'TransformJobSummary$TransformJobName' => '

The name of the transform job.

', ], ], 'TransformJobStatus' => [ 'base' => NULL, 'refs' => [ 'DescribeTransformJobResponse$TransformJobStatus' => '

The status of the transform job. If the transform job failed, the reason is returned in the FailureReason field.

', 'ListTransformJobsRequest$StatusEquals' => '

A filter that retrieves only transform jobs with a specific status.

', 'TransformJobSummary$TransformJobStatus' => '

The status of the transform job.

', ], ], 'TransformJobSummaries' => [ 'base' => NULL, 'refs' => [ 'ListTransformJobsResponse$TransformJobSummaries' => '

An array of TransformJobSummary objects.

', ], ], 'TransformJobSummary' => [ 'base' => '

Provides a summary of a transform job. Multiple TransformJobSummary objects are returned as a list after in response to a ListTransformJobs call.

', 'refs' => [ 'TransformJobSummaries$member' => NULL, ], ], 'TransformOutput' => [ 'base' => '

Describes the results of a transform job.

', 'refs' => [ 'CreateTransformJobRequest$TransformOutput' => '

Describes the results of the transform job.

', 'DescribeTransformJobResponse$TransformOutput' => '

Identifies the Amazon S3 location where you want Amazon SageMaker to save the results from the transform job.

', 'TransformJobDefinition$TransformOutput' => '

Identifies the Amazon S3 location where you want Amazon SageMaker to save the results from the transform job.

', ], ], 'TransformResources' => [ 'base' => '

Describes the resources, including ML instance types and ML instance count, to use for transform job.

', 'refs' => [ 'CreateTransformJobRequest$TransformResources' => '

Describes the resources, including ML instance types and ML instance count, to use for the transform job.

', 'DescribeTransformJobResponse$TransformResources' => '

Describes the resources, including ML instance types and ML instance count, to use for the transform job.

', 'TransformJobDefinition$TransformResources' => '

Identifies the ML compute instances for the transform job.

', ], ], 'TransformS3DataSource' => [ 'base' => '

Describes the S3 data source.

', 'refs' => [ 'TransformDataSource$S3DataSource' => '

The S3 location of the data source that is associated with a channel.

', ], ], 'USD' => [ 'base' => '

Represents an amount of money in United States dollars/

', 'refs' => [ 'PublicWorkforceTaskPrice$AmountInUsd' => '

Defines the amount of money paid to a worker in United States dollars.

', ], ], 'UiConfig' => [ 'base' => '

Provided configuration information for the worker UI for a labeling job.

', 'refs' => [ 'HumanTaskConfig$UiConfig' => '

Information about the user interface that workers use to complete the labeling task.

', ], ], 'UiTemplate' => [ 'base' => '

The Liquid template for the worker user interface.

', 'refs' => [ 'RenderUiTemplateRequest$UiTemplate' => '

A Template object containing the worker UI template to render.

', ], ], 'UpdateCodeRepositoryInput' => [ 'base' => NULL, 'refs' => [], ], 'UpdateCodeRepositoryOutput' => [ 'base' => NULL, 'refs' => [], ], 'UpdateEndpointInput' => [ 'base' => NULL, 'refs' => [], ], 'UpdateEndpointOutput' => [ 'base' => NULL, 'refs' => [], ], 'UpdateEndpointWeightsAndCapacitiesInput' => [ 'base' => NULL, 'refs' => [], ], 'UpdateEndpointWeightsAndCapacitiesOutput' => [ 'base' => NULL, 'refs' => [], ], 'UpdateNotebookInstanceInput' => [ 'base' => NULL, 'refs' => [], ], 'UpdateNotebookInstanceLifecycleConfigInput' => [ 'base' => NULL, 'refs' => [], ], 'UpdateNotebookInstanceLifecycleConfigOutput' => [ 'base' => NULL, 'refs' => [], ], 'UpdateNotebookInstanceOutput' => [ 'base' => NULL, 'refs' => [], ], 'UpdateWorkteamRequest' => [ 'base' => NULL, 'refs' => [], ], 'UpdateWorkteamResponse' => [ 'base' => NULL, 'refs' => [], ], 'Url' => [ 'base' => NULL, 'refs' => [ 'ContainerDefinition$ModelDataUrl' => '

The S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix). The S3 path is required for Amazon SageMaker built-in algorithms, but not if you use your own algorithms. For more information on built-in algorithms, see Common Parameters.

If you provide a value for this parameter, Amazon SageMaker uses AWS Security Token Service to download model artifacts from the S3 path you provide. AWS STS is activated in your IAM user account by default. If you previously deactivated AWS STS for a region, you need to reactivate AWS STS for that region. For more information, see Activating and Deactivating AWS STS in an AWS Region in the AWS Identity and Access Management User Guide.

If you use a built-in algorithm to create a model, Amazon SageMaker requires that you provide a S3 path to the model artifacts in ModelDataUrl.

', 'ModelPackageContainerDefinition$ModelDataUrl' => '

The Amazon S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).

', 'SourceAlgorithm$ModelDataUrl' => '

The Amazon S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).

', ], ], 'VariantName' => [ 'base' => NULL, 'refs' => [ 'DesiredWeightAndCapacity$VariantName' => '

The name of the variant to update.

', 'ProductionVariant$VariantName' => '

The name of the production variant.

', 'ProductionVariantSummary$VariantName' => '

The name of the variant.

', ], ], 'VariantWeight' => [ 'base' => NULL, 'refs' => [ 'DesiredWeightAndCapacity$DesiredWeight' => '

The variant\'s weight.

', 'ProductionVariant$InitialVariantWeight' => '

Determines initial traffic distribution among all of the models that you specify in the endpoint configuration. The traffic to a production variant is determined by the ratio of the VariantWeight to the sum of all VariantWeight values across all ProductionVariants. If unspecified, it defaults to 1.0.

', 'ProductionVariantSummary$CurrentWeight' => '

The weight associated with the variant.

', 'ProductionVariantSummary$DesiredWeight' => '

The requested weight, as specified in the UpdateEndpointWeightsAndCapacities request.

', ], ], 'VolumeSizeInGB' => [ 'base' => NULL, 'refs' => [ 'ResourceConfig$VolumeSizeInGB' => '

The size of the ML storage volume that you want to provision.

ML storage volumes store model artifacts and incremental states. Training algorithms might also use the ML storage volume for scratch space. If you want to store the training data in the ML storage volume, choose File as the TrainingInputMode in the algorithm specification.

You must specify sufficient ML storage for your scenario.

Amazon SageMaker supports only the General Purpose SSD (gp2) ML storage volume type.

', ], ], 'VpcConfig' => [ 'base' => '

Specifies a VPC that your training jobs and hosted models have access to. Control access to and from your training and model containers by configuring the VPC. For more information, see Protect Endpoints by Using an Amazon Virtual Private Cloud and Protect Training Jobs by Using an Amazon Virtual Private Cloud.

', 'refs' => [ 'CreateModelInput$VpcConfig' => '

A VpcConfig object that specifies the VPC that you want your model to connect to. Control access to and from your model container by configuring the VPC. VpcConfig is used in hosting services and in batch transform. For more information, see Protect Endpoints by Using an Amazon Virtual Private Cloud and Protect Data in Batch Transform Jobs by Using an Amazon Virtual Private Cloud.

', 'CreateTrainingJobRequest$VpcConfig' => '

A VpcConfig object that specifies the VPC that you want your training job to connect to. Control access to and from your training container by configuring the VPC. For more information, see Protect Training Jobs by Using an Amazon Virtual Private Cloud.

', 'DescribeModelOutput$VpcConfig' => '

A VpcConfig object that specifies the VPC that this model has access to. For more information, see Protect Endpoints by Using an Amazon Virtual Private Cloud

', 'DescribeTrainingJobResponse$VpcConfig' => '

A VpcConfig object that specifies the VPC that this training job has access to. For more information, see Protect Training Jobs by Using an Amazon Virtual Private Cloud.

', 'HyperParameterTrainingJobDefinition$VpcConfig' => '

The VpcConfig object that specifies the VPC that you want the training jobs that this hyperparameter tuning job launches to connect to. Control access to and from your training container by configuring the VPC. For more information, see Protect Training Jobs by Using an Amazon Virtual Private Cloud.

', 'TrainingJob$VpcConfig' => '

A VpcConfig object that specifies the VPC that this training job has access to. For more information, see Protect Training Jobs by Using an Amazon Virtual Private Cloud.

', ], ], 'VpcSecurityGroupIds' => [ 'base' => NULL, 'refs' => [ 'VpcConfig$SecurityGroupIds' => '

The VPC security group IDs, in the form sg-xxxxxxxx. Specify the security groups for the VPC that is specified in the Subnets field.

', ], ], 'Workteam' => [ 'base' => '

Provides details about a labeling work team.

', 'refs' => [ 'DescribeWorkteamResponse$Workteam' => '

A Workteam instance that contains information about the work team.

', 'UpdateWorkteamResponse$Workteam' => '

A Workteam object that describes the updated work team.

', 'Workteams$member' => NULL, ], ], 'WorkteamArn' => [ 'base' => NULL, 'refs' => [ 'CreateWorkteamResponse$WorkteamArn' => '

The Amazon Resource Name (ARN) of the work team. You can use this ARN to identify the work team.

', 'DescribeSubscribedWorkteamRequest$WorkteamArn' => '

The Amazon Resource Name (ARN) of the subscribed work team to describe.

', 'HumanTaskConfig$WorkteamArn' => '

The Amazon Resource Name (ARN) of the work team assigned to complete the tasks.

', 'LabelingJobSummary$WorkteamArn' => '

The Amazon Resource Name (ARN) of the work team assigned to the job.

', 'ListLabelingJobsForWorkteamRequest$WorkteamArn' => '

The Amazon Resource Name (ARN) of the work team for which you want to see labeling jobs for.

', 'SubscribedWorkteam$WorkteamArn' => '

The Amazon Resource Name (ARN) of the vendor that you have subscribed.

', 'Workteam$WorkteamArn' => '

The Amazon Resource Name (ARN) that identifies the work team.

', ], ], 'WorkteamName' => [ 'base' => NULL, 'refs' => [ 'CreateWorkteamRequest$WorkteamName' => '

The name of the work team. Use this name to identify the work team.

', 'DeleteWorkteamRequest$WorkteamName' => '

The name of the work team to delete.

', 'DescribeWorkteamRequest$WorkteamName' => '

The name of the work team to return a description of.

', 'ListSubscribedWorkteamsRequest$NameContains' => '

A string in the work team name. This filter returns only work teams whose name contains the specified string.

', 'ListWorkteamsRequest$NameContains' => '

A string in the work team\'s name. This filter returns only work teams whose name contains the specified string.

', 'UpdateWorkteamRequest$WorkteamName' => '

The name of the work team to update.

', 'Workteam$WorkteamName' => '

The name of the work team.

', ], ], 'Workteams' => [ 'base' => NULL, 'refs' => [ 'ListWorkteamsResponse$Workteams' => '

An array of Workteam objects, each describing a work team.

', ], ], ],]; +return [ 'version' => '2.0', 'service' => '

Provides APIs for creating and managing Amazon SageMaker resources.

', 'operations' => [ 'AddTags' => '

Adds or overwrites one or more tags for the specified Amazon SageMaker resource. You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints.

Each tag consists of a key and an optional value. Tag keys must be unique per resource. For more information about tags, see For more information, see AWS Tagging Strategies.

Tags that you add to a hyperparameter tuning job by calling this API are also added to any training jobs that the hyperparameter tuning job launches after you call this API, but not to training jobs that the hyperparameter tuning job launched before you called this API. To make sure that the tags associated with a hyperparameter tuning job are also added to all training jobs that the hyperparameter tuning job launches, add the tags when you first create the tuning job by specifying them in the Tags parameter of CreateHyperParameterTuningJob

', 'CreateAlgorithm' => '

Create a machine learning algorithm that you can use in Amazon SageMaker and list in the AWS Marketplace.

', 'CreateCodeRepository' => '

Creates a Git repository as a resource in your Amazon SageMaker account. You can associate the repository with notebook instances so that you can use Git source control for the notebooks you create. The Git repository is a resource in your Amazon SageMaker account, so it can be associated with more than one notebook instance, and it persists independently from the lifecycle of any notebook instances it is associated with.

The repository can be hosted either in AWS CodeCommit or in any other Git repository.

', 'CreateCompilationJob' => '

Starts a model compilation job. After the model has been compiled, Amazon SageMaker saves the resulting model artifacts to an Amazon Simple Storage Service (Amazon S3) bucket that you specify.

If you choose to host your model using Amazon SageMaker hosting services, you can use the resulting model artifacts as part of the model. You can also use the artifacts with AWS IoT Greengrass. In that case, deploy them as an ML resource.

In the request body, you provide the following:

  • A name for the compilation job

  • Information about the input model artifacts

  • The output location for the compiled model and the device (target) that the model runs on

  • The Amazon Resource Name (ARN) of the IAM role that Amazon SageMaker assumes to perform the model compilation job

You can also provide a Tag to track the model compilation job\'s resource use and costs. The response body contains the CompilationJobArn for the compiled job.

To stop a model compilation job, use StopCompilationJob. To get information about a particular model compilation job, use DescribeCompilationJob. To get information about multiple model compilation jobs, use ListCompilationJobs.

', 'CreateEndpoint' => '

Creates an endpoint using the endpoint configuration specified in the request. Amazon SageMaker uses the endpoint to provision resources and deploy models. You create the endpoint configuration with the CreateEndpointConfig API.

Use this API only for hosting models using Amazon SageMaker hosting services.

You must not delete an EndpointConfig in use by an endpoint that is live or while the UpdateEndpoint or CreateEndpoint operations are being performed on the endpoint. To update an endpoint, you must create a new EndpointConfig.

The endpoint name must be unique within an AWS Region in your AWS account.

When it receives the request, Amazon SageMaker creates the endpoint, launches the resources (ML compute instances), and deploys the model(s) on them.

When Amazon SageMaker receives the request, it sets the endpoint status to Creating. After it creates the endpoint, it sets the status to InService. Amazon SageMaker can then process incoming requests for inferences. To check the status of an endpoint, use the DescribeEndpoint API.

For an example, see Exercise 1: Using the K-Means Algorithm Provided by Amazon SageMaker.

If any of the models hosted at this endpoint get model data from an Amazon S3 location, Amazon SageMaker uses AWS Security Token Service to download model artifacts from the S3 path you provided. AWS STS is activated in your IAM user account by default. If you previously deactivated AWS STS for a region, you need to reactivate AWS STS for that region. For more information, see Activating and Deactivating AWS STS i an AWS Region in the AWS Identity and Access Management User Guide.

', 'CreateEndpointConfig' => '

Creates an endpoint configuration that Amazon SageMaker hosting services uses to deploy models. In the configuration, you identify one or more models, created using the CreateModel API, to deploy and the resources that you want Amazon SageMaker to provision. Then you call the CreateEndpoint API.

Use this API only if you want to use Amazon SageMaker hosting services to deploy models into production.

In the request, you define one or more ProductionVariants, each of which identifies a model. Each ProductionVariant parameter also describes the resources that you want Amazon SageMaker to provision. This includes the number and type of ML compute instances to deploy.

If you are hosting multiple models, you also assign a VariantWeight to specify how much traffic you want to allocate to each model. For example, suppose that you want to host two models, A and B, and you assign traffic weight 2 for model A and 1 for model B. Amazon SageMaker distributes two-thirds of the traffic to Model A, and one-third to model B.

', 'CreateHyperParameterTuningJob' => '

Starts a hyperparameter tuning job. A hyperparameter tuning job finds the best version of a model by running many training jobs on your dataset using the algorithm you choose and values for hyperparameters within ranges that you specify. It then chooses the hyperparameter values that result in a model that performs the best, as measured by an objective metric that you choose.

', 'CreateLabelingJob' => '

Creates a job that uses workers to label the data objects in your input dataset. You can use the labeled data to train machine learning models.

You can select your workforce from one of three providers:

  • A private workforce that you create. It can include employees, contractors, and outside experts. Use a private workforce when want the data to stay within your organization or when a specific set of skills is required.

  • One or more vendors that you select from the AWS Marketplace. Vendors provide expertise in specific areas.

  • The Amazon Mechanical Turk workforce. This is the largest workforce, but it should only be used for public data or data that has been stripped of any personally identifiable information.

You can also use automated data labeling to reduce the number of data objects that need to be labeled by a human. Automated data labeling uses active learning to determine if a data object can be labeled by machine or if it needs to be sent to a human worker. For more information, see Using Automated Data Labeling.

The data objects to be labeled are contained in an Amazon S3 bucket. You create a manifest file that describes the location of each object. For more information, see Using Input and Output Data.

The output can be used as the manifest file for another labeling job or as training data for your machine learning models.

', 'CreateModel' => '

Creates a model in Amazon SageMaker. In the request, you name the model and describe a primary container. For the primary container, you specify the docker image containing inference code, artifacts (from prior training), and custom environment map that the inference code uses when you deploy the model for predictions.

Use this API to create a model if you want to use Amazon SageMaker hosting services or run a batch transform job.

To host your model, you create an endpoint configuration with the CreateEndpointConfig API, and then create an endpoint with the CreateEndpoint API. Amazon SageMaker then deploys all of the containers that you defined for the model in the hosting environment.

To run a batch transform using your model, you start a job with the CreateTransformJob API. Amazon SageMaker uses your model and your dataset to get inferences which are then saved to a specified S3 location.

In the CreateModel request, you must define a container with the PrimaryContainer parameter.

In the request, you also provide an IAM role that Amazon SageMaker can assume to access model artifacts and docker image for deployment on ML compute hosting instances or for batch transform jobs. In addition, you also use the IAM role to manage permissions the inference code needs. For example, if the inference code access any other AWS resources, you grant necessary permissions via this role.

', 'CreateModelPackage' => '

Creates a model package that you can use to create Amazon SageMaker models or list on AWS Marketplace. Buyers can subscribe to model packages listed on AWS Marketplace to create models in Amazon SageMaker.

To create a model package by specifying a Docker container that contains your inference code and the Amazon S3 location of your model artifacts, provide values for InferenceSpecification. To create a model from an algorithm resource that you created or subscribed to in AWS Marketplace, provide a value for SourceAlgorithmSpecification.

', 'CreateNotebookInstance' => '

Creates an Amazon SageMaker notebook instance. A notebook instance is a machine learning (ML) compute instance running on a Jupyter notebook.

In a CreateNotebookInstance request, specify the type of ML compute instance that you want to run. Amazon SageMaker launches the instance, installs common libraries that you can use to explore datasets for model training, and attaches an ML storage volume to the notebook instance.

Amazon SageMaker also provides a set of example notebooks. Each notebook demonstrates how to use Amazon SageMaker with a specific algorithm or with a machine learning framework.

After receiving the request, Amazon SageMaker does the following:

  1. Creates a network interface in the Amazon SageMaker VPC.

  2. (Option) If you specified SubnetId, Amazon SageMaker creates a network interface in your own VPC, which is inferred from the subnet ID that you provide in the input. When creating this network interface, Amazon SageMaker attaches the security group that you specified in the request to the network interface that it creates in your VPC.

  3. Launches an EC2 instance of the type specified in the request in the Amazon SageMaker VPC. If you specified SubnetId of your VPC, Amazon SageMaker specifies both network interfaces when launching this instance. This enables inbound traffic from your own VPC to the notebook instance, assuming that the security groups allow it.

After creating the notebook instance, Amazon SageMaker returns its Amazon Resource Name (ARN).

After Amazon SageMaker creates the notebook instance, you can connect to the Jupyter server and work in Jupyter notebooks. For example, you can write code to explore a dataset that you can use for model training, train a model, host models by creating Amazon SageMaker endpoints, and validate hosted models.

For more information, see How It Works.

', 'CreateNotebookInstanceLifecycleConfig' => '

Creates a lifecycle configuration that you can associate with a notebook instance. A lifecycle configuration is a collection of shell scripts that run when you create or start a notebook instance.

Each lifecycle configuration script has a limit of 16384 characters.

The value of the $PATH environment variable that is available to both scripts is /sbin:bin:/usr/sbin:/usr/bin.

View CloudWatch Logs for notebook instance lifecycle configurations in log group /aws/sagemaker/NotebookInstances in log stream [notebook-instance-name]/[LifecycleConfigHook].

Lifecycle configuration scripts cannot run for longer than 5 minutes. If a script runs for longer than 5 minutes, it fails and the notebook instance is not created or started.

For information about notebook instance lifestyle configurations, see Step 2.1: (Optional) Customize a Notebook Instance.

', 'CreatePresignedNotebookInstanceUrl' => '

Returns a URL that you can use to connect to the Jupyter server from a notebook instance. In the Amazon SageMaker console, when you choose Open next to a notebook instance, Amazon SageMaker opens a new tab showing the Jupyter server home page from the notebook instance. The console uses this API to get the URL and show the page.

IAM authorization policies for this API are also enforced for every HTTP request and WebSocket frame that attempts to connect to the notebook instance.For example, you can restrict access to this API and to the URL that it returns to a list of IP addresses that you specify. Use the NotIpAddress condition operator and the aws:SourceIP condition context key to specify the list of IP addresses that you want to have access to the notebook instance. For more information, see Limit Access to a Notebook Instance by IP Address.

The URL that you get from a call to is valid only for 5 minutes. If you try to use the URL after the 5-minute limit expires, you are directed to the AWS console sign-in page.

', 'CreateTrainingJob' => '

Starts a model training job. After training completes, Amazon SageMaker saves the resulting model artifacts to an Amazon S3 location that you specify.

If you choose to host your model using Amazon SageMaker hosting services, you can use the resulting model artifacts as part of the model. You can also use the artifacts in a machine learning service other than Amazon SageMaker, provided that you know how to use them for inferences.

In the request body, you provide the following:

  • AlgorithmSpecification - Identifies the training algorithm to use.

  • HyperParameters - Specify these algorithm-specific parameters to enable the estimation of model parameters during training. Hyperparameters can be tuned to optimize this learning process. For a list of hyperparameters for each training algorithm provided by Amazon SageMaker, see Algorithms.

  • InputDataConfig - Describes the training dataset and the Amazon S3 location where it is stored.

  • OutputDataConfig - Identifies the Amazon S3 location where you want Amazon SageMaker to save the results of model training.

  • ResourceConfig - Identifies the resources, ML compute instances, and ML storage volumes to deploy for model training. In distributed training, you specify more than one instance.

  • RoleARN - The Amazon Resource Number (ARN) that Amazon SageMaker assumes to perform tasks on your behalf during model training. You must grant this role the necessary permissions so that Amazon SageMaker can successfully complete model training.

  • StoppingCondition - Sets a time limit for training. Use this parameter to cap model training costs.

For more information about Amazon SageMaker, see How It Works.

', 'CreateTransformJob' => '

Starts a transform job. A transform job uses a trained model to get inferences on a dataset and saves these results to an Amazon S3 location that you specify.

To perform batch transformations, you create a transform job and use the data that you have readily available.

In the request body, you provide the following:

  • TransformJobName - Identifies the transform job. The name must be unique within an AWS Region in an AWS account.

  • ModelName - Identifies the model to use. ModelName must be the name of an existing Amazon SageMaker model in the same AWS Region and AWS account. For information on creating a model, see CreateModel.

  • TransformInput - Describes the dataset to be transformed and the Amazon S3 location where it is stored.

  • TransformOutput - Identifies the Amazon S3 location where you want Amazon SageMaker to save the results from the transform job.

  • TransformResources - Identifies the ML compute instances for the transform job.

For more information about how batch transformation works Amazon SageMaker, see How It Works.

', 'CreateWorkteam' => '

Creates a new work team for labeling your data. A work team is defined by one or more Amazon Cognito user pools. You must first create the user pools before you can create a work team.

You cannot create more than 25 work teams in an account and region.

', 'DeleteAlgorithm' => '

Removes the specified algorithm from your account.

', 'DeleteCodeRepository' => '

Deletes the specified Git repository from your account.

', 'DeleteEndpoint' => '

Deletes an endpoint. Amazon SageMaker frees up all of the resources that were deployed when the endpoint was created.

Amazon SageMaker retires any custom KMS key grants associated with the endpoint, meaning you don\'t need to use the RevokeGrant API call.

', 'DeleteEndpointConfig' => '

Deletes an endpoint configuration. The DeleteEndpointConfig API deletes only the specified configuration. It does not delete endpoints created using the configuration.

', 'DeleteModel' => '

Deletes a model. The DeleteModel API deletes only the model entry that was created in Amazon SageMaker when you called the CreateModel API. It does not delete model artifacts, inference code, or the IAM role that you specified when creating the model.

', 'DeleteModelPackage' => '

Deletes a model package.

A model package is used to create Amazon SageMaker models or list on AWS Marketplace. Buyers can subscribe to model packages listed on AWS Marketplace to create models in Amazon SageMaker.

', 'DeleteNotebookInstance' => '

Deletes an Amazon SageMaker notebook instance. Before you can delete a notebook instance, you must call the StopNotebookInstance API.

When you delete a notebook instance, you lose all of your data. Amazon SageMaker removes the ML compute instance, and deletes the ML storage volume and the network interface associated with the notebook instance.

', 'DeleteNotebookInstanceLifecycleConfig' => '

Deletes a notebook instance lifecycle configuration.

', 'DeleteTags' => '

Deletes the specified tags from an Amazon SageMaker resource.

To list a resource\'s tags, use the ListTags API.

When you call this API to delete tags from a hyperparameter tuning job, the deleted tags are not removed from training jobs that the hyperparameter tuning job launched before you called this API.

', 'DeleteWorkteam' => '

Deletes an existing work team. This operation can\'t be undone.

', 'DescribeAlgorithm' => '

Returns a description of the specified algorithm that is in your account.

', 'DescribeCodeRepository' => '

Gets details about the specified Git repository.

', 'DescribeCompilationJob' => '

Returns information about a model compilation job.

To create a model compilation job, use CreateCompilationJob. To get information about multiple model compilation jobs, use ListCompilationJobs.

', 'DescribeEndpoint' => '

Returns the description of an endpoint.

', 'DescribeEndpointConfig' => '

Returns the description of an endpoint configuration created using the CreateEndpointConfig API.

', 'DescribeHyperParameterTuningJob' => '

Gets a description of a hyperparameter tuning job.

', 'DescribeLabelingJob' => '

Gets information about a labeling job.

', 'DescribeModel' => '

Describes a model that you created using the CreateModel API.

', 'DescribeModelPackage' => '

Returns a description of the specified model package, which is used to create Amazon SageMaker models or list them on AWS Marketplace.

To create models in Amazon SageMaker, buyers can subscribe to model packages listed on AWS Marketplace.

', 'DescribeNotebookInstance' => '

Returns information about a notebook instance.

', 'DescribeNotebookInstanceLifecycleConfig' => '

Returns a description of a notebook instance lifecycle configuration.

For information about notebook instance lifestyle configurations, see Step 2.1: (Optional) Customize a Notebook Instance.

', 'DescribeSubscribedWorkteam' => '

Gets information about a work team provided by a vendor. It returns details about the subscription with a vendor in the AWS Marketplace.

', 'DescribeTrainingJob' => '

Returns information about a training job.

', 'DescribeTransformJob' => '

Returns information about a transform job.

', 'DescribeWorkteam' => '

Gets information about a specific work team. You can see information such as the create date, the last updated date, membership information, and the work team\'s Amazon Resource Name (ARN).

', 'GetSearchSuggestions' => '

An auto-complete API for the search functionality in the Amazon SageMaker console. It returns suggestions of possible matches for the property name to use in Search queries. Provides suggestions for HyperParameters, Tags, and Metrics.

', 'ListAlgorithms' => '

Lists the machine learning algorithms that have been created.

', 'ListCodeRepositories' => '

Gets a list of the Git repositories in your account.

', 'ListCompilationJobs' => '

Lists model compilation jobs that satisfy various filters.

To create a model compilation job, use CreateCompilationJob. To get information about a particular model compilation job you have created, use DescribeCompilationJob.

', 'ListEndpointConfigs' => '

Lists endpoint configurations.

', 'ListEndpoints' => '

Lists endpoints.

', 'ListHyperParameterTuningJobs' => '

Gets a list of HyperParameterTuningJobSummary objects that describe the hyperparameter tuning jobs launched in your account.

', 'ListLabelingJobs' => '

Gets a list of labeling jobs.

', 'ListLabelingJobsForWorkteam' => '

Gets a list of labeling jobs assigned to a specified work team.

', 'ListModelPackages' => '

Lists the model packages that have been created.

', 'ListModels' => '

Lists models created with the CreateModel API.

', 'ListNotebookInstanceLifecycleConfigs' => '

Lists notebook instance lifestyle configurations created with the CreateNotebookInstanceLifecycleConfig API.

', 'ListNotebookInstances' => '

Returns a list of the Amazon SageMaker notebook instances in the requester\'s account in an AWS Region.

', 'ListSubscribedWorkteams' => '

Gets a list of the work teams that you are subscribed to in the AWS Marketplace. The list may be empty if no work team satisfies the filter specified in the NameContains parameter.

', 'ListTags' => '

Returns the tags for the specified Amazon SageMaker resource.

', 'ListTrainingJobs' => '

Lists training jobs.

', 'ListTrainingJobsForHyperParameterTuningJob' => '

Gets a list of TrainingJobSummary objects that describe the training jobs that a hyperparameter tuning job launched.

', 'ListTransformJobs' => '

Lists transform jobs.

', 'ListWorkteams' => '

Gets a list of work teams that you have defined in a region. The list may be empty if no work team satisfies the filter specified in the NameContains parameter.

', 'RenderUiTemplate' => '

Renders the UI template so that you can preview the worker\'s experience.

', 'Search' => '

Finds Amazon SageMaker resources that match a search query. Matching resource objects are returned as a list of SearchResult objects in the response. You can sort the search results by any resource property in a ascending or descending order.

You can query against the following value types: numerical, text, Booleans, and timestamps.

', 'StartNotebookInstance' => '

Launches an ML compute instance with the latest version of the libraries and attaches your ML storage volume. After configuring the notebook instance, Amazon SageMaker sets the notebook instance status to InService. A notebook instance\'s status must be InService before you can connect to your Jupyter notebook.

', 'StopCompilationJob' => '

Stops a model compilation job.

To stop a job, Amazon SageMaker sends the algorithm the SIGTERM signal. This gracefully shuts the job down. If the job hasn\'t stopped, it sends the SIGKILL signal.

When it receives a StopCompilationJob request, Amazon SageMaker changes the CompilationJobSummary$CompilationJobStatus of the job to Stopping. After Amazon SageMaker stops the job, it sets the CompilationJobSummary$CompilationJobStatus to Stopped.

', 'StopHyperParameterTuningJob' => '

Stops a running hyperparameter tuning job and all running training jobs that the tuning job launched.

All model artifacts output from the training jobs are stored in Amazon Simple Storage Service (Amazon S3). All data that the training jobs write to Amazon CloudWatch Logs are still available in CloudWatch. After the tuning job moves to the Stopped state, it releases all reserved resources for the tuning job.

', 'StopLabelingJob' => '

Stops a running labeling job. A job that is stopped cannot be restarted. Any results obtained before the job is stopped are placed in the Amazon S3 output bucket.

', 'StopNotebookInstance' => '

Terminates the ML compute instance. Before terminating the instance, Amazon SageMaker disconnects the ML storage volume from it. Amazon SageMaker preserves the ML storage volume. Amazon SageMaker stops charging you for the ML compute instance when you call StopNotebookInstance.

To access data on the ML storage volume for a notebook instance that has been terminated, call the StartNotebookInstance API. StartNotebookInstance launches another ML compute instance, configures it, and attaches the preserved ML storage volume so you can continue your work.

', 'StopTrainingJob' => '

Stops a training job. To stop a job, Amazon SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds. Algorithms might use this 120-second window to save the model artifacts, so the results of the training is not lost.

When it receives a StopTrainingJob request, Amazon SageMaker changes the status of the job to Stopping. After Amazon SageMaker stops the job, it sets the status to Stopped.

', 'StopTransformJob' => '

Stops a transform job.

When Amazon SageMaker receives a StopTransformJob request, the status of the job changes to Stopping. After Amazon SageMaker stops the job, the status is set to Stopped. When you stop a transform job before it is completed, Amazon SageMaker doesn\'t store the job\'s output in Amazon S3.

', 'UpdateCodeRepository' => '

Updates the specified Git repository with the specified values.

', 'UpdateEndpoint' => '

Deploys the new EndpointConfig specified in the request, switches to using newly created endpoint, and then deletes resources provisioned for the endpoint using the previous EndpointConfig (there is no availability loss).

When Amazon SageMaker receives the request, it sets the endpoint status to Updating. After updating the endpoint, it sets the status to InService. To check the status of an endpoint, use the DescribeEndpoint API.

You must not delete an EndpointConfig in use by an endpoint that is live or while the UpdateEndpoint or CreateEndpoint operations are being performed on the endpoint. To update an endpoint, you must create a new EndpointConfig.

', 'UpdateEndpointWeightsAndCapacities' => '

Updates variant weight of one or more variants associated with an existing endpoint, or capacity of one variant associated with an existing endpoint. When it receives the request, Amazon SageMaker sets the endpoint status to Updating. After updating the endpoint, it sets the status to InService. To check the status of an endpoint, use the DescribeEndpoint API.

', 'UpdateNotebookInstance' => '

Updates a notebook instance. NotebookInstance updates include upgrading or downgrading the ML compute instance used for your notebook instance to accommodate changes in your workload requirements.

', 'UpdateNotebookInstanceLifecycleConfig' => '

Updates a notebook instance lifecycle configuration created with the CreateNotebookInstanceLifecycleConfig API.

', 'UpdateWorkteam' => '

Updates an existing work team with new member definitions or description.

', ], 'shapes' => [ 'Accept' => [ 'base' => NULL, 'refs' => [ 'TransformOutput$Accept' => '

The MIME type used to specify the output data. Amazon SageMaker uses the MIME type with each http call to transfer data from the transform job.

', ], ], 'AccountId' => [ 'base' => NULL, 'refs' => [ 'LabelingJobForWorkteamSummary$WorkRequesterAccountId' => '

', ], ], 'AddTagsInput' => [ 'base' => NULL, 'refs' => [], ], 'AddTagsOutput' => [ 'base' => NULL, 'refs' => [], ], 'AdditionalCodeRepositoryNamesOrUrls' => [ 'base' => NULL, 'refs' => [ 'CreateNotebookInstanceInput$AdditionalCodeRepositories' => '

An array of up to three Git repositories to associate with the notebook instance. These can be either the names of Git repositories stored as resources in your account, or the URL of Git repositories in AWS CodeCommit or in any other Git repository. These repositories are cloned at the same level as the default repository of your notebook instance. For more information, see Associating Git Repositories with Amazon SageMaker Notebook Instances.

', 'DescribeNotebookInstanceOutput$AdditionalCodeRepositories' => '

An array of up to three Git repositories associated with the notebook instance. These can be either the names of Git repositories stored as resources in your account, or the URL of Git repositories in AWS CodeCommit or in any other Git repository. These repositories are cloned at the same level as the default repository of your notebook instance. For more information, see Associating Git Repositories with Amazon SageMaker Notebook Instances.

', 'NotebookInstanceSummary$AdditionalCodeRepositories' => '

An array of up to three Git repositories associated with the notebook instance. These can be either the names of Git repositories stored as resources in your account, or the URL of Git repositories in AWS CodeCommit or in any other Git repository. These repositories are cloned at the same level as the default repository of your notebook instance. For more information, see Associating Git Repositories with Amazon SageMaker Notebook Instances.

', 'UpdateNotebookInstanceInput$AdditionalCodeRepositories' => '

An array of up to three Git repositories to associate with the notebook instance. These can be either the names of Git repositories stored as resources in your account, or the URL of Git repositories in AWS CodeCommit or in any other Git repository. These repositories are cloned at the same level as the default repository of your notebook instance. For more information, see Associating Git Repositories with Amazon SageMaker Notebook Instances.

', ], ], 'AlgorithmArn' => [ 'base' => NULL, 'refs' => [ 'AlgorithmSummary$AlgorithmArn' => '

The Amazon Resource Name (ARN) of the algorithm.

', 'CreateAlgorithmOutput$AlgorithmArn' => '

The Amazon Resource Name (ARN) of the new algorithm.

', 'DescribeAlgorithmOutput$AlgorithmArn' => '

The Amazon Resource Name (ARN) of the algorithm.

', ], ], 'AlgorithmImage' => [ 'base' => NULL, 'refs' => [ 'AlgorithmSpecification$TrainingImage' => '

The registry path of the Docker image that contains the training algorithm. For information about docker registry paths for built-in algorithms, see Algorithms Provided by Amazon SageMaker: Common Parameters. Amazon SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker.

', 'HyperParameterAlgorithmSpecification$TrainingImage' => '

The registry path of the Docker image that contains the training algorithm. For information about Docker registry paths for built-in algorithms, see Algorithms Provided by Amazon SageMaker: Common Parameters. Amazon SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker.

', ], ], 'AlgorithmSortBy' => [ 'base' => NULL, 'refs' => [ 'ListAlgorithmsInput$SortBy' => '

The parameter by which to sort the results. The default is CreationTime.

', ], ], 'AlgorithmSpecification' => [ 'base' => '

Specifies the training algorithm to use in a CreateTrainingJob request.

For more information about algorithms provided by Amazon SageMaker, see Algorithms. For information about using your own algorithms, see Using Your Own Algorithms with Amazon SageMaker.

', 'refs' => [ 'CreateTrainingJobRequest$AlgorithmSpecification' => '

The registry path of the Docker image that contains the training algorithm and algorithm-specific metadata, including the input mode. For more information about algorithms provided by Amazon SageMaker, see Algorithms. For information about providing your own algorithms, see Using Your Own Algorithms with Amazon SageMaker.

', 'DescribeTrainingJobResponse$AlgorithmSpecification' => '

Information about the algorithm used for training, and algorithm metadata.

', 'TrainingJob$AlgorithmSpecification' => '

Information about the algorithm used for training, and algorithm metadata.

', ], ], 'AlgorithmStatus' => [ 'base' => NULL, 'refs' => [ 'AlgorithmSummary$AlgorithmStatus' => '

The overall status of the algorithm.

', 'DescribeAlgorithmOutput$AlgorithmStatus' => '

The current status of the algorithm.

', ], ], 'AlgorithmStatusDetails' => [ 'base' => '

Specifies the validation and image scan statuses of the algorithm.

', 'refs' => [ 'DescribeAlgorithmOutput$AlgorithmStatusDetails' => '

Details about the current status of the algorithm.

', ], ], 'AlgorithmStatusItem' => [ 'base' => '

Represents the overall status of an algorithm.

', 'refs' => [ 'AlgorithmStatusItemList$member' => NULL, ], ], 'AlgorithmStatusItemList' => [ 'base' => NULL, 'refs' => [ 'AlgorithmStatusDetails$ValidationStatuses' => '

The status of algorithm validation.

', 'AlgorithmStatusDetails$ImageScanStatuses' => '

The status of the scan of the algorithm\'s Docker image container.

', ], ], 'AlgorithmSummary' => [ 'base' => '

Provides summary information about an algorithm.

', 'refs' => [ 'AlgorithmSummaryList$member' => NULL, ], ], 'AlgorithmSummaryList' => [ 'base' => NULL, 'refs' => [ 'ListAlgorithmsOutput$AlgorithmSummaryList' => '

>An array of AlgorithmSummary objects, each of which lists an algorithm.

', ], ], 'AlgorithmValidationProfile' => [ 'base' => '

Defines a training job and a batch transform job that Amazon SageMaker runs to validate your algorithm.

The data provided in the validation profile is made available to your buyers on AWS Marketplace.

', 'refs' => [ 'AlgorithmValidationProfiles$member' => NULL, ], ], 'AlgorithmValidationProfiles' => [ 'base' => NULL, 'refs' => [ 'AlgorithmValidationSpecification$ValidationProfiles' => '

An array of AlgorithmValidationProfile objects, each of which specifies a training job and batch transform job that Amazon SageMaker runs to validate your algorithm.

', ], ], 'AlgorithmValidationSpecification' => [ 'base' => '

Specifies configurations for one or more training jobs that Amazon SageMaker runs to test the algorithm.

', 'refs' => [ 'CreateAlgorithmInput$ValidationSpecification' => '

Specifies configurations for one or more training jobs and that Amazon SageMaker runs to test the algorithm\'s training code and, optionally, one or more batch transform jobs that Amazon SageMaker runs to test the algorithm\'s inference code.

', 'DescribeAlgorithmOutput$ValidationSpecification' => '

Details about configurations for one or more training jobs that Amazon SageMaker runs to test the algorithm.

', ], ], 'AnnotationConsolidationConfig' => [ 'base' => '

Configures how labels are consolidated across human workers.

', 'refs' => [ 'HumanTaskConfig$AnnotationConsolidationConfig' => '

Configures how labels are consolidated across human workers.

', ], ], 'ArnOrName' => [ 'base' => NULL, 'refs' => [ 'AlgorithmSpecification$AlgorithmName' => '

The name of the algorithm resource to use for the training job. This must be an algorithm resource that you created or subscribe to on AWS Marketplace. If you specify a value for this parameter, you can\'t specify a value for TrainingImage.

', 'ContainerDefinition$ModelPackageName' => '

The name or Amazon Resource Name (ARN) of the model package to use to create the model.

', 'DescribeAlgorithmInput$AlgorithmName' => '

The name of the algorithm to describe.

', 'DescribeModelPackageInput$ModelPackageName' => '

The name of the model package to describe.

', 'HyperParameterAlgorithmSpecification$AlgorithmName' => '

The name of the resource algorithm to use for the hyperparameter tuning job. If you specify a value for this parameter, do not specify a value for TrainingImage.

', 'SourceAlgorithm$AlgorithmName' => '

The name of an algorithm that was used to create the model package. The algorithm must be either an algorithm resource in your Amazon SageMaker account or an algorithm in AWS Marketplace that you are subscribed to.

', ], ], 'AssemblyType' => [ 'base' => NULL, 'refs' => [ 'TransformOutput$AssembleWith' => '

Defines how to assemble the results of the transform job as a single S3 object. Choose a format that is most convenient to you. To concatenate the results in binary format, specify None. To add a newline character at the end of every transformed record, specify Line.

', ], ], 'AttributeName' => [ 'base' => NULL, 'refs' => [ 'AttributeNames$member' => NULL, ], ], 'AttributeNames' => [ 'base' => NULL, 'refs' => [ 'S3DataSource$AttributeNames' => '

A list of one or more attribute names to use that are found in a specified augmented manifest file.

', ], ], 'BatchStrategy' => [ 'base' => NULL, 'refs' => [ 'CreateTransformJobRequest$BatchStrategy' => '

Specifies the number of records to include in a mini-batch for an HTTP inference request. A record is a single unit of input data that inference can be made on. For example, a single line in a CSV file is a record.

To enable the batch strategy, you must set SplitType to Line, RecordIO, or TFRecord.

To use only one record when making an HTTP invocation request to a container, set BatchStrategy to SingleRecord and SplitType to Line.

To fit as many records in a mini-batch as can fit within the MaxPayloadInMB limit, set BatchStrategy to MultiRecord and SplitType to Line.

', 'DescribeTransformJobResponse$BatchStrategy' => '

Specifies the number of records to include in a mini-batch for an HTTP inference request. A record is a single unit of input data that inference can be made on. For example, a single line in a CSV file is a record.

To enable the batch strategy, you must set SplitType to Line, RecordIO, or TFRecord.

', 'TransformJobDefinition$BatchStrategy' => '

A string that determines the number of records included in a single mini-batch.

SingleRecord means only one record is used per mini-batch. MultiRecord means a mini-batch is set to contain as many records that can fit within the MaxPayloadInMB limit.

', ], ], 'Boolean' => [ 'base' => NULL, 'refs' => [ 'ChannelSpecification$IsRequired' => '

Indicates whether the channel is required by the algorithm.

', 'CreateModelInput$EnableNetworkIsolation' => '

Isolates the model container. No inbound or outbound network calls can be made to or from the model container.

The Semantic Segmentation built-in algorithm does not support network isolation.

', 'CreateTrainingJobRequest$EnableNetworkIsolation' => '

Isolates the training container. No inbound or outbound network calls can be made, except for calls between peers within a training cluster for distributed training. If you enable network isolation for training jobs that are configured to use a VPC, Amazon SageMaker downloads and uploads customer data and model artifacts through the specified VPC, but the training container does not have network access.

The Semantic Segmentation built-in algorithm does not support network isolation.

', 'CreateTrainingJobRequest$EnableInterContainerTrafficEncryption' => '

To encrypt all communications between ML compute instances in distributed training, choose True. Encryption provides greater security for distributed training, but training might take longer. How long it takes depends on the amount of communication between compute instances, especially if you use a deep learning algorithm in distributed training. For more information, see Protect Communications Between ML Compute Instances in a Distributed Training Job.

', 'DescribeModelOutput$EnableNetworkIsolation' => '

If True, no inbound or outbound network calls can be made to or from the model container.

The Semantic Segmentation built-in algorithm does not support network isolation.

', 'DescribeTrainingJobResponse$EnableNetworkIsolation' => '

If you want to allow inbound or outbound network calls, except for calls between peers within a training cluster for distributed training, choose True. If you enable network isolation for training jobs that are configured to use a VPC, Amazon SageMaker downloads and uploads customer data and model artifacts through the specified VPC, but the training container does not have network access.

The Semantic Segmentation built-in algorithm does not support network isolation.

', 'DescribeTrainingJobResponse$EnableInterContainerTrafficEncryption' => '

To encrypt all communications between ML compute instances in distributed training, choose True. Encryption provides greater security for distributed training, but training might take longer. How long it takes depends on the amount of communication between compute instances, especially if you use a deep learning algorithms in distributed training.

', 'HyperParameterSpecification$IsTunable' => '

Indicates whether this hyperparameter is tunable in a hyperparameter tuning job.

', 'HyperParameterSpecification$IsRequired' => '

Indicates whether this hyperparameter is required.

', 'HyperParameterTrainingJobDefinition$EnableNetworkIsolation' => '

Isolates the training container. No inbound or outbound network calls can be made, except for calls between peers within a training cluster for distributed training. If network isolation is used for training jobs that are configured to use a VPC, Amazon SageMaker downloads and uploads customer data and model artifacts through the specified VPC, but the training container does not have network access.

The Semantic Segmentation built-in algorithm does not support network isolation.

', 'HyperParameterTrainingJobDefinition$EnableInterContainerTrafficEncryption' => '

To encrypt all communications between ML compute instances in distributed training, choose True. Encryption provides greater security for distributed training, but training might take longer. How long it takes depends on the amount of communication between compute instances, especially if you use a deep learning algorithm in distributed training.

', 'TrainingJob$EnableNetworkIsolation' => '

If the TrainingJob was created with network isolation, the value is set to true. If network isolation is enabled, nodes can\'t communicate beyond the VPC they run in.

', 'TrainingJob$EnableInterContainerTrafficEncryption' => '

To encrypt all communications between ML compute instances in distributed training, choose True. Encryption provides greater security for distributed training, but training might take longer. How long it takes depends on the amount of communication between compute instances, especially if you use a deep learning algorithm in distributed training.

', 'TrainingSpecification$SupportsDistributedTraining' => '

Indicates whether the algorithm supports distributed training. If set to false, buyers can’t request more than one instance during training.

', ], ], 'BooleanOperator' => [ 'base' => NULL, 'refs' => [ 'SearchExpression$Operator' => '

A Boolean operator used to evaluate the search expression. If you want every conditional statement in all lists to be satisfied for the entire search expression to be true, specify And. If only a single conditional statement needs to be true for the entire search expression to be true, specify Or. The default value is And.

', ], ], 'Branch' => [ 'base' => NULL, 'refs' => [ 'GitConfig$Branch' => '

The default branch for the Git repository.

', ], ], 'CategoricalParameterRange' => [ 'base' => '

A list of categorical hyperparameters to tune.

', 'refs' => [ 'CategoricalParameterRanges$member' => NULL, ], ], 'CategoricalParameterRangeSpecification' => [ 'base' => '

Defines the possible values for a categorical hyperparameter.

', 'refs' => [ 'ParameterRange$CategoricalParameterRangeSpecification' => '

A CategoricalParameterRangeSpecification object that defines the possible values for a categorical hyperparameter.

', ], ], 'CategoricalParameterRanges' => [ 'base' => NULL, 'refs' => [ 'ParameterRanges$CategoricalParameterRanges' => '

The array of CategoricalParameterRange objects that specify ranges of categorical hyperparameters that a hyperparameter tuning job searches.

', ], ], 'Cents' => [ 'base' => NULL, 'refs' => [ 'USD$Cents' => '

The fractional portion, in cents, of the amount.

', ], ], 'CertifyForMarketplace' => [ 'base' => NULL, 'refs' => [ 'CreateAlgorithmInput$CertifyForMarketplace' => '

Whether to certify the algorithm so that it can be listed in AWS Marketplace.

', 'CreateModelPackageInput$CertifyForMarketplace' => '

Whether to certify the model package for listing on AWS Marketplace.

', 'DescribeAlgorithmOutput$CertifyForMarketplace' => '

Whether the algorithm is certified to be listed in AWS Marketplace.

', 'DescribeModelPackageOutput$CertifyForMarketplace' => '

Whether the model package is certified for listing on AWS Marketplace.

', ], ], 'Channel' => [ 'base' => '

A channel is a named input source that training algorithms can consume.

', 'refs' => [ 'InputDataConfig$member' => NULL, ], ], 'ChannelName' => [ 'base' => NULL, 'refs' => [ 'Channel$ChannelName' => '

The name of the channel.

', 'ChannelSpecification$Name' => '

The name of the channel.

', ], ], 'ChannelSpecification' => [ 'base' => '

Defines a named input source, called a channel, to be used by an algorithm.

', 'refs' => [ 'ChannelSpecifications$member' => NULL, ], ], 'ChannelSpecifications' => [ 'base' => NULL, 'refs' => [ 'TrainingSpecification$TrainingChannels' => '

A list of ChannelSpecification objects, which specify the input sources to be used by the algorithm.

', ], ], 'CodeRepositoryArn' => [ 'base' => NULL, 'refs' => [ 'CodeRepositorySummary$CodeRepositoryArn' => '

The Amazon Resource Name (ARN) of the Git repository.

', 'CreateCodeRepositoryOutput$CodeRepositoryArn' => '

The Amazon Resource Name (ARN) of the new repository.

', 'DescribeCodeRepositoryOutput$CodeRepositoryArn' => '

The Amazon Resource Name (ARN) of the Git repository.

', 'UpdateCodeRepositoryOutput$CodeRepositoryArn' => '

The ARN of the Git repository.

', ], ], 'CodeRepositoryContains' => [ 'base' => NULL, 'refs' => [ 'ListNotebookInstancesInput$DefaultCodeRepositoryContains' => '

A string in the name or URL of a Git repository associated with this notebook instance. This filter returns only notebook instances associated with a git repository with a name that contains the specified string.

', ], ], 'CodeRepositoryNameContains' => [ 'base' => NULL, 'refs' => [ 'ListCodeRepositoriesInput$NameContains' => '

A string in the Git repositories name. This filter returns only repositories whose name contains the specified string.

', ], ], 'CodeRepositoryNameOrUrl' => [ 'base' => NULL, 'refs' => [ 'AdditionalCodeRepositoryNamesOrUrls$member' => NULL, 'CreateNotebookInstanceInput$DefaultCodeRepository' => '

A Git repository to associate with the notebook instance as its default code repository. This can be either the name of a Git repository stored as a resource in your account, or the URL of a Git repository in AWS CodeCommit or in any other Git repository. When you open a notebook instance, it opens in the directory that contains this repository. For more information, see Associating Git Repositories with Amazon SageMaker Notebook Instances.

', 'DescribeNotebookInstanceOutput$DefaultCodeRepository' => '

The Git repository associated with the notebook instance as its default code repository. This can be either the name of a Git repository stored as a resource in your account, or the URL of a Git repository in AWS CodeCommit or in any other Git repository. When you open a notebook instance, it opens in the directory that contains this repository. For more information, see Associating Git Repositories with Amazon SageMaker Notebook Instances.

', 'ListNotebookInstancesInput$AdditionalCodeRepositoryEquals' => '

A filter that returns only notebook instances with associated with the specified git repository.

', 'NotebookInstanceSummary$DefaultCodeRepository' => '

The Git repository associated with the notebook instance as its default code repository. This can be either the name of a Git repository stored as a resource in your account, or the URL of a Git repository in AWS CodeCommit or in any other Git repository. When you open a notebook instance, it opens in the directory that contains this repository. For more information, see Associating Git Repositories with Amazon SageMaker Notebook Instances.

', 'UpdateNotebookInstanceInput$DefaultCodeRepository' => '

The Git repository to associate with the notebook instance as its default code repository. This can be either the name of a Git repository stored as a resource in your account, or the URL of a Git repository in AWS CodeCommit or in any other Git repository. When you open a notebook instance, it opens in the directory that contains this repository. For more information, see Associating Git Repositories with Amazon SageMaker Notebook Instances.

', ], ], 'CodeRepositorySortBy' => [ 'base' => NULL, 'refs' => [ 'ListCodeRepositoriesInput$SortBy' => '

The field to sort results by. The default is Name.

', ], ], 'CodeRepositorySortOrder' => [ 'base' => NULL, 'refs' => [ 'ListCodeRepositoriesInput$SortOrder' => '

The sort order for results. The default is Ascending.

', ], ], 'CodeRepositorySummary' => [ 'base' => '

Specifies summary information about a Git repository.

', 'refs' => [ 'CodeRepositorySummaryList$member' => NULL, ], ], 'CodeRepositorySummaryList' => [ 'base' => NULL, 'refs' => [ 'ListCodeRepositoriesOutput$CodeRepositorySummaryList' => '

Gets a list of summaries of the Git repositories. Each summary specifies the following values for the repository:

  • Name

  • Amazon Resource Name (ARN)

  • Creation time

  • Last modified time

  • Configuration information, including the URL location of the repository and the ARN of the AWS Secrets Manager secret that contains the credentials used to access the repository.

', ], ], 'CognitoClientId' => [ 'base' => NULL, 'refs' => [ 'CognitoMemberDefinition$ClientId' => '

An identifier for an application client. You must create the app client ID using Amazon Cognito.

', ], ], 'CognitoMemberDefinition' => [ 'base' => '

Identifies a Amazon Cognito user group. A user group can be used in on or more work teams.

', 'refs' => [ 'MemberDefinition$CognitoMemberDefinition' => '

The Amazon Cognito user group that is part of the work team.

', ], ], 'CognitoUserGroup' => [ 'base' => NULL, 'refs' => [ 'CognitoMemberDefinition$UserGroup' => '

An identifier for a user group.

', ], ], 'CognitoUserPool' => [ 'base' => NULL, 'refs' => [ 'CognitoMemberDefinition$UserPool' => '

An identifier for a user pool. The user pool must be in the same region as the service that you are calling.

', ], ], 'CompilationJobArn' => [ 'base' => NULL, 'refs' => [ 'CompilationJobSummary$CompilationJobArn' => '

The Amazon Resource Name (ARN) of the model compilation job.

', 'CreateCompilationJobResponse$CompilationJobArn' => '

If the action is successful, the service sends back an HTTP 200 response. Amazon SageMaker returns the following data in JSON format:

  • CompilationJobArn: The Amazon Resource Name (ARN) of the compiled job.

', 'DescribeCompilationJobResponse$CompilationJobArn' => '

The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker assumes to perform the model compilation job.

', ], ], 'CompilationJobStatus' => [ 'base' => NULL, 'refs' => [ 'CompilationJobSummary$CompilationJobStatus' => '

The status of the model compilation job.

', 'DescribeCompilationJobResponse$CompilationJobStatus' => '

The status of the model compilation job.

', 'ListCompilationJobsRequest$StatusEquals' => '

A filter that retrieves model compilation jobs with a specific DescribeCompilationJobResponse$CompilationJobStatus status.

', ], ], 'CompilationJobSummaries' => [ 'base' => NULL, 'refs' => [ 'ListCompilationJobsResponse$CompilationJobSummaries' => '

An array of CompilationJobSummary objects, each describing a model compilation job.

', ], ], 'CompilationJobSummary' => [ 'base' => '

A summary of a model compilation job.

', 'refs' => [ 'CompilationJobSummaries$member' => NULL, ], ], 'CompressionType' => [ 'base' => NULL, 'refs' => [ 'Channel$CompressionType' => '

If training data is compressed, the compression type. The default value is None. CompressionType is used only in Pipe input mode. In File mode, leave this field unset or set it to None.

', 'CompressionTypes$member' => NULL, 'TransformInput$CompressionType' => '

If your transform data is compressed, specify the compression type. Amazon SageMaker automatically decompresses the data for the transform job accordingly. The default value is None.

', ], ], 'CompressionTypes' => [ 'base' => NULL, 'refs' => [ 'ChannelSpecification$SupportedCompressionTypes' => '

The allowed compression types, if data compression is used.

', ], ], 'ContainerDefinition' => [ 'base' => '

Describes the container, as part of model definition.

', 'refs' => [ 'ContainerDefinitionList$member' => NULL, 'CreateModelInput$PrimaryContainer' => '

The location of the primary docker image containing inference code, associated artifacts, and custom environment map that the inference code uses when the model is deployed for predictions.

', 'DescribeModelOutput$PrimaryContainer' => '

The location of the primary inference code, associated artifacts, and custom environment map that the inference code uses when it is deployed in production.

', ], ], 'ContainerDefinitionList' => [ 'base' => NULL, 'refs' => [ 'CreateModelInput$Containers' => '

Specifies the containers in the inference pipeline.

', 'DescribeModelOutput$Containers' => '

The containers in the inference pipeline.

', ], ], 'ContainerHostname' => [ 'base' => NULL, 'refs' => [ 'ContainerDefinition$ContainerHostname' => '

This parameter is ignored for models that contain only a PrimaryContainer.

When a ContainerDefinition is part of an inference pipeline, the value of ths parameter uniquely identifies the container for the purposes of logging and metrics. For information, see Use Logs and Metrics to Monitor an Inference Pipeline. If you don\'t specify a value for this parameter for a ContainerDefinition that is part of an inference pipeline, a unique name is automatically assigned based on the position of the ContainerDefinition in the pipeline. If you specify a value for the ContainerHostName for any ContainerDefinition that is part of an inference pipeline, you must specify a value for the ContainerHostName parameter of every ContainerDefinition in that pipeline.

', 'ModelPackageContainerDefinition$ContainerHostname' => '

The DNS host name for the Docker container.

', ], ], 'ContentClassifier' => [ 'base' => NULL, 'refs' => [ 'ContentClassifiers$member' => NULL, ], ], 'ContentClassifiers' => [ 'base' => NULL, 'refs' => [ 'LabelingJobDataAttributes$ContentClassifiers' => '

Declares that your content is free of personally identifiable information or adult content. Amazon SageMaker may restrict the Amazon Mechanical Turk workers that can view your task based on this information.

', ], ], 'ContentType' => [ 'base' => NULL, 'refs' => [ 'Channel$ContentType' => '

The MIME type of the data.

', 'ContentTypes$member' => NULL, 'TransformInput$ContentType' => '

The multipurpose internet mail extension (MIME) type of the data. Amazon SageMaker uses the MIME type with each http call to transfer data to the transform job.

', ], ], 'ContentTypes' => [ 'base' => NULL, 'refs' => [ 'ChannelSpecification$SupportedContentTypes' => '

The supported MIME types for the data.

', 'InferenceSpecification$SupportedContentTypes' => '

The supported MIME types for the input data.

', ], ], 'ContinuousParameterRange' => [ 'base' => '

A list of continuous hyperparameters to tune.

', 'refs' => [ 'ContinuousParameterRanges$member' => NULL, ], ], 'ContinuousParameterRangeSpecification' => [ 'base' => '

Defines the possible values for a continuous hyperparameter.

', 'refs' => [ 'ParameterRange$ContinuousParameterRangeSpecification' => '

A ContinuousParameterRangeSpecification object that defines the possible values for a continuous hyperparameter.

', ], ], 'ContinuousParameterRanges' => [ 'base' => NULL, 'refs' => [ 'ParameterRanges$ContinuousParameterRanges' => '

The array of ContinuousParameterRange objects that specify ranges of continuous hyperparameters that a hyperparameter tuning job searches.

', ], ], 'CreateAlgorithmInput' => [ 'base' => NULL, 'refs' => [], ], 'CreateAlgorithmOutput' => [ 'base' => NULL, 'refs' => [], ], 'CreateCodeRepositoryInput' => [ 'base' => NULL, 'refs' => [], ], 'CreateCodeRepositoryOutput' => [ 'base' => NULL, 'refs' => [], ], 'CreateCompilationJobRequest' => [ 'base' => NULL, 'refs' => [], ], 'CreateCompilationJobResponse' => [ 'base' => NULL, 'refs' => [], ], 'CreateEndpointConfigInput' => [ 'base' => NULL, 'refs' => [], ], 'CreateEndpointConfigOutput' => [ 'base' => NULL, 'refs' => [], ], 'CreateEndpointInput' => [ 'base' => NULL, 'refs' => [], ], 'CreateEndpointOutput' => [ 'base' => NULL, 'refs' => [], ], 'CreateHyperParameterTuningJobRequest' => [ 'base' => NULL, 'refs' => [], ], 'CreateHyperParameterTuningJobResponse' => [ 'base' => NULL, 'refs' => [], ], 'CreateLabelingJobRequest' => [ 'base' => NULL, 'refs' => [], ], 'CreateLabelingJobResponse' => [ 'base' => NULL, 'refs' => [], ], 'CreateModelInput' => [ 'base' => NULL, 'refs' => [], ], 'CreateModelOutput' => [ 'base' => NULL, 'refs' => [], ], 'CreateModelPackageInput' => [ 'base' => NULL, 'refs' => [], ], 'CreateModelPackageOutput' => [ 'base' => NULL, 'refs' => [], ], 'CreateNotebookInstanceInput' => [ 'base' => NULL, 'refs' => [], ], 'CreateNotebookInstanceLifecycleConfigInput' => [ 'base' => NULL, 'refs' => [], ], 'CreateNotebookInstanceLifecycleConfigOutput' => [ 'base' => NULL, 'refs' => [], ], 'CreateNotebookInstanceOutput' => [ 'base' => NULL, 'refs' => [], ], 'CreatePresignedNotebookInstanceUrlInput' => [ 'base' => NULL, 'refs' => [], ], 'CreatePresignedNotebookInstanceUrlOutput' => [ 'base' => NULL, 'refs' => [], ], 'CreateTrainingJobRequest' => [ 'base' => NULL, 'refs' => [], ], 'CreateTrainingJobResponse' => [ 'base' => NULL, 'refs' => [], ], 'CreateTransformJobRequest' => [ 'base' => NULL, 'refs' => [], ], 'CreateTransformJobResponse' => [ 'base' => NULL, 'refs' => [], ], 'CreateWorkteamRequest' => [ 'base' => NULL, 'refs' => [], ], 'CreateWorkteamResponse' => [ 'base' => NULL, 'refs' => [], ], 'CreationTime' => [ 'base' => NULL, 'refs' => [ 'AlgorithmSummary$CreationTime' => '

A timestamp that shows when the algorithm was created.

', 'CodeRepositorySummary$CreationTime' => '

The date and time that the Git repository was created.

', 'CompilationJobSummary$CreationTime' => '

The time when the model compilation job was created.

', 'DescribeAlgorithmOutput$CreationTime' => '

A timestamp specifying when the algorithm was created.

', 'DescribeCodeRepositoryOutput$CreationTime' => '

The date and time that the repository was created.

', 'DescribeCompilationJobResponse$CreationTime' => '

The time that the model compilation job was created.

', 'DescribeModelPackageOutput$CreationTime' => '

A timestamp specifying when the model package was created.

', 'DescribeNotebookInstanceLifecycleConfigOutput$CreationTime' => '

A timestamp that tells when the lifecycle configuration was created.

', 'DescribeNotebookInstanceOutput$CreationTime' => '

A timestamp. Use this parameter to return the time when the notebook instance was created

', 'ListAlgorithmsInput$CreationTimeAfter' => '

A filter that returns only algorithms created after the specified time (timestamp).

', 'ListAlgorithmsInput$CreationTimeBefore' => '

A filter that returns only algorithms created before the specified time (timestamp).

', 'ListCodeRepositoriesInput$CreationTimeAfter' => '

A filter that returns only Git repositories that were created after the specified time.

', 'ListCodeRepositoriesInput$CreationTimeBefore' => '

A filter that returns only Git repositories that were created before the specified time.

', 'ListCompilationJobsRequest$CreationTimeAfter' => '

A filter that returns the model compilation jobs that were created after a specified time.

', 'ListCompilationJobsRequest$CreationTimeBefore' => '

A filter that returns the model compilation jobs that were created before a specified time.

', 'ListModelPackagesInput$CreationTimeAfter' => '

A filter that returns only model packages created after the specified time (timestamp).

', 'ListModelPackagesInput$CreationTimeBefore' => '

A filter that returns only model packages created before the specified time (timestamp).

', 'ListNotebookInstanceLifecycleConfigsInput$CreationTimeBefore' => '

A filter that returns only lifecycle configurations that were created before the specified time (timestamp).

', 'ListNotebookInstanceLifecycleConfigsInput$CreationTimeAfter' => '

A filter that returns only lifecycle configurations that were created after the specified time (timestamp).

', 'ListNotebookInstancesInput$CreationTimeBefore' => '

A filter that returns only notebook instances that were created before the specified time (timestamp).

', 'ListNotebookInstancesInput$CreationTimeAfter' => '

A filter that returns only notebook instances that were created after the specified time (timestamp).

', 'ModelPackageSummary$CreationTime' => '

A timestamp that shows when the model package was created.

', 'NotebookInstanceLifecycleConfigSummary$CreationTime' => '

A timestamp that tells when the lifecycle configuration was created.

', 'NotebookInstanceSummary$CreationTime' => '

A timestamp that shows when the notebook instance was created.

', ], ], 'DataInputConfig' => [ 'base' => NULL, 'refs' => [ 'InputConfig$DataInputConfig' => '

Specifies the name and shape of the expected data inputs for your trained model with a JSON dictionary form. The data inputs are InputConfig$Framework specific.

  • TensorFlow: You must specify the name and shape (NHWC format) of the expected data inputs using a dictionary format for your trained model. The dictionary formats required for the console and CLI are different.

    • Examples for one input:

      • If using the console, {"input":[1,1024,1024,3]}

      • If using the CLI, {\\"input\\":[1,1024,1024,3]}

    • Examples for two inputs:

      • If using the console, {"data1": [1,28,28,1], "data2":[1,28,28,1]}

      • If using the CLI, {\\"data1\\": [1,28,28,1], \\"data2\\":[1,28,28,1]}

  • MXNET/ONNX: You must specify the name and shape (NCHW format) of the expected data inputs in order using a dictionary format for your trained model. The dictionary formats required for the console and CLI are different.

    • Examples for one input:

      • If using the console, {"data":[1,3,1024,1024]}

      • If using the CLI, {\\"data\\":[1,3,1024,1024]}

    • Examples for two inputs:

      • If using the console, {"var1": [1,1,28,28], "var2":[1,1,28,28]}

      • If using the CLI, {\\"var1\\": [1,1,28,28], \\"var2\\":[1,1,28,28]}

  • PyTorch: You can either specify the name and shape (NCHW format) of expected data inputs in order using a dictionary format for your trained model or you can specify the shape only using a list format. The dictionary formats required for the console and CLI are different. The list formats for the console and CLI are the same.

    • Examples for one input in dictionary format:

      • If using the console, {"input0":[1,3,224,224]}

      • If using the CLI, {\\"input0\\":[1,3,224,224]}

    • Example for one input in list format: [[1,3,224,224]]

    • Examples for two inputs in dictionary format:

      • If using the console, {"input0":[1,3,224,224], "input1":[1,3,224,224]}

      • If using the CLI, {\\"input0\\":[1,3,224,224], \\"input1\\":[1,3,224,224]}

    • Example for two inputs in list format: [[1,3,224,224], [1,3,224,224]]

  • XGBOOST: input data name and shape are not needed.

', ], ], 'DataProcessing' => [ 'base' => '

The data structure used to combine the input data and transformed data from the batch transform output into a joined dataset and to store it in an output file. It also contains information on how to filter the input data and the joined dataset. For more information, see Batch Transform I/O Join.

', 'refs' => [ 'CreateTransformJobRequest$DataProcessing' => '

The data structure used for combining the input data and inference in the output file. For more information, see Batch Transform I/O Join.

', 'DescribeTransformJobResponse$DataProcessing' => NULL, ], ], 'DataSource' => [ 'base' => '

Describes the location of the channel data.

', 'refs' => [ 'Channel$DataSource' => '

The location of the channel data.

', ], ], 'DeleteAlgorithmInput' => [ 'base' => NULL, 'refs' => [], ], 'DeleteCodeRepositoryInput' => [ 'base' => NULL, 'refs' => [], ], 'DeleteEndpointConfigInput' => [ 'base' => NULL, 'refs' => [], ], 'DeleteEndpointInput' => [ 'base' => NULL, 'refs' => [], ], 'DeleteModelInput' => [ 'base' => NULL, 'refs' => [], ], 'DeleteModelPackageInput' => [ 'base' => NULL, 'refs' => [], ], 'DeleteNotebookInstanceInput' => [ 'base' => NULL, 'refs' => [], ], 'DeleteNotebookInstanceLifecycleConfigInput' => [ 'base' => NULL, 'refs' => [], ], 'DeleteTagsInput' => [ 'base' => NULL, 'refs' => [], ], 'DeleteTagsOutput' => [ 'base' => NULL, 'refs' => [], ], 'DeleteWorkteamRequest' => [ 'base' => NULL, 'refs' => [], ], 'DeleteWorkteamResponse' => [ 'base' => NULL, 'refs' => [], ], 'DeployedImage' => [ 'base' => '

Gets the Amazon EC2 Container Registry path of the docker image of the model that is hosted in this ProductionVariant.

If you used the registry/repository[:tag] form to specify the image path of the primary container when you created the model hosted in this ProductionVariant, the path resolves to a path of the form registry/repository[@digest]. A digest is a hash value that identifies a specific version of an image. For information about Amazon ECR paths, see Pulling an Image in the Amazon ECR User Guide.

', 'refs' => [ 'DeployedImages$member' => NULL, ], ], 'DeployedImages' => [ 'base' => NULL, 'refs' => [ 'ProductionVariantSummary$DeployedImages' => '

An array of DeployedImage objects that specify the Amazon EC2 Container Registry paths of the inference images deployed on instances of this ProductionVariant.

', ], ], 'DescribeAlgorithmInput' => [ 'base' => NULL, 'refs' => [], ], 'DescribeAlgorithmOutput' => [ 'base' => NULL, 'refs' => [], ], 'DescribeCodeRepositoryInput' => [ 'base' => NULL, 'refs' => [], ], 'DescribeCodeRepositoryOutput' => [ 'base' => NULL, 'refs' => [], ], 'DescribeCompilationJobRequest' => [ 'base' => NULL, 'refs' => [], ], 'DescribeCompilationJobResponse' => [ 'base' => NULL, 'refs' => [], ], 'DescribeEndpointConfigInput' => [ 'base' => NULL, 'refs' => [], ], 'DescribeEndpointConfigOutput' => [ 'base' => NULL, 'refs' => [], ], 'DescribeEndpointInput' => [ 'base' => NULL, 'refs' => [], ], 'DescribeEndpointOutput' => [ 'base' => NULL, 'refs' => [], ], 'DescribeHyperParameterTuningJobRequest' => [ 'base' => NULL, 'refs' => [], ], 'DescribeHyperParameterTuningJobResponse' => [ 'base' => NULL, 'refs' => [], ], 'DescribeLabelingJobRequest' => [ 'base' => NULL, 'refs' => [], ], 'DescribeLabelingJobResponse' => [ 'base' => NULL, 'refs' => [], ], 'DescribeModelInput' => [ 'base' => NULL, 'refs' => [], ], 'DescribeModelOutput' => [ 'base' => NULL, 'refs' => [], ], 'DescribeModelPackageInput' => [ 'base' => NULL, 'refs' => [], ], 'DescribeModelPackageOutput' => [ 'base' => NULL, 'refs' => [], ], 'DescribeNotebookInstanceInput' => [ 'base' => NULL, 'refs' => [], ], 'DescribeNotebookInstanceLifecycleConfigInput' => [ 'base' => NULL, 'refs' => [], ], 'DescribeNotebookInstanceLifecycleConfigOutput' => [ 'base' => NULL, 'refs' => [], ], 'DescribeNotebookInstanceOutput' => [ 'base' => NULL, 'refs' => [], ], 'DescribeSubscribedWorkteamRequest' => [ 'base' => NULL, 'refs' => [], ], 'DescribeSubscribedWorkteamResponse' => [ 'base' => NULL, 'refs' => [], ], 'DescribeTrainingJobRequest' => [ 'base' => NULL, 'refs' => [], ], 'DescribeTrainingJobResponse' => [ 'base' => NULL, 'refs' => [], ], 'DescribeTransformJobRequest' => [ 'base' => NULL, 'refs' => [], ], 'DescribeTransformJobResponse' => [ 'base' => NULL, 'refs' => [], ], 'DescribeWorkteamRequest' => [ 'base' => NULL, 'refs' => [], ], 'DescribeWorkteamResponse' => [ 'base' => NULL, 'refs' => [], ], 'DesiredWeightAndCapacity' => [ 'base' => '

Specifies weight and capacity values for a production variant.

', 'refs' => [ 'DesiredWeightAndCapacityList$member' => NULL, ], ], 'DesiredWeightAndCapacityList' => [ 'base' => NULL, 'refs' => [ 'UpdateEndpointWeightsAndCapacitiesInput$DesiredWeightsAndCapacities' => '

An object that provides new capacity and weight values for a variant.

', ], ], 'DetailedAlgorithmStatus' => [ 'base' => NULL, 'refs' => [ 'AlgorithmStatusItem$Status' => '

The current status.

', ], ], 'DetailedModelPackageStatus' => [ 'base' => NULL, 'refs' => [ 'ModelPackageStatusItem$Status' => '

The current status.

', ], ], 'DirectInternetAccess' => [ 'base' => NULL, 'refs' => [ 'CreateNotebookInstanceInput$DirectInternetAccess' => '

Sets whether Amazon SageMaker provides internet access to the notebook instance. If you set this to Disabled this notebook instance will be able to access resources only in your VPC, and will not be able to connect to Amazon SageMaker training and endpoint services unless your configure a NAT Gateway in your VPC.

For more information, see Notebook Instances Are Internet-Enabled by Default. You can set the value of this parameter to Disabled only if you set a value for the SubnetId parameter.

', 'DescribeNotebookInstanceOutput$DirectInternetAccess' => '

Describes whether Amazon SageMaker provides internet access to the notebook instance. If this value is set to Disabled, the notebook instance does not have internet access, and cannot connect to Amazon SageMaker training and endpoint services.

For more information, see Notebook Instances Are Internet-Enabled by Default.

', ], ], 'DisassociateAdditionalCodeRepositories' => [ 'base' => NULL, 'refs' => [ 'UpdateNotebookInstanceInput$DisassociateAdditionalCodeRepositories' => '

A list of names or URLs of the default Git repositories to remove from this notebook instance. This operation is idempotent. If you specify a Git repository that is not associated with the notebook instance when you call this method, it does not throw an error.

', ], ], 'DisassociateDefaultCodeRepository' => [ 'base' => NULL, 'refs' => [ 'UpdateNotebookInstanceInput$DisassociateDefaultCodeRepository' => '

The name or URL of the default Git repository to remove from this notebook instance. This operation is idempotent. If you specify a Git repository that is not associated with the notebook instance when you call this method, it does not throw an error.

', ], ], 'DisassociateNotebookInstanceAcceleratorTypes' => [ 'base' => NULL, 'refs' => [ 'UpdateNotebookInstanceInput$DisassociateAcceleratorTypes' => '

A list of the Elastic Inference (EI) instance types to remove from this notebook instance. This operation is idempotent. If you specify an accelerator type that is not associated with the notebook instance when you call this method, it does not throw an error.

', ], ], 'DisassociateNotebookInstanceLifecycleConfig' => [ 'base' => NULL, 'refs' => [ 'UpdateNotebookInstanceInput$DisassociateLifecycleConfig' => '

Set to true to remove the notebook instance lifecycle configuration currently associated with the notebook instance. This operation is idempotent. If you specify a lifecycle configuration that is not associated with the notebook instance when you call this method, it does not throw an error.

', ], ], 'Dollars' => [ 'base' => NULL, 'refs' => [ 'USD$Dollars' => '

The whole number of dollars in the amount.

', ], ], 'EndpointArn' => [ 'base' => NULL, 'refs' => [ 'CreateEndpointOutput$EndpointArn' => '

The Amazon Resource Name (ARN) of the endpoint.

', 'DescribeEndpointOutput$EndpointArn' => '

The Amazon Resource Name (ARN) of the endpoint.

', 'EndpointSummary$EndpointArn' => '

The Amazon Resource Name (ARN) of the endpoint.

', 'UpdateEndpointOutput$EndpointArn' => '

The Amazon Resource Name (ARN) of the endpoint.

', 'UpdateEndpointWeightsAndCapacitiesOutput$EndpointArn' => '

The Amazon Resource Name (ARN) of the updated endpoint.

', ], ], 'EndpointConfigArn' => [ 'base' => NULL, 'refs' => [ 'CreateEndpointConfigOutput$EndpointConfigArn' => '

The Amazon Resource Name (ARN) of the endpoint configuration.

', 'DescribeEndpointConfigOutput$EndpointConfigArn' => '

The Amazon Resource Name (ARN) of the endpoint configuration.

', 'EndpointConfigSummary$EndpointConfigArn' => '

The Amazon Resource Name (ARN) of the endpoint configuration.

', ], ], 'EndpointConfigName' => [ 'base' => NULL, 'refs' => [ 'CreateEndpointConfigInput$EndpointConfigName' => '

The name of the endpoint configuration. You specify this name in a CreateEndpoint request.

', 'CreateEndpointInput$EndpointConfigName' => '

The name of an endpoint configuration. For more information, see CreateEndpointConfig.

', 'DeleteEndpointConfigInput$EndpointConfigName' => '

The name of the endpoint configuration that you want to delete.

', 'DescribeEndpointConfigInput$EndpointConfigName' => '

The name of the endpoint configuration.

', 'DescribeEndpointConfigOutput$EndpointConfigName' => '

Name of the Amazon SageMaker endpoint configuration.

', 'DescribeEndpointOutput$EndpointConfigName' => '

The name of the endpoint configuration associated with this endpoint.

', 'EndpointConfigSummary$EndpointConfigName' => '

The name of the endpoint configuration.

', 'UpdateEndpointInput$EndpointConfigName' => '

The name of the new endpoint configuration.

', ], ], 'EndpointConfigNameContains' => [ 'base' => NULL, 'refs' => [ 'ListEndpointConfigsInput$NameContains' => '

A string in the endpoint configuration name. This filter returns only endpoint configurations whose name contains the specified string.

', ], ], 'EndpointConfigSortKey' => [ 'base' => NULL, 'refs' => [ 'ListEndpointConfigsInput$SortBy' => '

The field to sort results by. The default is CreationTime.

', ], ], 'EndpointConfigSummary' => [ 'base' => '

Provides summary information for an endpoint configuration.

', 'refs' => [ 'EndpointConfigSummaryList$member' => NULL, ], ], 'EndpointConfigSummaryList' => [ 'base' => NULL, 'refs' => [ 'ListEndpointConfigsOutput$EndpointConfigs' => '

An array of endpoint configurations.

', ], ], 'EndpointName' => [ 'base' => NULL, 'refs' => [ 'CreateEndpointInput$EndpointName' => '

The name of the endpoint. The name must be unique within an AWS Region in your AWS account.

', 'DeleteEndpointInput$EndpointName' => '

The name of the endpoint that you want to delete.

', 'DescribeEndpointInput$EndpointName' => '

The name of the endpoint.

', 'DescribeEndpointOutput$EndpointName' => '

Name of the endpoint.

', 'EndpointSummary$EndpointName' => '

The name of the endpoint.

', 'UpdateEndpointInput$EndpointName' => '

The name of the endpoint whose configuration you want to update.

', 'UpdateEndpointWeightsAndCapacitiesInput$EndpointName' => '

The name of an existing Amazon SageMaker endpoint.

', ], ], 'EndpointNameContains' => [ 'base' => NULL, 'refs' => [ 'ListEndpointsInput$NameContains' => '

A string in endpoint names. This filter returns only endpoints whose name contains the specified string.

', ], ], 'EndpointSortKey' => [ 'base' => NULL, 'refs' => [ 'ListEndpointsInput$SortBy' => '

Sorts the list of results. The default is CreationTime.

', ], ], 'EndpointStatus' => [ 'base' => NULL, 'refs' => [ 'DescribeEndpointOutput$EndpointStatus' => '

The status of the endpoint.

  • OutOfService: Endpoint is not available to take incoming requests.

  • Creating: CreateEndpoint is executing.

  • Updating: UpdateEndpoint or UpdateEndpointWeightsAndCapacities is executing.

  • SystemUpdating: Endpoint is undergoing maintenance and cannot be updated or deleted or re-scaled until it has completed. This maintenance operation does not change any customer-specified values such as VPC config, KMS encryption, model, instance type, or instance count.

  • RollingBack: Endpoint fails to scale up or down or change its variant weight and is in the process of rolling back to its previous configuration. Once the rollback completes, endpoint returns to an InService status. This transitional status only applies to an endpoint that has autoscaling enabled and is undergoing variant weight or capacity changes as part of an UpdateEndpointWeightsAndCapacities call or when the UpdateEndpointWeightsAndCapacities operation is called explicitly.

  • InService: Endpoint is available to process incoming requests.

  • Deleting: DeleteEndpoint is executing.

  • Failed: Endpoint could not be created, updated, or re-scaled. Use DescribeEndpointOutput$FailureReason for information about the failure. DeleteEndpoint is the only operation that can be performed on a failed endpoint.

', 'EndpointSummary$EndpointStatus' => '

The status of the endpoint.

  • OutOfService: Endpoint is not available to take incoming requests.

  • Creating: CreateEndpoint is executing.

  • Updating: UpdateEndpoint or UpdateEndpointWeightsAndCapacities is executing.

  • SystemUpdating: Endpoint is undergoing maintenance and cannot be updated or deleted or re-scaled until it has completed. This maintenance operation does not change any customer-specified values such as VPC config, KMS encryption, model, instance type, or instance count.

  • RollingBack: Endpoint fails to scale up or down or change its variant weight and is in the process of rolling back to its previous configuration. Once the rollback completes, endpoint returns to an InService status. This transitional status only applies to an endpoint that has autoscaling enabled and is undergoing variant weight or capacity changes as part of an UpdateEndpointWeightsAndCapacities call or when the UpdateEndpointWeightsAndCapacities operation is called explicitly.

  • InService: Endpoint is available to process incoming requests.

  • Deleting: DeleteEndpoint is executing.

  • Failed: Endpoint could not be created, updated, or re-scaled. Use DescribeEndpointOutput$FailureReason for information about the failure. DeleteEndpoint is the only operation that can be performed on a failed endpoint.

To get a list of endpoints with a specified status, use the ListEndpointsInput$StatusEquals filter.

', 'ListEndpointsInput$StatusEquals' => '

A filter that returns only endpoints with the specified status.

', ], ], 'EndpointSummary' => [ 'base' => '

Provides summary information for an endpoint.

', 'refs' => [ 'EndpointSummaryList$member' => NULL, ], ], 'EndpointSummaryList' => [ 'base' => NULL, 'refs' => [ 'ListEndpointsOutput$Endpoints' => '

An array or endpoint objects.

', ], ], 'EntityDescription' => [ 'base' => NULL, 'refs' => [ 'AlgorithmSummary$AlgorithmDescription' => '

A brief description of the algorithm.

', 'ChannelSpecification$Description' => '

A brief description of the channel.

', 'CreateAlgorithmInput$AlgorithmDescription' => '

A description of the algorithm.

', 'CreateModelPackageInput$ModelPackageDescription' => '

A description of the model package.

', 'DescribeAlgorithmOutput$AlgorithmDescription' => '

A brief summary about the algorithm.

', 'DescribeModelPackageOutput$ModelPackageDescription' => '

A brief summary of the model package.

', 'HyperParameterSpecification$Description' => '

A brief description of the hyperparameter.

', 'ModelPackageSummary$ModelPackageDescription' => '

A brief description of the model package.

', ], ], 'EntityName' => [ 'base' => NULL, 'refs' => [ 'AlgorithmStatusItem$Name' => '

The name of the algorithm for which the overall status is being reported.

', 'AlgorithmSummary$AlgorithmName' => '

The name of the algorithm that is described by the summary.

', 'AlgorithmValidationProfile$ProfileName' => '

The name of the profile for the algorithm. The name must have 1 to 63 characters. Valid characters are a-z, A-Z, 0-9, and - (hyphen).

', 'CodeRepositorySummary$CodeRepositoryName' => '

The name of the Git repository.

', 'CompilationJobSummary$CompilationJobName' => '

The name of the model compilation job that you want a summary for.

', 'CreateAlgorithmInput$AlgorithmName' => '

The name of the algorithm.

', 'CreateCodeRepositoryInput$CodeRepositoryName' => '

The name of the Git repository. The name must have 1 to 63 characters. Valid characters are a-z, A-Z, 0-9, and - (hyphen).

', 'CreateCompilationJobRequest$CompilationJobName' => '

A name for the model compilation job. The name must be unique within the AWS Region and within your AWS account.

', 'CreateModelPackageInput$ModelPackageName' => '

The name of the model package. The name must have 1 to 63 characters. Valid characters are a-z, A-Z, 0-9, and - (hyphen).

', 'DeleteAlgorithmInput$AlgorithmName' => '

The name of the algorithm to delete.

', 'DeleteCodeRepositoryInput$CodeRepositoryName' => '

The name of the Git repository to delete.

', 'DeleteModelPackageInput$ModelPackageName' => '

The name of the model package. The name must have 1 to 63 characters. Valid characters are a-z, A-Z, 0-9, and - (hyphen).

', 'DescribeAlgorithmOutput$AlgorithmName' => '

The name of the algorithm being described.

', 'DescribeCodeRepositoryInput$CodeRepositoryName' => '

The name of the Git repository to describe.

', 'DescribeCodeRepositoryOutput$CodeRepositoryName' => '

The name of the Git repository.

', 'DescribeCompilationJobRequest$CompilationJobName' => '

The name of the model compilation job that you want information about.

', 'DescribeCompilationJobResponse$CompilationJobName' => '

The name of the model compilation job.

', 'DescribeModelPackageOutput$ModelPackageName' => '

The name of the model package being described.

', 'ModelPackageStatusItem$Name' => '

The name of the model package for which the overall status is being reported.

', 'ModelPackageSummary$ModelPackageName' => '

The name of the model package.

', 'ModelPackageValidationProfile$ProfileName' => '

The name of the profile for the model package.

', 'StopCompilationJobRequest$CompilationJobName' => '

The name of the model compilation job to stop.

', 'UpdateCodeRepositoryInput$CodeRepositoryName' => '

The name of the Git repository to update.

', ], ], 'EnvironmentKey' => [ 'base' => NULL, 'refs' => [ 'EnvironmentMap$key' => NULL, ], ], 'EnvironmentMap' => [ 'base' => NULL, 'refs' => [ 'ContainerDefinition$Environment' => '

The environment variables to set in the Docker container. Each key and value in the Environment string to string map can have length of up to 1024. We support up to 16 entries in the map.

', ], ], 'EnvironmentValue' => [ 'base' => NULL, 'refs' => [ 'EnvironmentMap$value' => NULL, ], ], 'FailureReason' => [ 'base' => NULL, 'refs' => [ 'DescribeCompilationJobResponse$FailureReason' => '

If a model compilation job failed, the reason it failed.

', 'DescribeEndpointOutput$FailureReason' => '

If the status of the endpoint is Failed, the reason why it failed.

', 'DescribeHyperParameterTuningJobResponse$FailureReason' => '

If the tuning job failed, the reason it failed.

', 'DescribeLabelingJobResponse$FailureReason' => '

If the job failed, the reason that it failed.

', 'DescribeNotebookInstanceOutput$FailureReason' => '

If status is Failed, the reason it failed.

', 'DescribeTrainingJobResponse$FailureReason' => '

If the training job failed, the reason it failed.

', 'DescribeTransformJobResponse$FailureReason' => '

If the transform job failed, FailureReason describes why it failed. A transform job creates a log file, which includes error messages, and stores it as an Amazon S3 object. For more information, see Log Amazon SageMaker Events with Amazon CloudWatch.

', 'HyperParameterTrainingJobSummary$FailureReason' => '

The reason that the training job failed.

', 'LabelingJobSummary$FailureReason' => '

If the LabelingJobStatus field is Failed, this field contains a description of the error.

', 'ResourceInUse$Message' => NULL, 'ResourceLimitExceeded$Message' => NULL, 'ResourceNotFound$Message' => NULL, 'TrainingJob$FailureReason' => '

If the training job failed, the reason it failed.

', 'TransformJobSummary$FailureReason' => '

If the transform job failed, the reason it failed.

', ], ], 'Filter' => [ 'base' => '

A conditional statement for a search expression that includes a Boolean operator, a resource property, and a value.

If you don\'t specify an Operator and a Value, the filter searches for only the specified property. For example, defining a Filter for the FailureReason for the TrainingJob Resource searches for training job objects that have a value in the FailureReason field.

If you specify a Value, but not an Operator, Amazon SageMaker uses the equals operator as the default.

In search, there are several property types:

Metrics

To define a metric filter, enter a value using the form "Metrics.<name>", where <name> is a metric name. For example, the following filter searches for training jobs with an "accuracy" metric greater than "0.9":

{

"Name": "Metrics.accuracy",

"Operator": "GREATER_THAN",

"Value": "0.9"

}

HyperParameters

To define a hyperparameter filter, enter a value with the form "HyperParameters.<name>". Decimal hyperparameter values are treated as a decimal in a comparison if the specified Value is also a decimal value. If the specified Value is an integer, the decimal hyperparameter values are treated as integers. For example, the following filter is satisfied by training jobs with a "learning_rate" hyperparameter that is less than "0.5":

{

"Name": "HyperParameters.learning_rate",

"Operator": "LESS_THAN",

"Value": "0.5"

}

Tags

To define a tag filter, enter a value with the form "Tags.<key>".

', 'refs' => [ 'FilterList$member' => NULL, ], ], 'FilterList' => [ 'base' => NULL, 'refs' => [ 'NestedFilters$Filters' => '

A list of filters. Each filter acts on a property. Filters must contain at least one Filters value. For example, a NestedFilters call might include a filter on the PropertyName parameter of the InputDataConfig property: InputDataConfig.DataSource.S3DataSource.S3Uri.

', 'SearchExpression$Filters' => '

A list of filter objects.

', ], ], 'FilterValue' => [ 'base' => NULL, 'refs' => [ 'Filter$Value' => '

A value used with Resource and Operator to determine if objects satisfy the filter\'s condition. For numerical properties, Value must be an integer or floating-point decimal. For timestamp properties, Value must be an ISO 8601 date-time string of the following format: YYYY-mm-dd\'T\'HH:MM:SS.

', ], ], 'FinalHyperParameterTuningJobObjectiveMetric' => [ 'base' => '

Shows the final value for the objective metric for a training job that was launched by a hyperparameter tuning job. You define the objective metric in the HyperParameterTuningJobObjective parameter of HyperParameterTuningJobConfig.

', 'refs' => [ 'HyperParameterTrainingJobSummary$FinalHyperParameterTuningJobObjectiveMetric' => '

The FinalHyperParameterTuningJobObjectiveMetric object that specifies the value of the objective metric of the tuning job that launched this training job.

', ], ], 'FinalMetricDataList' => [ 'base' => NULL, 'refs' => [ 'DescribeTrainingJobResponse$FinalMetricDataList' => '

A collection of MetricData objects that specify the names, values, and dates and times that the training algorithm emitted to Amazon CloudWatch.

', 'TrainingJob$FinalMetricDataList' => '

A list of final metric values that are set when the training job completes. Used only if the training job was configured to use metrics.

', ], ], 'Float' => [ 'base' => NULL, 'refs' => [ 'MetricData$Value' => '

The value of the metric.

', ], ], 'Framework' => [ 'base' => NULL, 'refs' => [ 'InputConfig$Framework' => '

Identifies the framework in which the model was trained. For example: TENSORFLOW.

', ], ], 'GetSearchSuggestionsRequest' => [ 'base' => NULL, 'refs' => [], ], 'GetSearchSuggestionsResponse' => [ 'base' => NULL, 'refs' => [], ], 'GitConfig' => [ 'base' => '

Specifies configuration details for a Git repository in your AWS account.

', 'refs' => [ 'CodeRepositorySummary$GitConfig' => '

Configuration details for the Git repository, including the URL where it is located and the ARN of the AWS Secrets Manager secret that contains the credentials used to access the repository.

', 'CreateCodeRepositoryInput$GitConfig' => '

Specifies details about the repository, including the URL where the repository is located, the default branch, and credentials to use to access the repository.

', 'DescribeCodeRepositoryOutput$GitConfig' => '

Configuration details about the repository, including the URL where the repository is located, the default branch, and the Amazon Resource Name (ARN) of the AWS Secrets Manager secret that contains the credentials used to access the repository.

', ], ], 'GitConfigForUpdate' => [ 'base' => '

Specifies configuration details for a Git repository when the repository is updated.

', 'refs' => [ 'UpdateCodeRepositoryInput$GitConfig' => '

The configuration of the git repository, including the URL and the Amazon Resource Name (ARN) of the AWS Secrets Manager secret that contains the credentials used to access the repository. The secret must have a staging label of AWSCURRENT and must be in the following format:

{"username": UserName, "password": Password}

', ], ], 'GitConfigUrl' => [ 'base' => NULL, 'refs' => [ 'GitConfig$RepositoryUrl' => '

The URL where the Git repository is located.

', ], ], 'HumanTaskConfig' => [ 'base' => '

Information required for human workers to complete a labeling task.

', 'refs' => [ 'CreateLabelingJobRequest$HumanTaskConfig' => '

Configures the information required for human workers to complete a labeling task.

', 'DescribeLabelingJobResponse$HumanTaskConfig' => '

Configuration information required for human workers to complete a labeling task.

', ], ], 'HyperParameterAlgorithmSpecification' => [ 'base' => '

Specifies which training algorithm to use for training jobs that a hyperparameter tuning job launches and the metrics to monitor.

', 'refs' => [ 'HyperParameterTrainingJobDefinition$AlgorithmSpecification' => '

The HyperParameterAlgorithmSpecification object that specifies the resource algorithm to use for the training jobs that the tuning job launches.

', ], ], 'HyperParameterScalingType' => [ 'base' => NULL, 'refs' => [ 'ContinuousParameterRange$ScalingType' => '

The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling. One of the following values:

Auto

Amazon SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.

Linear

Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.

Logarithmic

Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale.

Logarithmic scaling works only for ranges that have only values greater than 0.

ReverseLogarithmic

Hyperparemeter tuning searches the values in the hyperparameter range by using a reverse logarithmic scale.

Reverse logarithmic scaling works only for ranges that are entirely within the range 0<=x<1.0.

', 'IntegerParameterRange$ScalingType' => '

The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling. One of the following values:

Auto

Amazon SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.

Linear

Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.

Logarithmic

Hyperparemeter tuning searches the values in the hyperparameter range by using a logarithmic scale.

Logarithmic scaling works only for ranges that have only values greater than 0.

', ], ], 'HyperParameterSpecification' => [ 'base' => '

Defines a hyperparameter to be used by an algorithm.

', 'refs' => [ 'HyperParameterSpecifications$member' => NULL, ], ], 'HyperParameterSpecifications' => [ 'base' => NULL, 'refs' => [ 'TrainingSpecification$SupportedHyperParameters' => '

A list of the HyperParameterSpecification objects, that define the supported hyperparameters. This is required if the algorithm supports automatic model tuning.>

', ], ], 'HyperParameterTrainingJobDefinition' => [ 'base' => '

Defines the training jobs launched by a hyperparameter tuning job.

', 'refs' => [ 'CreateHyperParameterTuningJobRequest$TrainingJobDefinition' => '

The HyperParameterTrainingJobDefinition object that describes the training jobs that this tuning job launches, including static hyperparameters, input data configuration, output data configuration, resource configuration, and stopping condition.

', 'DescribeHyperParameterTuningJobResponse$TrainingJobDefinition' => '

The HyperParameterTrainingJobDefinition object that specifies the definition of the training jobs that this tuning job launches.

', ], ], 'HyperParameterTrainingJobSummaries' => [ 'base' => NULL, 'refs' => [ 'ListTrainingJobsForHyperParameterTuningJobResponse$TrainingJobSummaries' => '

A list of TrainingJobSummary objects that describe the training jobs that the ListTrainingJobsForHyperParameterTuningJob request returned.

', ], ], 'HyperParameterTrainingJobSummary' => [ 'base' => '

Specifies summary information about a training job.

', 'refs' => [ 'DescribeHyperParameterTuningJobResponse$BestTrainingJob' => '

A TrainingJobSummary object that describes the training job that completed with the best current HyperParameterTuningJobObjective.

', 'DescribeHyperParameterTuningJobResponse$OverallBestTrainingJob' => '

If the hyperparameter tuning job is an warm start tuning job with a WarmStartType of IDENTICAL_DATA_AND_ALGORITHM, this is the TrainingJobSummary for the training job with the best objective metric value of all training jobs launched by this tuning job and all parent jobs specified for the warm start tuning job.

', 'HyperParameterTrainingJobSummaries$member' => NULL, ], ], 'HyperParameterTuningJobArn' => [ 'base' => NULL, 'refs' => [ 'CreateHyperParameterTuningJobResponse$HyperParameterTuningJobArn' => '

The Amazon Resource Name (ARN) of the tuning job. Amazon SageMaker assigns an ARN to a hyperparameter tuning job when you create it.

', 'DescribeHyperParameterTuningJobResponse$HyperParameterTuningJobArn' => '

The Amazon Resource Name (ARN) of the tuning job.

', 'DescribeTrainingJobResponse$TuningJobArn' => '

The Amazon Resource Name (ARN) of the associated hyperparameter tuning job if the training job was launched by a hyperparameter tuning job.

', 'HyperParameterTuningJobSummary$HyperParameterTuningJobArn' => '

The Amazon Resource Name (ARN) of the tuning job.

', 'TrainingJob$TuningJobArn' => '

The Amazon Resource Name (ARN) of the associated hyperparameter tuning job if the training job was launched by a hyperparameter tuning job.

', ], ], 'HyperParameterTuningJobConfig' => [ 'base' => '

Configures a hyperparameter tuning job.

', 'refs' => [ 'CreateHyperParameterTuningJobRequest$HyperParameterTuningJobConfig' => '

The HyperParameterTuningJobConfig object that describes the tuning job, including the search strategy, the objective metric used to evaluate training jobs, ranges of parameters to search, and resource limits for the tuning job. For more information, see automatic-model-tuning

', 'DescribeHyperParameterTuningJobResponse$HyperParameterTuningJobConfig' => '

The HyperParameterTuningJobConfig object that specifies the configuration of the tuning job.

', ], ], 'HyperParameterTuningJobName' => [ 'base' => NULL, 'refs' => [ 'CreateHyperParameterTuningJobRequest$HyperParameterTuningJobName' => '

The name of the tuning job. This name is the prefix for the names of all training jobs that this tuning job launches. The name must be unique within the same AWS account and AWS Region. The name must have { } to { } characters. Valid characters are a-z, A-Z, 0-9, and : + = @ _ % - (hyphen). The name is not case sensitive.

', 'DescribeHyperParameterTuningJobRequest$HyperParameterTuningJobName' => '

The name of the tuning job to describe.

', 'DescribeHyperParameterTuningJobResponse$HyperParameterTuningJobName' => '

The name of the tuning job.

', 'HyperParameterTrainingJobSummary$TuningJobName' => '

The HyperParameter tuning job that launched the training job.

', 'HyperParameterTuningJobSummary$HyperParameterTuningJobName' => '

The name of the tuning job.

', 'ListTrainingJobsForHyperParameterTuningJobRequest$HyperParameterTuningJobName' => '

The name of the tuning job whose training jobs you want to list.

', 'ParentHyperParameterTuningJob$HyperParameterTuningJobName' => '

The name of the hyperparameter tuning job to be used as a starting point for a new hyperparameter tuning job.

', 'StopHyperParameterTuningJobRequest$HyperParameterTuningJobName' => '

The name of the tuning job to stop.

', ], ], 'HyperParameterTuningJobObjective' => [ 'base' => '

Defines the objective metric for a hyperparameter tuning job. Hyperparameter tuning uses the value of this metric to evaluate the training jobs it launches, and returns the training job that results in either the highest or lowest value for this metric, depending on the value you specify for the Type parameter.

', 'refs' => [ 'HyperParameterTuningJobConfig$HyperParameterTuningJobObjective' => '

The HyperParameterTuningJobObjective object that specifies the objective metric for this tuning job.

', 'HyperParameterTuningJobObjectives$member' => NULL, ], ], 'HyperParameterTuningJobObjectiveType' => [ 'base' => NULL, 'refs' => [ 'FinalHyperParameterTuningJobObjectiveMetric$Type' => '

Whether to minimize or maximize the objective metric. Valid values are Minimize and Maximize.

', 'HyperParameterTuningJobObjective$Type' => '

Whether to minimize or maximize the objective metric.

', ], ], 'HyperParameterTuningJobObjectives' => [ 'base' => NULL, 'refs' => [ 'TrainingSpecification$SupportedTuningJobObjectiveMetrics' => '

A list of the metrics that the algorithm emits that can be used as the objective metric in a hyperparameter tuning job.

', ], ], 'HyperParameterTuningJobSortByOptions' => [ 'base' => NULL, 'refs' => [ 'ListHyperParameterTuningJobsRequest$SortBy' => '

The field to sort results by. The default is Name.

', ], ], 'HyperParameterTuningJobStatus' => [ 'base' => NULL, 'refs' => [ 'DescribeHyperParameterTuningJobResponse$HyperParameterTuningJobStatus' => '

The status of the tuning job: InProgress, Completed, Failed, Stopping, or Stopped.

', 'HyperParameterTuningJobSummary$HyperParameterTuningJobStatus' => '

The status of the tuning job.

', 'ListHyperParameterTuningJobsRequest$StatusEquals' => '

A filter that returns only tuning jobs with the specified status.

', ], ], 'HyperParameterTuningJobStrategyType' => [ 'base' => '

The strategy hyperparameter tuning uses to find the best combination of hyperparameters for your model. Currently, the only supported value is Bayesian.

', 'refs' => [ 'HyperParameterTuningJobConfig$Strategy' => '

Specifies how hyperparameter tuning chooses the combinations of hyperparameter values to use for the training job it launches. To use the Bayesian search stategy, set this to Bayesian. To randomly search, set it to Random. For information about search strategies, see How Hyperparameter Tuning Works.

', 'HyperParameterTuningJobSummary$Strategy' => '

Specifies the search strategy hyperparameter tuning uses to choose which hyperparameters to use for each iteration. Currently, the only valid value is Bayesian.

', ], ], 'HyperParameterTuningJobSummaries' => [ 'base' => NULL, 'refs' => [ 'ListHyperParameterTuningJobsResponse$HyperParameterTuningJobSummaries' => '

A list of HyperParameterTuningJobSummary objects that describe the tuning jobs that the ListHyperParameterTuningJobs request returned.

', ], ], 'HyperParameterTuningJobSummary' => [ 'base' => '

Provides summary information about a hyperparameter tuning job.

', 'refs' => [ 'HyperParameterTuningJobSummaries$member' => NULL, ], ], 'HyperParameterTuningJobWarmStartConfig' => [ 'base' => '

Specifies the configuration for a hyperparameter tuning job that uses one or more previous hyperparameter tuning jobs as a starting point. The results of previous tuning jobs are used to inform which combinations of hyperparameters to search over in the new tuning job.

All training jobs launched by the new hyperparameter tuning job are evaluated by using the objective metric, and the training job that performs the best is compared to the best training jobs from the parent tuning jobs. From these, the training job that performs the best as measured by the objective metric is returned as the overall best training job.

All training jobs launched by parent hyperparameter tuning jobs and the new hyperparameter tuning jobs count against the limit of training jobs for the tuning job.

', 'refs' => [ 'CreateHyperParameterTuningJobRequest$WarmStartConfig' => '

Specifies the configuration for starting the hyperparameter tuning job using one or more previous tuning jobs as a starting point. The results of previous tuning jobs are used to inform which combinations of hyperparameters to search over in the new tuning job.

All training jobs launched by the new hyperparameter tuning job are evaluated by using the objective metric. If you specify IDENTICAL_DATA_AND_ALGORITHM as the WarmStartType value for the warm start configuration, the training job that performs the best in the new tuning job is compared to the best training jobs from the parent tuning jobs. From these, the training job that performs the best as measured by the objective metric is returned as the overall best training job.

All training jobs launched by parent hyperparameter tuning jobs and the new hyperparameter tuning jobs count against the limit of training jobs for the tuning job.

', 'DescribeHyperParameterTuningJobResponse$WarmStartConfig' => '

The configuration for starting the hyperparameter parameter tuning job using one or more previous tuning jobs as a starting point. The results of previous tuning jobs are used to inform which combinations of hyperparameters to search over in the new tuning job.

', ], ], 'HyperParameterTuningJobWarmStartType' => [ 'base' => NULL, 'refs' => [ 'HyperParameterTuningJobWarmStartConfig$WarmStartType' => '

Specifies one of the following:

IDENTICAL_DATA_AND_ALGORITHM

The new hyperparameter tuning job uses the same input data and training image as the parent tuning jobs. You can change the hyperparameter ranges to search and the maximum number of training jobs that the hyperparameter tuning job launches. You cannot use a new version of the training algorithm, unless the changes in the new version do not affect the algorithm itself. For example, changes that improve logging or adding support for a different data format are allowed. You can also change hyperparameters from tunable to static, and from static to tunable, but the total number of static plus tunable hyperparameters must remain the same as it is in all parent jobs. The objective metric for the new tuning job must be the same as for all parent jobs.

TRANSFER_LEARNING

The new hyperparameter tuning job can include input data, hyperparameter ranges, maximum number of concurrent training jobs, and maximum number of training jobs that are different than those of its parent hyperparameter tuning jobs. The training image can also be a different version from the version used in the parent hyperparameter tuning job. You can also change hyperparameters from tunable to static, and from static to tunable, but the total number of static plus tunable hyperparameters must remain the same as it is in all parent jobs. The objective metric for the new tuning job must be the same as for all parent jobs.

', ], ], 'HyperParameters' => [ 'base' => NULL, 'refs' => [ 'CreateTrainingJobRequest$HyperParameters' => '

Algorithm-specific parameters that influence the quality of the model. You set hyperparameters before you start the learning process. For a list of hyperparameters for each training algorithm provided by Amazon SageMaker, see Algorithms.

You can specify a maximum of 100 hyperparameters. Each hyperparameter is a key-value pair. Each key and value is limited to 256 characters, as specified by the Length Constraint.

', 'DescribeTrainingJobResponse$HyperParameters' => '

Algorithm-specific parameters.

', 'HyperParameterTrainingJobDefinition$StaticHyperParameters' => '

Specifies the values of hyperparameters that do not change for the tuning job.

', 'HyperParameterTrainingJobSummary$TunedHyperParameters' => '

A list of the hyperparameters for which you specified ranges to search.

', 'TrainingJob$HyperParameters' => '

Algorithm-specific parameters.

', 'TrainingJobDefinition$HyperParameters' => '

The hyperparameters used for the training job.

', ], ], 'Image' => [ 'base' => NULL, 'refs' => [ 'ContainerDefinition$Image' => '

The Amazon EC2 Container Registry (Amazon ECR) path where inference code is stored. If you are using your own custom algorithm instead of an algorithm provided by Amazon SageMaker, the inference code must meet Amazon SageMaker requirements. Amazon SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker

', 'DeployedImage$SpecifiedImage' => '

The image path you specified when you created the model.

', 'DeployedImage$ResolvedImage' => '

The specific digest path of the image hosted in this ProductionVariant.

', 'ModelPackageContainerDefinition$Image' => '

The Amazon EC2 Container Registry (Amazon ECR) path where inference code is stored.

If you are using your own custom algorithm instead of an algorithm provided by Amazon SageMaker, the inference code must meet Amazon SageMaker requirements. Amazon SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker.

', 'TrainingSpecification$TrainingImage' => '

The Amazon ECR registry path of the Docker image that contains the training algorithm.

', ], ], 'ImageDigest' => [ 'base' => NULL, 'refs' => [ 'ModelPackageContainerDefinition$ImageDigest' => '

An MD5 hash of the training algorithm that identifies the Docker image used for training.

', 'TrainingSpecification$TrainingImageDigest' => '

An MD5 hash of the training algorithm that identifies the Docker image used for training.

', ], ], 'InferenceSpecification' => [ 'base' => '

Defines how to perform inference generation after a training job is run.

', 'refs' => [ 'CreateAlgorithmInput$InferenceSpecification' => '

Specifies details about inference jobs that the algorithm runs, including the following:

  • The Amazon ECR paths of containers that contain the inference code and model artifacts.

  • The instance types that the algorithm supports for transform jobs and real-time endpoints used for inference.

  • The input and output content formats that the algorithm supports for inference.

', 'CreateModelPackageInput$InferenceSpecification' => '

Specifies details about inference jobs that can be run with models based on this model package, including the following:

  • The Amazon ECR paths of containers that contain the inference code and model artifacts.

  • The instance types that the model package supports for transform jobs and real-time endpoints used for inference.

  • The input and output content formats that the model package supports for inference.

', 'DescribeAlgorithmOutput$InferenceSpecification' => '

Details about inference jobs that the algorithm runs.

', 'DescribeModelPackageOutput$InferenceSpecification' => '

Details about inference jobs that can be run with models based on this model package.

', ], ], 'InputConfig' => [ 'base' => '

Contains information about the location of input model artifacts, the name and shape of the expected data inputs, and the framework in which the model was trained.

', 'refs' => [ 'CreateCompilationJobRequest$InputConfig' => '

Provides information about the location of input model artifacts, the name and shape of the expected data inputs, and the framework in which the model was trained.

', 'DescribeCompilationJobResponse$InputConfig' => '

Information about the location in Amazon S3 of the input model artifacts, the name and shape of the expected data inputs, and the framework in which the model was trained.

', ], ], 'InputDataConfig' => [ 'base' => NULL, 'refs' => [ 'CreateTrainingJobRequest$InputDataConfig' => '

An array of Channel objects. Each channel is a named input source. InputDataConfig describes the input data and its location.

Algorithms can accept input data from one or more channels. For example, an algorithm might have two channels of input data, training_data and validation_data. The configuration for each channel provides the S3 location where the input data is stored. It also provides information about the stored data: the MIME type, compression method, and whether the data is wrapped in RecordIO format.

Depending on the input mode that the algorithm supports, Amazon SageMaker either copies input data files from an S3 bucket to a local directory in the Docker container, or makes it available as input streams.

', 'DescribeTrainingJobResponse$InputDataConfig' => '

An array of Channel objects that describes each data input channel.

', 'HyperParameterTrainingJobDefinition$InputDataConfig' => '

An array of Channel objects that specify the input for the training jobs that the tuning job launches.

', 'TrainingJob$InputDataConfig' => '

An array of Channel objects that describes each data input channel.

', 'TrainingJobDefinition$InputDataConfig' => '

An array of Channel objects, each of which specifies an input source.

', ], ], 'InputModes' => [ 'base' => NULL, 'refs' => [ 'ChannelSpecification$SupportedInputModes' => '

The allowed input mode, either FILE or PIPE.

In FILE mode, Amazon SageMaker copies the data from the input source onto the local Amazon Elastic Block Store (Amazon EBS) volumes before starting your training algorithm. This is the most commonly used input mode.

In PIPE mode, Amazon SageMaker streams input data from the source directly to your algorithm without using the EBS volume.

', ], ], 'InstanceType' => [ 'base' => NULL, 'refs' => [ 'CreateNotebookInstanceInput$InstanceType' => '

The type of ML compute instance to launch for the notebook instance.

', 'DescribeNotebookInstanceOutput$InstanceType' => '

The type of ML compute instance running on the notebook instance.

', 'NotebookInstanceSummary$InstanceType' => '

The type of ML compute instance that the notebook instance is running on.

', 'UpdateNotebookInstanceInput$InstanceType' => '

The Amazon ML compute instance type.

', ], ], 'IntegerParameterRange' => [ 'base' => '

For a hyperparameter of the integer type, specifies the range that a hyperparameter tuning job searches.

', 'refs' => [ 'IntegerParameterRanges$member' => NULL, ], ], 'IntegerParameterRangeSpecification' => [ 'base' => '

Defines the possible values for an integer hyperparameter.

', 'refs' => [ 'ParameterRange$IntegerParameterRangeSpecification' => '

A IntegerParameterRangeSpecification object that defines the possible values for an integer hyperparameter.

', ], ], 'IntegerParameterRanges' => [ 'base' => NULL, 'refs' => [ 'ParameterRanges$IntegerParameterRanges' => '

The array of IntegerParameterRange objects that specify ranges of integer hyperparameters that a hyperparameter tuning job searches.

', ], ], 'JobReferenceCode' => [ 'base' => NULL, 'refs' => [ 'DescribeLabelingJobResponse$JobReferenceCode' => '

A unique identifier for work done as part of a labeling job.

', 'LabelingJobForWorkteamSummary$JobReferenceCode' => '

A unique identifier for a labeling job. You can use this to refer to a specific labeling job.

', ], ], 'JobReferenceCodeContains' => [ 'base' => NULL, 'refs' => [ 'ListLabelingJobsForWorkteamRequest$JobReferenceCodeContains' => '

A filter the limits jobs to only the ones whose job reference code contains the specified string.

', ], ], 'JoinSource' => [ 'base' => NULL, 'refs' => [ 'DataProcessing$JoinSource' => '

Specifies the source of the data to join with the transformed data. The valid values are None and Input The default value is None which specifies not to join the input with the transformed data. If you want the batch transform job to join the original input data with the transformed data, set JoinSource to Input. To join input and output, the batch transform job must satisfy the Requirements for Using Batch Transform I/O Join.

For JSON or JSONLines objects, such as a JSON array, Amazon SageMaker adds the transformed data to the input JSON object in an attribute called SageMakerOutput. The joined result for JSON must be a key-value pair object. If the input is not a key-value pair object, Amazon SageMaker creates a new JSON file. In the new JSON file, and the input data is stored under the SageMakerInput key and the results are stored in SageMakerOutput.

For CSV files, Amazon SageMaker combines the transformed data with the input data at the end of the input data and stores it in the output file. The joined data has the joined input data followed by the transformed data and the output is a CSV file.

', ], ], 'JsonPath' => [ 'base' => NULL, 'refs' => [ 'DataProcessing$InputFilter' => '

A JSONPath expression used to select a portion of the input data to pass to the algorithm. Use the InputFilter parameter to exclude fields, such as an ID column, from the input. If you want Amazon SageMaker to pass the entire input dataset to the algorithm, accept the default value $.

Examples: "$", "$[1:]", "$.features"

', 'DataProcessing$OutputFilter' => '

A JSONPath expression used to select a portion of the joined dataset to save in the output file for a batch transform job. If you want Amazon SageMaker to store the entire input dataset in the output file, leave the default value, $. If you specify indexes that aren\'t within the dimension size of the joined dataset, you get an error.

Examples: "$", "$[0,5:]", "$.[\'id\',\'SageMakerOutput\']"

', ], ], 'KmsKeyId' => [ 'base' => NULL, 'refs' => [ 'CreateEndpointConfigInput$KmsKeyId' => '

The Amazon Resource Name (ARN) of a AWS Key Management Service key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance that hosts the endpoint.

', 'CreateNotebookInstanceInput$KmsKeyId' => '

The Amazon Resource Name (ARN) of a AWS Key Management Service key that Amazon SageMaker uses to encrypt data on the storage volume attached to your notebook instance. The KMS key you provide must be enabled. For information, see Enabling and Disabling Keys in the AWS Key Management Service Developer Guide.

', 'DescribeEndpointConfigOutput$KmsKeyId' => '

AWS KMS key ID Amazon SageMaker uses to encrypt data when storing it on the ML storage volume attached to the instance.

', 'DescribeNotebookInstanceOutput$KmsKeyId' => '

The AWS KMS key ID Amazon SageMaker uses to encrypt data when storing it on the ML storage volume attached to the instance.

', 'LabelingJobOutputConfig$KmsKeyId' => '

The AWS Key Management Service ID of the key used to encrypt the output data, if any.

If you use a KMS key ID or an alias of your master key, the Amazon SageMaker execution role must include permissions to call kms:Encrypt. If you don\'t provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role\'s account. Amazon SageMaker uses server-side encryption with KMS-managed keys for LabelingJobOutputConfig. If you use a bucket policy with an s3:PutObject permission that only allows objects with server-side encryption, set the condition key of s3:x-amz-server-side-encryption to "aws:kms". For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.

The KMS key policy must grant permission to the IAM role that you specify in your CreateLabelingJob request. For more information, see Using Key Policies in AWS KMS in the AWS Key Management Service Developer Guide.

', 'LabelingJobResourceConfig$VolumeKmsKeyId' => '

The AWS Key Management Service key ID for the key used to encrypt the output data, if any.

', 'OutputDataConfig$KmsKeyId' => '

The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:

  • // KMS Key ID

    "1234abcd-12ab-34cd-56ef-1234567890ab"

  • // Amazon Resource Name (ARN) of a KMS Key

    "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"

  • // KMS Key Alias

    "alias/ExampleAlias"

  • // Amazon Resource Name (ARN) of a KMS Key Alias

    "arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias"

If you use a KMS key ID or an alias of your master key, the Amazon SageMaker execution role must include permissions to call kms:Encrypt. If you don\'t provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role\'s account. Amazon SageMaker uses server-side encryption with KMS-managed keys for OutputDataConfig. If you use a bucket policy with an s3:PutObject permission that only allows objects with server-side encryption, set the condition key of s3:x-amz-server-side-encryption to "aws:kms". For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.

The KMS key policy must grant permission to the IAM role that you specify in your CreateTrainingJob, CreateTransformJob, or CreateHyperParameterTuningJob requests. For more information, see Using Key Policies in AWS KMS in the AWS Key Management Service Developer Guide.

', 'ResourceConfig$VolumeKmsKeyId' => '

The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the training job. The VolumeKmsKeyId can be any of the following formats:

  • // KMS Key ID

    "1234abcd-12ab-34cd-56ef-1234567890ab"

  • // Amazon Resource Name (ARN) of a KMS Key

    "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"

', 'TransformOutput$KmsKeyId' => '

The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:

  • // KMS Key ID

    "1234abcd-12ab-34cd-56ef-1234567890ab"

  • // Amazon Resource Name (ARN) of a KMS Key

    "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"

  • // KMS Key Alias

    "alias/ExampleAlias"

  • // Amazon Resource Name (ARN) of a KMS Key Alias

    "arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias"

If you don\'t provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role\'s account. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.

The KMS key policy must grant permission to the IAM role that you specify in your CreateTramsformJob request. For more information, see Using Key Policies in AWS KMS in the AWS Key Management Service Developer Guide.

', 'TransformResources$VolumeKmsKeyId' => '

The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the batch transform job. The VolumeKmsKeyId can be any of the following formats:

  • // KMS Key ID

    "1234abcd-12ab-34cd-56ef-1234567890ab"

  • // Amazon Resource Name (ARN) of a KMS Key

    "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"

', ], ], 'LabelAttributeName' => [ 'base' => NULL, 'refs' => [ 'CreateLabelingJobRequest$LabelAttributeName' => '

The attribute name to use for the label in the output manifest file. This is the key for the key/value pair formed with the label that a worker assigns to the object. The name can\'t end with "-metadata". If you are running a semantic segmentation labeling job, the attribute name must end with "-ref". If you are running any other kind of labeling job, the attribute name must not end with "-ref".

', 'DescribeLabelingJobResponse$LabelAttributeName' => '

The attribute used as the label in the output manifest file.

', ], ], 'LabelCounter' => [ 'base' => NULL, 'refs' => [ 'LabelCounters$TotalLabeled' => '

The total number of objects labeled.

', 'LabelCounters$HumanLabeled' => '

The total number of objects labeled by a human worker.

', 'LabelCounters$MachineLabeled' => '

The total number of objects labeled by automated data labeling.

', 'LabelCounters$FailedNonRetryableError' => '

The total number of objects that could not be labeled due to an error.

', 'LabelCounters$Unlabeled' => '

The total number of objects not yet labeled.

', 'LabelCountersForWorkteam$HumanLabeled' => '

The total number of data objects labeled by a human worker.

', 'LabelCountersForWorkteam$PendingHuman' => '

The total number of data objects that need to be labeled by a human worker.

', 'LabelCountersForWorkteam$Total' => '

The total number of tasks in the labeling job.

', ], ], 'LabelCounters' => [ 'base' => '

Provides a breakdown of the number of objects labeled.

', 'refs' => [ 'DescribeLabelingJobResponse$LabelCounters' => '

Provides a breakdown of the number of data objects labeled by humans, the number of objects labeled by machine, the number of objects than couldn\'t be labeled, and the total number of objects labeled.

', 'LabelingJobSummary$LabelCounters' => '

Counts showing the progress of the labeling job.

', ], ], 'LabelCountersForWorkteam' => [ 'base' => '

Provides counts for human-labeled tasks in the labeling job.

', 'refs' => [ 'LabelingJobForWorkteamSummary$LabelCounters' => '

Provides information about the progress of a labeling job.

', ], ], 'LabelingJobAlgorithmSpecificationArn' => [ 'base' => NULL, 'refs' => [ 'LabelingJobAlgorithmsConfig$LabelingJobAlgorithmSpecificationArn' => '

Specifies the Amazon Resource Name (ARN) of the algorithm used for auto-labeling. You must select one of the following ARNs:

  • Image classification

    arn:aws:sagemaker:region:027400017018:labeling-job-algorithm-specification/image-classification

  • Text classification

    arn:aws:sagemaker:region:027400017018:labeling-job-algorithm-specification/text-classification

  • Object detection

    arn:aws:sagemaker:region:027400017018:labeling-job-algorithm-specification/object-detection

', ], ], 'LabelingJobAlgorithmsConfig' => [ 'base' => '

Provides configuration information for auto-labeling of your data objects. A LabelingJobAlgorithmsConfig object must be supplied in order to use auto-labeling.

', 'refs' => [ 'CreateLabelingJobRequest$LabelingJobAlgorithmsConfig' => '

Configures the information required to perform automated data labeling.

', 'DescribeLabelingJobResponse$LabelingJobAlgorithmsConfig' => '

Configuration information for automated data labeling.

', ], ], 'LabelingJobArn' => [ 'base' => NULL, 'refs' => [ 'CreateLabelingJobResponse$LabelingJobArn' => '

The Amazon Resource Name (ARN) of the labeling job. You use this ARN to identify the labeling job.

', 'DescribeLabelingJobResponse$LabelingJobArn' => '

The Amazon Resource Name (ARN) of the labeling job.

', 'DescribeTrainingJobResponse$LabelingJobArn' => '

The Amazon Resource Name (ARN) of the Amazon SageMaker Ground Truth labeling job that created the transform or training job.

', 'DescribeTransformJobResponse$LabelingJobArn' => '

The Amazon Resource Name (ARN) of the Amazon SageMaker Ground Truth labeling job that created the transform or training job.

', 'LabelingJobSummary$LabelingJobArn' => '

The Amazon Resource Name (ARN) assigned to the labeling job when it was created.

', 'TrainingJob$LabelingJobArn' => '

The Amazon Resource Name (ARN) of the labeling job.

', ], ], 'LabelingJobDataAttributes' => [ 'base' => '

Attributes of the data specified by the customer. Use these to describe the data to be labeled.

', 'refs' => [ 'LabelingJobInputConfig$DataAttributes' => '

Attributes of the data specified by the customer.

', ], ], 'LabelingJobDataSource' => [ 'base' => '

Provides information about the location of input data.

', 'refs' => [ 'LabelingJobInputConfig$DataSource' => '

The location of the input data.

', ], ], 'LabelingJobForWorkteamSummary' => [ 'base' => '

Provides summary information for a work team.

', 'refs' => [ 'LabelingJobForWorkteamSummaryList$member' => NULL, ], ], 'LabelingJobForWorkteamSummaryList' => [ 'base' => NULL, 'refs' => [ 'ListLabelingJobsForWorkteamResponse$LabelingJobSummaryList' => '

An array of LabelingJobSummary objects, each describing a labeling job.

', ], ], 'LabelingJobInputConfig' => [ 'base' => '

Input configuration information for a labeling job.

', 'refs' => [ 'CreateLabelingJobRequest$InputConfig' => '

Input data for the labeling job, such as the Amazon S3 location of the data objects and the location of the manifest file that describes the data objects.

', 'DescribeLabelingJobResponse$InputConfig' => '

Input configuration information for the labeling job, such as the Amazon S3 location of the data objects and the location of the manifest file that describes the data objects.

', 'LabelingJobSummary$InputConfig' => '

Input configuration for the labeling job.

', ], ], 'LabelingJobName' => [ 'base' => NULL, 'refs' => [ 'CreateLabelingJobRequest$LabelingJobName' => '

The name of the labeling job. This name is used to identify the job in a list of labeling jobs.

', 'DescribeLabelingJobRequest$LabelingJobName' => '

The name of the labeling job to return information for.

', 'DescribeLabelingJobResponse$LabelingJobName' => '

The name assigned to the labeling job when it was created.

', 'LabelingJobForWorkteamSummary$LabelingJobName' => '

The name of the labeling job that the work team is assigned to.

', 'LabelingJobSummary$LabelingJobName' => '

The name of the labeling job.

', 'StopLabelingJobRequest$LabelingJobName' => '

The name of the labeling job to stop.

', ], ], 'LabelingJobOutput' => [ 'base' => '

Specifies the location of the output produced by the labeling job.

', 'refs' => [ 'DescribeLabelingJobResponse$LabelingJobOutput' => '

The location of the output produced by the labeling job.

', 'LabelingJobSummary$LabelingJobOutput' => '

The location of the output produced by the labeling job.

', ], ], 'LabelingJobOutputConfig' => [ 'base' => '

Output configuration information for a labeling job.

', 'refs' => [ 'CreateLabelingJobRequest$OutputConfig' => '

The location of the output data and the AWS Key Management Service key ID for the key used to encrypt the output data, if any.

', 'DescribeLabelingJobResponse$OutputConfig' => '

The location of the job\'s output data and the AWS Key Management Service key ID for the key used to encrypt the output data, if any.

', ], ], 'LabelingJobResourceConfig' => [ 'base' => '

Provides configuration information for labeling jobs.

', 'refs' => [ 'LabelingJobAlgorithmsConfig$LabelingJobResourceConfig' => '

Provides configuration information for a labeling job.

', ], ], 'LabelingJobS3DataSource' => [ 'base' => '

The Amazon S3 location of the input data objects.

', 'refs' => [ 'LabelingJobDataSource$S3DataSource' => '

The Amazon S3 location of the input data objects.

', ], ], 'LabelingJobStatus' => [ 'base' => NULL, 'refs' => [ 'DescribeLabelingJobResponse$LabelingJobStatus' => '

The processing status of the labeling job.

', 'LabelingJobSummary$LabelingJobStatus' => '

The current status of the labeling job.

', 'ListLabelingJobsRequest$StatusEquals' => '

A filter that retrieves only labeling jobs with a specific status.

', ], ], 'LabelingJobStoppingConditions' => [ 'base' => '

A set of conditions for stopping a labeling job. If any of the conditions are met, the job is automatically stopped. You can use these conditions to control the cost of data labeling.

', 'refs' => [ 'CreateLabelingJobRequest$StoppingConditions' => '

A set of conditions for stopping the labeling job. If any of the conditions are met, the job is automatically stopped. You can use these conditions to control the cost of data labeling.

', 'DescribeLabelingJobResponse$StoppingConditions' => '

A set of conditions for stopping a labeling job. If any of the conditions are met, the job is automatically stopped.

', ], ], 'LabelingJobSummary' => [ 'base' => '

Provides summary information about a labeling job.

', 'refs' => [ 'LabelingJobSummaryList$member' => NULL, ], ], 'LabelingJobSummaryList' => [ 'base' => NULL, 'refs' => [ 'ListLabelingJobsResponse$LabelingJobSummaryList' => '

An array of LabelingJobSummary objects, each describing a labeling job.

', ], ], 'LambdaFunctionArn' => [ 'base' => NULL, 'refs' => [ 'AnnotationConsolidationConfig$AnnotationConsolidationLambdaArn' => '

The Amazon Resource Name (ARN) of a Lambda function implements the logic for annotation consolidation.

For the built-in bounding box, image classification, semantic segmentation, and text classification task types, Amazon SageMaker Ground Truth provides the following Lambda functions:

  • Bounding box - Finds the most similar boxes from different workers based on the Jaccard index of the boxes.

    arn:aws:lambda:us-east-1:432418664414:function:ACS-BoundingBox

    arn:aws:lambda:us-east-2:266458841044:function:ACS-BoundingBox

    arn:aws:lambda:us-west-2:081040173940:function:ACS-BoundingBox

    arn:aws:lambda:eu-west-1:568282634449:function:ACS-BoundingBox

    arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-BoundingBox

    arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-BoundingBox

  • Image classification - Uses a variant of the Expectation Maximization approach to estimate the true class of an image based on annotations from individual workers.

    arn:aws:lambda:us-east-1:432418664414:function:ACS-ImageMultiClass

    arn:aws:lambda:us-east-2:266458841044:function:ACS-ImageMultiClass

    arn:aws:lambda:us-west-2:081040173940:function:ACS-ImageMultiClass

    arn:aws:lambda:eu-west-1:568282634449:function:ACS-ImageMultiClass

    arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-ImageMultiClass

    arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-ImageMultiClass

  • Semantic segmentation - Treats each pixel in an image as a multi-class classification and treats pixel annotations from workers as "votes" for the correct label.

    arn:aws:lambda:us-east-1:432418664414:function:ACS-SemanticSegmentation

    arn:aws:lambda:us-east-2:266458841044:function:ACS-SemanticSegmentation

    arn:aws:lambda:us-west-2:081040173940:function:ACS-SemanticSegmentation

    arn:aws:lambda:eu-west-1:568282634449:function:ACS-SemanticSegmentation

    arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-SemanticSegmentation

    arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-SemanticSegmentation

  • Text classification - Uses a variant of the Expectation Maximization approach to estimate the true class of text based on annotations from individual workers.

    arn:aws:lambda:us-east-1:432418664414:function:ACS-TextMultiClass

    arn:aws:lambda:us-east-2:266458841044:function:ACS-TextMultiClass

    arn:aws:lambda:us-west-2:081040173940:function:ACS-TextMultiClass

    arn:aws:lambda:eu-west-1:568282634449:function:ACS-TextMultiClass

    arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-TextMultiClass

    arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-TextMultiClass

For more information, see Annotation Consolidation.

', 'HumanTaskConfig$PreHumanTaskLambdaArn' => '

The Amazon Resource Name (ARN) of a Lambda function that is run before a data object is sent to a human worker. Use this function to provide input to a custom labeling job.

For the built-in bounding box, image classification, semantic segmentation, and text classification task types, Amazon SageMaker Ground Truth provides the following Lambda functions:

US East (Northern Virginia) (us-east-1):

  • arn:aws:lambda:us-east-1:432418664414:function:PRE-BoundingBox

  • arn:aws:lambda:us-east-1:432418664414:function:PRE-ImageMultiClass

  • arn:aws:lambda:us-east-1:432418664414:function:PRE-SemanticSegmentation

  • arn:aws:lambda:us-east-1:432418664414:function:PRE-TextMultiClass

US East (Ohio) (us-east-2):

  • arn:aws:lambda:us-east-2:266458841044:function:PRE-BoundingBox

  • arn:aws:lambda:us-east-2:266458841044:function:PRE-ImageMultiClass

  • arn:aws:lambda:us-east-2:266458841044:function:PRE-SemanticSegmentation

  • arn:aws:lambda:us-east-2:266458841044:function:PRE-TextMultiClass

US West (Oregon) (us-west-2):

  • arn:aws:lambda:us-west-2:081040173940:function:PRE-BoundingBox

  • arn:aws:lambda:us-west-2:081040173940:function:PRE-ImageMultiClass

  • arn:aws:lambda:us-west-2:081040173940:function:PRE-SemanticSegmentation

  • arn:aws:lambda:us-west-2:081040173940:function:PRE-TextMultiClass

EU (Ireland) (eu-west-1):

  • arn:aws:lambda:eu-west-1:568282634449:function:PRE-BoundingBox

  • arn:aws:lambda:eu-west-1:568282634449:function:PRE-ImageMultiClass

  • arn:aws:lambda:eu-west-1:568282634449:function:PRE-SemanticSegmentation

  • arn:aws:lambda:eu-west-1:568282634449:function:PRE-TextMultiClass

Asia Pacific (Tokyo) (ap-northeast-1):

  • arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-BoundingBox

  • arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-ImageMultiClass

  • arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-SemanticSegmentation

  • arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-TextMultiClass

Asia Pacific (Sydney) (ap-southeast-1):

  • arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-BoundingBox

  • arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-ImageMultiClass

  • arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-SemanticSegmentation

  • arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-TextMultiClass

', 'LabelingJobSummary$PreHumanTaskLambdaArn' => '

The Amazon Resource Name (ARN) of a Lambda function. The function is run before each data object is sent to a worker.

', 'LabelingJobSummary$AnnotationConsolidationLambdaArn' => '

The Amazon Resource Name (ARN) of the Lambda function used to consolidate the annotations from individual workers into a label for a data object. For more information, see Annotation Consolidation.

', ], ], 'LastModifiedTime' => [ 'base' => NULL, 'refs' => [ 'CodeRepositorySummary$LastModifiedTime' => '

The date and time that the Git repository was last modified.

', 'CompilationJobSummary$LastModifiedTime' => '

The time when the model compilation job was last modified.

', 'DescribeCodeRepositoryOutput$LastModifiedTime' => '

The date and time that the repository was last changed.

', 'DescribeCompilationJobResponse$LastModifiedTime' => '

The time that the status of the model compilation job was last modified.

', 'DescribeNotebookInstanceLifecycleConfigOutput$LastModifiedTime' => '

A timestamp that tells when the lifecycle configuration was last modified.

', 'DescribeNotebookInstanceOutput$LastModifiedTime' => '

A timestamp. Use this parameter to retrieve the time when the notebook instance was last modified.

', 'ListCompilationJobsRequest$LastModifiedTimeAfter' => '

A filter that returns the model compilation jobs that were modified after a specified time.

', 'ListCompilationJobsRequest$LastModifiedTimeBefore' => '

A filter that returns the model compilation jobs that were modified before a specified time.

', 'ListNotebookInstanceLifecycleConfigsInput$LastModifiedTimeBefore' => '

A filter that returns only lifecycle configurations that were modified before the specified time (timestamp).

', 'ListNotebookInstanceLifecycleConfigsInput$LastModifiedTimeAfter' => '

A filter that returns only lifecycle configurations that were modified after the specified time (timestamp).

', 'ListNotebookInstancesInput$LastModifiedTimeBefore' => '

A filter that returns only notebook instances that were modified before the specified time (timestamp).

', 'ListNotebookInstancesInput$LastModifiedTimeAfter' => '

A filter that returns only notebook instances that were modified after the specified time (timestamp).

', 'NotebookInstanceLifecycleConfigSummary$LastModifiedTime' => '

A timestamp that tells when the lifecycle configuration was last modified.

', 'NotebookInstanceSummary$LastModifiedTime' => '

A timestamp that shows when the notebook instance was last modified.

', ], ], 'ListAlgorithmsInput' => [ 'base' => NULL, 'refs' => [], ], 'ListAlgorithmsOutput' => [ 'base' => NULL, 'refs' => [], ], 'ListCodeRepositoriesInput' => [ 'base' => NULL, 'refs' => [], ], 'ListCodeRepositoriesOutput' => [ 'base' => NULL, 'refs' => [], ], 'ListCompilationJobsRequest' => [ 'base' => NULL, 'refs' => [], ], 'ListCompilationJobsResponse' => [ 'base' => NULL, 'refs' => [], ], 'ListCompilationJobsSortBy' => [ 'base' => NULL, 'refs' => [ 'ListCompilationJobsRequest$SortBy' => '

The field by which to sort results. The default is CreationTime.

', ], ], 'ListEndpointConfigsInput' => [ 'base' => NULL, 'refs' => [], ], 'ListEndpointConfigsOutput' => [ 'base' => NULL, 'refs' => [], ], 'ListEndpointsInput' => [ 'base' => NULL, 'refs' => [], ], 'ListEndpointsOutput' => [ 'base' => NULL, 'refs' => [], ], 'ListHyperParameterTuningJobsRequest' => [ 'base' => NULL, 'refs' => [], ], 'ListHyperParameterTuningJobsResponse' => [ 'base' => NULL, 'refs' => [], ], 'ListLabelingJobsForWorkteamRequest' => [ 'base' => NULL, 'refs' => [], ], 'ListLabelingJobsForWorkteamResponse' => [ 'base' => NULL, 'refs' => [], ], 'ListLabelingJobsForWorkteamSortByOptions' => [ 'base' => NULL, 'refs' => [ 'ListLabelingJobsForWorkteamRequest$SortBy' => '

The field to sort results by. The default is CreationTime.

', ], ], 'ListLabelingJobsRequest' => [ 'base' => NULL, 'refs' => [], ], 'ListLabelingJobsResponse' => [ 'base' => NULL, 'refs' => [], ], 'ListModelPackagesInput' => [ 'base' => NULL, 'refs' => [], ], 'ListModelPackagesOutput' => [ 'base' => NULL, 'refs' => [], ], 'ListModelsInput' => [ 'base' => NULL, 'refs' => [], ], 'ListModelsOutput' => [ 'base' => NULL, 'refs' => [], ], 'ListNotebookInstanceLifecycleConfigsInput' => [ 'base' => NULL, 'refs' => [], ], 'ListNotebookInstanceLifecycleConfigsOutput' => [ 'base' => NULL, 'refs' => [], ], 'ListNotebookInstancesInput' => [ 'base' => NULL, 'refs' => [], ], 'ListNotebookInstancesOutput' => [ 'base' => NULL, 'refs' => [], ], 'ListSubscribedWorkteamsRequest' => [ 'base' => NULL, 'refs' => [], ], 'ListSubscribedWorkteamsResponse' => [ 'base' => NULL, 'refs' => [], ], 'ListTagsInput' => [ 'base' => NULL, 'refs' => [], ], 'ListTagsMaxResults' => [ 'base' => NULL, 'refs' => [ 'ListTagsInput$MaxResults' => '

Maximum number of tags to return.

', ], ], 'ListTagsOutput' => [ 'base' => NULL, 'refs' => [], ], 'ListTrainingJobsForHyperParameterTuningJobRequest' => [ 'base' => NULL, 'refs' => [], ], 'ListTrainingJobsForHyperParameterTuningJobResponse' => [ 'base' => NULL, 'refs' => [], ], 'ListTrainingJobsRequest' => [ 'base' => NULL, 'refs' => [], ], 'ListTrainingJobsResponse' => [ 'base' => NULL, 'refs' => [], ], 'ListTransformJobsRequest' => [ 'base' => NULL, 'refs' => [], ], 'ListTransformJobsResponse' => [ 'base' => NULL, 'refs' => [], ], 'ListWorkteamsRequest' => [ 'base' => NULL, 'refs' => [], ], 'ListWorkteamsResponse' => [ 'base' => NULL, 'refs' => [], ], 'ListWorkteamsSortByOptions' => [ 'base' => NULL, 'refs' => [ 'ListWorkteamsRequest$SortBy' => '

The field to sort results by. The default is CreationTime.

', ], ], 'MaxConcurrentTaskCount' => [ 'base' => NULL, 'refs' => [ 'HumanTaskConfig$MaxConcurrentTaskCount' => '

Defines the maximum number of data objects that can be labeled by human workers at the same time. Each object may have more than one worker at one time.

', ], ], 'MaxConcurrentTransforms' => [ 'base' => NULL, 'refs' => [ 'CreateTransformJobRequest$MaxConcurrentTransforms' => '

The maximum number of parallel requests that can be sent to each instance in a transform job. If MaxConcurrentTransforms is set to 0 or left unset, Amazon SageMaker checks the optional execution-parameters to determine the optimal settings for your chosen algorithm. If the execution-parameters endpoint is not enabled, the default value is 1. For more information on execution-parameters, see How Containers Serve Requests. For built-in algorithms, you don\'t need to set a value for MaxConcurrentTransforms.

', 'DescribeTransformJobResponse$MaxConcurrentTransforms' => '

The maximum number of parallel requests on each instance node that can be launched in a transform job. The default value is 1.

', 'TransformJobDefinition$MaxConcurrentTransforms' => '

The maximum number of parallel requests that can be sent to each instance in a transform job. The default value is 1.

', ], ], 'MaxHumanLabeledObjectCount' => [ 'base' => NULL, 'refs' => [ 'LabelingJobStoppingConditions$MaxHumanLabeledObjectCount' => '

The maximum number of objects that can be labeled by human workers.

', ], ], 'MaxNumberOfTrainingJobs' => [ 'base' => NULL, 'refs' => [ 'ResourceLimits$MaxNumberOfTrainingJobs' => '

The maximum number of training jobs that a hyperparameter tuning job can launch.

', ], ], 'MaxParallelTrainingJobs' => [ 'base' => NULL, 'refs' => [ 'ResourceLimits$MaxParallelTrainingJobs' => '

The maximum number of concurrent training jobs that a hyperparameter tuning job can launch.

', ], ], 'MaxPayloadInMB' => [ 'base' => NULL, 'refs' => [ 'CreateTransformJobRequest$MaxPayloadInMB' => '

The maximum allowed size of the payload, in MB. A payload is the data portion of a record (without metadata). The value in MaxPayloadInMB must be greater than, or equal to, the size of a single record. To estimate the size of a record in MB, divide the size of your dataset by the number of records. To ensure that the records fit within the maximum payload size, we recommend using a slightly larger value. The default value is 6 MB.

For cases where the payload might be arbitrarily large and is transmitted using HTTP chunked encoding, set the value to 0. This feature works only in supported algorithms. Currently, Amazon SageMaker built-in algorithms do not support HTTP chunked encoding.

', 'DescribeTransformJobResponse$MaxPayloadInMB' => '

The maximum payload size, in MB, used in the transform job.

', 'TransformJobDefinition$MaxPayloadInMB' => '

The maximum payload size allowed, in MB. A payload is the data portion of a record (without metadata).

', ], ], 'MaxPercentageOfInputDatasetLabeled' => [ 'base' => NULL, 'refs' => [ 'LabelingJobStoppingConditions$MaxPercentageOfInputDatasetLabeled' => '

The maximum number of input data objects that should be labeled.

', ], ], 'MaxResults' => [ 'base' => NULL, 'refs' => [ 'ListAlgorithmsInput$MaxResults' => '

The maximum number of algorithms to return in the response.

', 'ListCodeRepositoriesInput$MaxResults' => '

The maximum number of Git repositories to return in the response.

', 'ListCompilationJobsRequest$MaxResults' => '

The maximum number of model compilation jobs to return in the response.

', 'ListEndpointConfigsInput$MaxResults' => '

The maximum number of training jobs to return in the response.

', 'ListEndpointsInput$MaxResults' => '

The maximum number of endpoints to return in the response.

', 'ListHyperParameterTuningJobsRequest$MaxResults' => '

The maximum number of tuning jobs to return. The default value is 10.

', 'ListLabelingJobsForWorkteamRequest$MaxResults' => '

The maximum number of labeling jobs to return in each page of the response.

', 'ListLabelingJobsRequest$MaxResults' => '

The maximum number of labeling jobs to return in each page of the response.

', 'ListModelPackagesInput$MaxResults' => '

The maximum number of model packages to return in the response.

', 'ListModelsInput$MaxResults' => '

The maximum number of models to return in the response.

', 'ListNotebookInstanceLifecycleConfigsInput$MaxResults' => '

The maximum number of lifecycle configurations to return in the response.

', 'ListNotebookInstancesInput$MaxResults' => '

The maximum number of notebook instances to return.

', 'ListSubscribedWorkteamsRequest$MaxResults' => '

The maximum number of work teams to return in each page of the response.

', 'ListTrainingJobsForHyperParameterTuningJobRequest$MaxResults' => '

The maximum number of training jobs to return. The default value is 10.

', 'ListTrainingJobsRequest$MaxResults' => '

The maximum number of training jobs to return in the response.

', 'ListTransformJobsRequest$MaxResults' => '

The maximum number of transform jobs to return in the response. The default value is 10.

', 'ListWorkteamsRequest$MaxResults' => '

The maximum number of work teams to return in each page of the response.

', 'SearchRequest$MaxResults' => '

The maximum number of results to return in a SearchResponse.

', ], ], 'MaxRuntimeInSeconds' => [ 'base' => NULL, 'refs' => [ 'StoppingCondition$MaxRuntimeInSeconds' => '

The maximum length of time, in seconds, that the training or compilation job can run. If job does not complete during this time, Amazon SageMaker ends the job. If value is not specified, default value is 1 day. The maximum value is 28 days.

', ], ], 'MemberDefinition' => [ 'base' => '

Defines the Amazon Cognito user group that is part of a work team.

', 'refs' => [ 'MemberDefinitions$member' => NULL, ], ], 'MemberDefinitions' => [ 'base' => NULL, 'refs' => [ 'CreateWorkteamRequest$MemberDefinitions' => '

A list of MemberDefinition objects that contains objects that identify the Amazon Cognito user pool that makes up the work team. For more information, see Amazon Cognito User Pools.

All of the CognitoMemberDefinition objects that make up the member definition must have the same ClientId and UserPool values.

', 'UpdateWorkteamRequest$MemberDefinitions' => '

A list of MemberDefinition objects that contain the updated work team members.

', 'Workteam$MemberDefinitions' => '

The Amazon Cognito user groups that make up the work team.

', ], ], 'MetricData' => [ 'base' => '

The name, value, and date and time of a metric that was emitted to Amazon CloudWatch.

', 'refs' => [ 'FinalMetricDataList$member' => NULL, ], ], 'MetricDefinition' => [ 'base' => '

Specifies a metric that the training algorithm writes to stderr or stdout. Amazon SageMakerhyperparameter tuning captures all defined metrics. You specify one metric that a hyperparameter tuning job uses as its objective metric to choose the best training job.

', 'refs' => [ 'MetricDefinitionList$member' => NULL, ], ], 'MetricDefinitionList' => [ 'base' => NULL, 'refs' => [ 'AlgorithmSpecification$MetricDefinitions' => '

A list of metric definition objects. Each object specifies the metric name and regular expressions used to parse algorithm logs. Amazon SageMaker publishes each metric to Amazon CloudWatch.

', 'HyperParameterAlgorithmSpecification$MetricDefinitions' => '

An array of MetricDefinition objects that specify the metrics that the algorithm emits.

', 'TrainingSpecification$MetricDefinitions' => '

A list of MetricDefinition objects, which are used for parsing metrics generated by the algorithm.

', ], ], 'MetricName' => [ 'base' => NULL, 'refs' => [ 'FinalHyperParameterTuningJobObjectiveMetric$MetricName' => '

The name of the objective metric.

', 'HyperParameterTuningJobObjective$MetricName' => '

The name of the metric to use for the objective metric.

', 'MetricData$MetricName' => '

The name of the metric.

', 'MetricDefinition$Name' => '

The name of the metric.

', ], ], 'MetricRegex' => [ 'base' => NULL, 'refs' => [ 'MetricDefinition$Regex' => '

A regular expression that searches the output of a training job and gets the value of the metric. For more information about using regular expressions to define metrics, see Defining Objective Metrics.

', ], ], 'MetricValue' => [ 'base' => NULL, 'refs' => [ 'FinalHyperParameterTuningJobObjectiveMetric$Value' => '

The value of the objective metric.

', ], ], 'ModelArn' => [ 'base' => NULL, 'refs' => [ 'CreateModelOutput$ModelArn' => '

The ARN of the model created in Amazon SageMaker.

', 'DescribeModelOutput$ModelArn' => '

The Amazon Resource Name (ARN) of the model.

', 'LabelingJobAlgorithmsConfig$InitialActiveLearningModelArn' => '

At the end of an auto-label job Amazon SageMaker Ground Truth sends the Amazon Resource Nam (ARN) of the final model used for auto-labeling. You can use this model as the starting point for subsequent similar jobs by providing the ARN of the model here.

', 'LabelingJobOutput$FinalActiveLearningModelArn' => '

The Amazon Resource Name (ARN) for the most recent Amazon SageMaker model trained as part of automated data labeling.

', 'ModelSummary$ModelArn' => '

The Amazon Resource Name (ARN) of the model.

', ], ], 'ModelArtifacts' => [ 'base' => '

Provides information about the location that is configured for storing model artifacts.

', 'refs' => [ 'DescribeCompilationJobResponse$ModelArtifacts' => '

Information about the location in Amazon S3 that has been configured for storing the model artifacts used in the compilation job.

', 'DescribeTrainingJobResponse$ModelArtifacts' => '

Information about the Amazon S3 location that is configured for storing model artifacts.

', 'TrainingJob$ModelArtifacts' => '

Information about the Amazon S3 location that is configured for storing model artifacts.

', ], ], 'ModelName' => [ 'base' => NULL, 'refs' => [ 'CreateModelInput$ModelName' => '

The name of the new model.

', 'CreateTransformJobRequest$ModelName' => '

The name of the model that you want to use for the transform job. ModelName must be the name of an existing Amazon SageMaker model within an AWS Region in an AWS account.

', 'DeleteModelInput$ModelName' => '

The name of the model to delete.

', 'DescribeModelInput$ModelName' => '

The name of the model.

', 'DescribeModelOutput$ModelName' => '

Name of the Amazon SageMaker model.

', 'DescribeTransformJobResponse$ModelName' => '

The name of the model used in the transform job.

', 'ModelSummary$ModelName' => '

The name of the model that you want a summary for.

', 'ProductionVariant$ModelName' => '

The name of the model that you want to host. This is the name that you specified when creating the model.

', ], ], 'ModelNameContains' => [ 'base' => NULL, 'refs' => [ 'ListModelsInput$NameContains' => '

A string in the training job name. This filter returns only models in the training job whose name contains the specified string.

', ], ], 'ModelPackageArn' => [ 'base' => NULL, 'refs' => [ 'CreateModelPackageOutput$ModelPackageArn' => '

The Amazon Resource Name (ARN) of the new model package.

', 'DescribeModelPackageOutput$ModelPackageArn' => '

The Amazon Resource Name (ARN) of the model package.

', 'ModelPackageSummary$ModelPackageArn' => '

The Amazon Resource Name (ARN) of the model package.

', ], ], 'ModelPackageContainerDefinition' => [ 'base' => '

Describes the Docker container for the model package.

', 'refs' => [ 'ModelPackageContainerDefinitionList$member' => NULL, ], ], 'ModelPackageContainerDefinitionList' => [ 'base' => NULL, 'refs' => [ 'InferenceSpecification$Containers' => '

The Amazon ECR registry path of the Docker image that contains the inference code.

', ], ], 'ModelPackageSortBy' => [ 'base' => NULL, 'refs' => [ 'ListModelPackagesInput$SortBy' => '

The parameter by which to sort the results. The default is CreationTime.

', ], ], 'ModelPackageStatus' => [ 'base' => NULL, 'refs' => [ 'DescribeModelPackageOutput$ModelPackageStatus' => '

The current status of the model package.

', 'ModelPackageSummary$ModelPackageStatus' => '

The overall status of the model package.

', ], ], 'ModelPackageStatusDetails' => [ 'base' => '

Specifies the validation and image scan statuses of the model package.

', 'refs' => [ 'DescribeModelPackageOutput$ModelPackageStatusDetails' => '

Details about the current status of the model package.

', ], ], 'ModelPackageStatusItem' => [ 'base' => '

Represents the overall status of a model package.

', 'refs' => [ 'ModelPackageStatusItemList$member' => NULL, ], ], 'ModelPackageStatusItemList' => [ 'base' => NULL, 'refs' => [ 'ModelPackageStatusDetails$ValidationStatuses' => '

The validation status of the model package.

', 'ModelPackageStatusDetails$ImageScanStatuses' => '

The status of the scan of the Docker image container for the model package.

', ], ], 'ModelPackageSummary' => [ 'base' => '

Provides summary information about a model package.

', 'refs' => [ 'ModelPackageSummaryList$member' => NULL, ], ], 'ModelPackageSummaryList' => [ 'base' => NULL, 'refs' => [ 'ListModelPackagesOutput$ModelPackageSummaryList' => '

An array of ModelPackageSummary objects, each of which lists a model package.

', ], ], 'ModelPackageValidationProfile' => [ 'base' => '

Contains data, such as the inputs and targeted instance types that are used in the process of validating the model package.

The data provided in the validation profile is made available to your buyers on AWS Marketplace.

', 'refs' => [ 'ModelPackageValidationProfiles$member' => NULL, ], ], 'ModelPackageValidationProfiles' => [ 'base' => NULL, 'refs' => [ 'ModelPackageValidationSpecification$ValidationProfiles' => '

An array of ModelPackageValidationProfile objects, each of which specifies a batch transform job that Amazon SageMaker runs to validate your model package.

', ], ], 'ModelPackageValidationSpecification' => [ 'base' => '

Specifies batch transform jobs that Amazon SageMaker runs to validate your model package.

', 'refs' => [ 'CreateModelPackageInput$ValidationSpecification' => '

Specifies configurations for one or more transform jobs that Amazon SageMaker runs to test the model package.

', 'DescribeModelPackageOutput$ValidationSpecification' => '

Configurations for one or more transform jobs that Amazon SageMaker runs to test the model package.

', ], ], 'ModelSortKey' => [ 'base' => NULL, 'refs' => [ 'ListModelsInput$SortBy' => '

Sorts the list of results. The default is CreationTime.

', ], ], 'ModelSummary' => [ 'base' => '

Provides summary information about a model.

', 'refs' => [ 'ModelSummaryList$member' => NULL, ], ], 'ModelSummaryList' => [ 'base' => NULL, 'refs' => [ 'ListModelsOutput$Models' => '

An array of ModelSummary objects, each of which lists a model.

', ], ], 'NameContains' => [ 'base' => NULL, 'refs' => [ 'ListAlgorithmsInput$NameContains' => '

A string in the algorithm name. This filter returns only algorithms whose name contains the specified string.

', 'ListCompilationJobsRequest$NameContains' => '

A filter that returns the model compilation jobs whose name contains a specified string.

', 'ListHyperParameterTuningJobsRequest$NameContains' => '

A string in the tuning job name. This filter returns only tuning jobs whose name contains the specified string.

', 'ListLabelingJobsRequest$NameContains' => '

A string in the labeling job name. This filter returns only labeling jobs whose name contains the specified string.

', 'ListModelPackagesInput$NameContains' => '

A string in the model package name. This filter returns only model packages whose name contains the specified string.

', 'ListTrainingJobsRequest$NameContains' => '

A string in the training job name. This filter returns only training jobs whose name contains the specified string.

', 'ListTransformJobsRequest$NameContains' => '

A string in the transform job name. This filter returns only transform jobs whose name contains the specified string.

', ], ], 'NestedFilters' => [ 'base' => '

Defines a list of NestedFilters objects. To satisfy the conditions specified in the NestedFilters call, a resource must satisfy the conditions of all of the filters.

For example, you could define a NestedFilters using the training job\'s InputDataConfig property to filter on Channel objects.

A NestedFilters object contains multiple filters. For example, to find all training jobs whose name contains train and that have cat/data in their S3Uri (specified in InputDataConfig), you need to create a NestedFilters object that specifies the InputDataConfig property with the following Filter objects:

  • \'{Name:"InputDataConfig.ChannelName", "Operator":"EQUALS", "Value":"train"}\',

  • \'{Name:"InputDataConfig.DataSource.S3DataSource.S3Uri", "Operator":"CONTAINS", "Value":"cat/data"}\'

', 'refs' => [ 'NestedFiltersList$member' => NULL, ], ], 'NestedFiltersList' => [ 'base' => NULL, 'refs' => [ 'SearchExpression$NestedFilters' => '

A list of nested filter objects.

', ], ], 'NetworkInterfaceId' => [ 'base' => NULL, 'refs' => [ 'DescribeNotebookInstanceOutput$NetworkInterfaceId' => '

The network interface IDs that Amazon SageMaker created at the time of creating the instance.

', ], ], 'NextToken' => [ 'base' => NULL, 'refs' => [ 'ListAlgorithmsInput$NextToken' => '

If the response to a previous ListAlgorithms request was truncated, the response includes a NextToken. To retrieve the next set of algorithms, use the token in the next request.

', 'ListAlgorithmsOutput$NextToken' => '

If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of algorithms, use it in the subsequent request.

', 'ListCodeRepositoriesInput$NextToken' => '

If the result of a ListCodeRepositoriesOutput request was truncated, the response includes a NextToken. To get the next set of Git repositories, use the token in the next request.

', 'ListCodeRepositoriesOutput$NextToken' => '

If the result of a ListCodeRepositoriesOutput request was truncated, the response includes a NextToken. To get the next set of Git repositories, use the token in the next request.

', 'ListCompilationJobsRequest$NextToken' => '

If the result of the previous ListCompilationJobs request was truncated, the response includes a NextToken. To retrieve the next set of model compilation jobs, use the token in the next request.

', 'ListCompilationJobsResponse$NextToken' => '

If the response is truncated, Amazon SageMaker returns this NextToken. To retrieve the next set of model compilation jobs, use this token in the next request.

', 'ListHyperParameterTuningJobsRequest$NextToken' => '

If the result of the previous ListHyperParameterTuningJobs request was truncated, the response includes a NextToken. To retrieve the next set of tuning jobs, use the token in the next request.

', 'ListHyperParameterTuningJobsResponse$NextToken' => '

If the result of this ListHyperParameterTuningJobs request was truncated, the response includes a NextToken. To retrieve the next set of tuning jobs, use the token in the next request.

', 'ListLabelingJobsForWorkteamRequest$NextToken' => '

If the result of the previous ListLabelingJobsForWorkteam request was truncated, the response includes a NextToken. To retrieve the next set of labeling jobs, use the token in the next request.

', 'ListLabelingJobsForWorkteamResponse$NextToken' => '

If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of labeling jobs, use it in the subsequent request.

', 'ListLabelingJobsRequest$NextToken' => '

If the result of the previous ListLabelingJobs request was truncated, the response includes a NextToken. To retrieve the next set of labeling jobs, use the token in the next request.

', 'ListLabelingJobsResponse$NextToken' => '

If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of labeling jobs, use it in the subsequent request.

', 'ListModelPackagesInput$NextToken' => '

If the response to a previous ListModelPackages request was truncated, the response includes a NextToken. To retrieve the next set of model packages, use the token in the next request.

', 'ListModelPackagesOutput$NextToken' => '

If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of model packages, use it in the subsequent request.

', 'ListNotebookInstanceLifecycleConfigsInput$NextToken' => '

If the result of a ListNotebookInstanceLifecycleConfigs request was truncated, the response includes a NextToken. To get the next set of lifecycle configurations, use the token in the next request.

', 'ListNotebookInstanceLifecycleConfigsOutput$NextToken' => '

If the response is truncated, Amazon SageMaker returns this token. To get the next set of lifecycle configurations, use it in the next request.

', 'ListNotebookInstancesInput$NextToken' => '

If the previous call to the ListNotebookInstances is truncated, the response includes a NextToken. You can use this token in your subsequent ListNotebookInstances request to fetch the next set of notebook instances.

You might specify a filter or a sort order in your request. When response is truncated, you must use the same values for the filer and sort order in the next request.

', 'ListNotebookInstancesOutput$NextToken' => '

If the response to the previous ListNotebookInstances request was truncated, Amazon SageMaker returns this token. To retrieve the next set of notebook instances, use the token in the next request.

', 'ListSubscribedWorkteamsRequest$NextToken' => '

If the result of the previous ListSubscribedWorkteams request was truncated, the response includes a NextToken. To retrieve the next set of labeling jobs, use the token in the next request.

', 'ListSubscribedWorkteamsResponse$NextToken' => '

If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of work teams, use it in the subsequent request.

', 'ListTagsInput$NextToken' => '

If the response to the previous ListTags request is truncated, Amazon SageMaker returns this token. To retrieve the next set of tags, use it in the subsequent request.

', 'ListTagsOutput$NextToken' => '

If response is truncated, Amazon SageMaker includes a token in the response. You can use this token in your subsequent request to fetch next set of tokens.

', 'ListTrainingJobsForHyperParameterTuningJobRequest$NextToken' => '

If the result of the previous ListTrainingJobsForHyperParameterTuningJob request was truncated, the response includes a NextToken. To retrieve the next set of training jobs, use the token in the next request.

', 'ListTrainingJobsForHyperParameterTuningJobResponse$NextToken' => '

If the result of this ListTrainingJobsForHyperParameterTuningJob request was truncated, the response includes a NextToken. To retrieve the next set of training jobs, use the token in the next request.

', 'ListTrainingJobsRequest$NextToken' => '

If the result of the previous ListTrainingJobs request was truncated, the response includes a NextToken. To retrieve the next set of training jobs, use the token in the next request.

', 'ListTrainingJobsResponse$NextToken' => '

If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of training jobs, use it in the subsequent request.

', 'ListTransformJobsRequest$NextToken' => '

If the result of the previous ListTransformJobs request was truncated, the response includes a NextToken. To retrieve the next set of transform jobs, use the token in the next request.

', 'ListTransformJobsResponse$NextToken' => '

If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of transform jobs, use it in the next request.

', 'ListWorkteamsRequest$NextToken' => '

If the result of the previous ListWorkteams request was truncated, the response includes a NextToken. To retrieve the next set of labeling jobs, use the token in the next request.

', 'ListWorkteamsResponse$NextToken' => '

If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of work teams, use it in the subsequent request.

', 'SearchRequest$NextToken' => '

If more than MaxResults resource objects match the specified SearchExpression, the SearchResponse includes a NextToken. The NextToken can be passed to the next SearchRequest to continue retrieving results for the specified SearchExpression and Sort parameters.

', 'SearchResponse$NextToken' => '

If the result of the previous Search request was truncated, the response includes a NextToken. To retrieve the next set of results, use the token in the next request.

', ], ], 'NotebookInstanceAcceleratorType' => [ 'base' => NULL, 'refs' => [ 'NotebookInstanceAcceleratorTypes$member' => NULL, ], ], 'NotebookInstanceAcceleratorTypes' => [ 'base' => NULL, 'refs' => [ 'CreateNotebookInstanceInput$AcceleratorTypes' => '

A list of Elastic Inference (EI) instance types to associate with this notebook instance. Currently, only one instance type can be associated with a notebook instance. For more information, see Using Elastic Inference in Amazon SageMaker.

', 'DescribeNotebookInstanceOutput$AcceleratorTypes' => '

A list of the Elastic Inference (EI) instance types associated with this notebook instance. Currently only one EI instance type can be associated with a notebook instance. For more information, see Using Elastic Inference in Amazon SageMaker.

', 'UpdateNotebookInstanceInput$AcceleratorTypes' => '

A list of the Elastic Inference (EI) instance types to associate with this notebook instance. Currently only one EI instance type can be associated with a notebook instance. For more information, see Using Elastic Inference in Amazon SageMaker.

', ], ], 'NotebookInstanceArn' => [ 'base' => NULL, 'refs' => [ 'CreateNotebookInstanceOutput$NotebookInstanceArn' => '

The Amazon Resource Name (ARN) of the notebook instance.

', 'DescribeNotebookInstanceOutput$NotebookInstanceArn' => '

The Amazon Resource Name (ARN) of the notebook instance.

', 'NotebookInstanceSummary$NotebookInstanceArn' => '

The Amazon Resource Name (ARN) of the notebook instance.

', ], ], 'NotebookInstanceLifecycleConfigArn' => [ 'base' => NULL, 'refs' => [ 'CreateNotebookInstanceLifecycleConfigOutput$NotebookInstanceLifecycleConfigArn' => '

The Amazon Resource Name (ARN) of the lifecycle configuration.

', 'DescribeNotebookInstanceLifecycleConfigOutput$NotebookInstanceLifecycleConfigArn' => '

The Amazon Resource Name (ARN) of the lifecycle configuration.

', 'NotebookInstanceLifecycleConfigSummary$NotebookInstanceLifecycleConfigArn' => '

The Amazon Resource Name (ARN) of the lifecycle configuration.

', ], ], 'NotebookInstanceLifecycleConfigContent' => [ 'base' => NULL, 'refs' => [ 'NotebookInstanceLifecycleHook$Content' => '

A base64-encoded string that contains a shell script for a notebook instance lifecycle configuration.

', ], ], 'NotebookInstanceLifecycleConfigList' => [ 'base' => NULL, 'refs' => [ 'CreateNotebookInstanceLifecycleConfigInput$OnCreate' => '

A shell script that runs only once, when you create a notebook instance. The shell script must be a base64-encoded string.

', 'CreateNotebookInstanceLifecycleConfigInput$OnStart' => '

A shell script that runs every time you start a notebook instance, including when you create the notebook instance. The shell script must be a base64-encoded string.

', 'DescribeNotebookInstanceLifecycleConfigOutput$OnCreate' => '

The shell script that runs only once, when you create a notebook instance.

', 'DescribeNotebookInstanceLifecycleConfigOutput$OnStart' => '

The shell script that runs every time you start a notebook instance, including when you create the notebook instance.

', 'UpdateNotebookInstanceLifecycleConfigInput$OnCreate' => '

The shell script that runs only once, when you create a notebook instance

', 'UpdateNotebookInstanceLifecycleConfigInput$OnStart' => '

The shell script that runs every time you start a notebook instance, including when you create the notebook instance.

', ], ], 'NotebookInstanceLifecycleConfigName' => [ 'base' => NULL, 'refs' => [ 'CreateNotebookInstanceInput$LifecycleConfigName' => '

The name of a lifecycle configuration to associate with the notebook instance. For information about lifestyle configurations, see Step 2.1: (Optional) Customize a Notebook Instance.

', 'CreateNotebookInstanceLifecycleConfigInput$NotebookInstanceLifecycleConfigName' => '

The name of the lifecycle configuration.

', 'DeleteNotebookInstanceLifecycleConfigInput$NotebookInstanceLifecycleConfigName' => '

The name of the lifecycle configuration to delete.

', 'DescribeNotebookInstanceLifecycleConfigInput$NotebookInstanceLifecycleConfigName' => '

The name of the lifecycle configuration to describe.

', 'DescribeNotebookInstanceLifecycleConfigOutput$NotebookInstanceLifecycleConfigName' => '

The name of the lifecycle configuration.

', 'DescribeNotebookInstanceOutput$NotebookInstanceLifecycleConfigName' => '

Returns the name of a notebook instance lifecycle configuration.

For information about notebook instance lifestyle configurations, see Step 2.1: (Optional) Customize a Notebook Instance

', 'ListNotebookInstancesInput$NotebookInstanceLifecycleConfigNameContains' => '

A string in the name of a notebook instances lifecycle configuration associated with this notebook instance. This filter returns only notebook instances associated with a lifecycle configuration with a name that contains the specified string.

', 'NotebookInstanceLifecycleConfigSummary$NotebookInstanceLifecycleConfigName' => '

The name of the lifecycle configuration.

', 'NotebookInstanceSummary$NotebookInstanceLifecycleConfigName' => '

The name of a notebook instance lifecycle configuration associated with this notebook instance.

For information about notebook instance lifestyle configurations, see Step 2.1: (Optional) Customize a Notebook Instance.

', 'UpdateNotebookInstanceInput$LifecycleConfigName' => '

The name of a lifecycle configuration to associate with the notebook instance. For information about lifestyle configurations, see Step 2.1: (Optional) Customize a Notebook Instance.

', 'UpdateNotebookInstanceLifecycleConfigInput$NotebookInstanceLifecycleConfigName' => '

The name of the lifecycle configuration.

', ], ], 'NotebookInstanceLifecycleConfigNameContains' => [ 'base' => NULL, 'refs' => [ 'ListNotebookInstanceLifecycleConfigsInput$NameContains' => '

A string in the lifecycle configuration name. This filter returns only lifecycle configurations whose name contains the specified string.

', ], ], 'NotebookInstanceLifecycleConfigSortKey' => [ 'base' => NULL, 'refs' => [ 'ListNotebookInstanceLifecycleConfigsInput$SortBy' => '

Sorts the list of results. The default is CreationTime.

', ], ], 'NotebookInstanceLifecycleConfigSortOrder' => [ 'base' => NULL, 'refs' => [ 'ListNotebookInstanceLifecycleConfigsInput$SortOrder' => '

The sort order for results.

', ], ], 'NotebookInstanceLifecycleConfigSummary' => [ 'base' => '

Provides a summary of a notebook instance lifecycle configuration.

', 'refs' => [ 'NotebookInstanceLifecycleConfigSummaryList$member' => NULL, ], ], 'NotebookInstanceLifecycleConfigSummaryList' => [ 'base' => NULL, 'refs' => [ 'ListNotebookInstanceLifecycleConfigsOutput$NotebookInstanceLifecycleConfigs' => '

An array of NotebookInstanceLifecycleConfiguration objects, each listing a lifecycle configuration.

', ], ], 'NotebookInstanceLifecycleHook' => [ 'base' => '

Contains the notebook instance lifecycle configuration script.

Each lifecycle configuration script has a limit of 16384 characters.

The value of the $PATH environment variable that is available to both scripts is /sbin:bin:/usr/sbin:/usr/bin.

View CloudWatch Logs for notebook instance lifecycle configurations in log group /aws/sagemaker/NotebookInstances in log stream [notebook-instance-name]/[LifecycleConfigHook].

Lifecycle configuration scripts cannot run for longer than 5 minutes. If a script runs for longer than 5 minutes, it fails and the notebook instance is not created or started.

For information about notebook instance lifestyle configurations, see Step 2.1: (Optional) Customize a Notebook Instance.

', 'refs' => [ 'NotebookInstanceLifecycleConfigList$member' => NULL, ], ], 'NotebookInstanceName' => [ 'base' => NULL, 'refs' => [ 'CreateNotebookInstanceInput$NotebookInstanceName' => '

The name of the new notebook instance.

', 'CreatePresignedNotebookInstanceUrlInput$NotebookInstanceName' => '

The name of the notebook instance.

', 'DeleteNotebookInstanceInput$NotebookInstanceName' => '

The name of the Amazon SageMaker notebook instance to delete.

', 'DescribeNotebookInstanceInput$NotebookInstanceName' => '

The name of the notebook instance that you want information about.

', 'DescribeNotebookInstanceOutput$NotebookInstanceName' => '

The name of the Amazon SageMaker notebook instance.

', 'NotebookInstanceSummary$NotebookInstanceName' => '

The name of the notebook instance that you want a summary for.

', 'StartNotebookInstanceInput$NotebookInstanceName' => '

The name of the notebook instance to start.

', 'StopNotebookInstanceInput$NotebookInstanceName' => '

The name of the notebook instance to terminate.

', 'UpdateNotebookInstanceInput$NotebookInstanceName' => '

The name of the notebook instance to update.

', ], ], 'NotebookInstanceNameContains' => [ 'base' => NULL, 'refs' => [ 'ListNotebookInstancesInput$NameContains' => '

A string in the notebook instances\' name. This filter returns only notebook instances whose name contains the specified string.

', ], ], 'NotebookInstanceSortKey' => [ 'base' => NULL, 'refs' => [ 'ListNotebookInstancesInput$SortBy' => '

The field to sort results by. The default is Name.

', ], ], 'NotebookInstanceSortOrder' => [ 'base' => NULL, 'refs' => [ 'ListNotebookInstancesInput$SortOrder' => '

The sort order for results.

', ], ], 'NotebookInstanceStatus' => [ 'base' => NULL, 'refs' => [ 'DescribeNotebookInstanceOutput$NotebookInstanceStatus' => '

The status of the notebook instance.

', 'ListNotebookInstancesInput$StatusEquals' => '

A filter that returns only notebook instances with the specified status.

', 'NotebookInstanceSummary$NotebookInstanceStatus' => '

The status of the notebook instance.

', ], ], 'NotebookInstanceSummary' => [ 'base' => '

Provides summary information for an Amazon SageMaker notebook instance.

', 'refs' => [ 'NotebookInstanceSummaryList$member' => NULL, ], ], 'NotebookInstanceSummaryList' => [ 'base' => NULL, 'refs' => [ 'ListNotebookInstancesOutput$NotebookInstances' => '

An array of NotebookInstanceSummary objects, one for each notebook instance.

', ], ], 'NotebookInstanceUrl' => [ 'base' => NULL, 'refs' => [ 'CreatePresignedNotebookInstanceUrlOutput$AuthorizedUrl' => '

A JSON object that contains the URL string.

', 'DescribeNotebookInstanceOutput$Url' => '

The URL that you use to connect to the Jupyter notebook that is running in your notebook instance.

', 'NotebookInstanceSummary$Url' => '

The URL that you use to connect to the Jupyter instance running in your notebook instance.

', ], ], 'NotebookInstanceVolumeSizeInGB' => [ 'base' => NULL, 'refs' => [ 'CreateNotebookInstanceInput$VolumeSizeInGB' => '

The size, in GB, of the ML storage volume to attach to the notebook instance. The default value is 5 GB.

', 'DescribeNotebookInstanceOutput$VolumeSizeInGB' => '

The size, in GB, of the ML storage volume attached to the notebook instance.

', 'UpdateNotebookInstanceInput$VolumeSizeInGB' => '

The size, in GB, of the ML storage volume to attach to the notebook instance. The default value is 5 GB. ML storage volumes are encrypted, so Amazon SageMaker can\'t determine the amount of available free space on the volume. Because of this, you can increase the volume size when you update a notebook instance, but you can\'t decrease the volume size. If you want to decrease the size of the ML storage volume in use, create a new notebook instance with the desired size.

', ], ], 'NotificationConfiguration' => [ 'base' => '

Configures SNS notifications of available or expiring work items for work teams.

', 'refs' => [ 'CreateWorkteamRequest$NotificationConfiguration' => '

Configures notification of workers regarding available or expiring work items.

', 'UpdateWorkteamRequest$NotificationConfiguration' => '

Configures SNS topic notifications for available or expiring work items

', 'Workteam$NotificationConfiguration' => NULL, ], ], 'NotificationTopicArn' => [ 'base' => NULL, 'refs' => [ 'NotificationConfiguration$NotificationTopicArn' => '

The ARN for the SNS topic to which notifications should be published.

', ], ], 'NumberOfHumanWorkersPerDataObject' => [ 'base' => NULL, 'refs' => [ 'HumanTaskConfig$NumberOfHumanWorkersPerDataObject' => '

The number of human workers that will label an object.

', 'LabelingJobForWorkteamSummary$NumberOfHumanWorkersPerDataObject' => '

The configured number of workers per data object.

', ], ], 'ObjectiveStatus' => [ 'base' => NULL, 'refs' => [ 'HyperParameterTrainingJobSummary$ObjectiveStatus' => '

The status of the objective metric for the training job:

  • Succeeded: The final objective metric for the training job was evaluated by the hyperparameter tuning job and used in the hyperparameter tuning process.

  • Pending: The training job is in progress and evaluation of its final objective metric is pending.

  • Failed: The final objective metric for the training job was not evaluated, and was not used in the hyperparameter tuning process. This typically occurs when the training job failed or did not emit an objective metric.

', ], ], 'ObjectiveStatusCounter' => [ 'base' => NULL, 'refs' => [ 'ObjectiveStatusCounters$Succeeded' => '

The number of training jobs whose final objective metric was evaluated by the hyperparameter tuning job and used in the hyperparameter tuning process.

', 'ObjectiveStatusCounters$Pending' => '

The number of training jobs that are in progress and pending evaluation of their final objective metric.

', 'ObjectiveStatusCounters$Failed' => '

The number of training jobs whose final objective metric was not evaluated and used in the hyperparameter tuning process. This typically occurs when the training job failed or did not emit an objective metric.

', ], ], 'ObjectiveStatusCounters' => [ 'base' => '

Specifies the number of training jobs that this hyperparameter tuning job launched, categorized by the status of their objective metric. The objective metric status shows whether the final objective metric for the training job has been evaluated by the tuning job and used in the hyperparameter tuning process.

', 'refs' => [ 'DescribeHyperParameterTuningJobResponse$ObjectiveStatusCounters' => '

The ObjectiveStatusCounters object that specifies the number of training jobs, categorized by the status of their final objective metric, that this tuning job launched.

', 'HyperParameterTuningJobSummary$ObjectiveStatusCounters' => '

The ObjectiveStatusCounters object that specifies the numbers of training jobs, categorized by objective metric status, that this tuning job launched.

', ], ], 'Operator' => [ 'base' => NULL, 'refs' => [ 'Filter$Operator' => '

A Boolean binary operator that is used to evaluate the filter. The operator field contains one of the following values:

Equals

The specified resource in Name equals the specified Value.

NotEquals

The specified resource in Name does not equal the specified Value.

GreaterThan

The specified resource in Name is greater than the specified Value. Not supported for text-based properties.

GreaterThanOrEqualTo

The specified resource in Name is greater than or equal to the specified Value. Not supported for text-based properties.

LessThan

The specified resource in Name is less than the specified Value. Not supported for text-based properties.

LessThanOrEqualTo

The specified resource in Name is less than or equal to the specified Value. Not supported for text-based properties.

Contains

Only supported for text-based properties. The word-list of the property contains the specified Value.

If you have specified a filter Value, the default is Equals.

', ], ], 'OrderKey' => [ 'base' => NULL, 'refs' => [ 'ListEndpointConfigsInput$SortOrder' => '

The sort order for results. The default is Descending.

', 'ListEndpointsInput$SortOrder' => '

The sort order for results. The default is Descending.

', 'ListModelsInput$SortOrder' => '

The sort order for results. The default is Descending.

', ], ], 'OutputConfig' => [ 'base' => '

Contains information about the output location for the compiled model and the device (target) that the model runs on.

', 'refs' => [ 'CreateCompilationJobRequest$OutputConfig' => '

Provides information about the output location for the compiled model and the target device the model runs on.

', 'DescribeCompilationJobResponse$OutputConfig' => '

Information about the output location for the compiled model and the target device that the model runs on.

', ], ], 'OutputDataConfig' => [ 'base' => '

Provides information about how to store model training results (model artifacts).

', 'refs' => [ 'CreateTrainingJobRequest$OutputDataConfig' => '

Specifies the path to the S3 bucket where you want to store model artifacts. Amazon SageMaker creates subfolders for the artifacts.

', 'DescribeTrainingJobResponse$OutputDataConfig' => '

The S3 path where model artifacts that you configured when creating the job are stored. Amazon SageMaker creates subfolders for model artifacts.

', 'HyperParameterTrainingJobDefinition$OutputDataConfig' => '

Specifies the path to the Amazon S3 bucket where you store model artifacts from the training jobs that the tuning job launches.

', 'TrainingJob$OutputDataConfig' => '

The S3 path where model artifacts that you configured when creating the job are stored. Amazon SageMaker creates subfolders for model artifacts.

', 'TrainingJobDefinition$OutputDataConfig' => '

the path to the S3 bucket where you want to store model artifacts. Amazon SageMaker creates subfolders for the artifacts.

', ], ], 'PaginationToken' => [ 'base' => NULL, 'refs' => [ 'ListEndpointConfigsInput$NextToken' => '

If the result of the previous ListEndpointConfig request was truncated, the response includes a NextToken. To retrieve the next set of endpoint configurations, use the token in the next request.

', 'ListEndpointConfigsOutput$NextToken' => '

If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of endpoint configurations, use it in the subsequent request

', 'ListEndpointsInput$NextToken' => '

If the result of a ListEndpoints request was truncated, the response includes a NextToken. To retrieve the next set of endpoints, use the token in the next request.

', 'ListEndpointsOutput$NextToken' => '

If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of training jobs, use it in the subsequent request.

', 'ListModelsInput$NextToken' => '

If the response to a previous ListModels request was truncated, the response includes a NextToken. To retrieve the next set of models, use the token in the next request.

', 'ListModelsOutput$NextToken' => '

If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of models, use it in the subsequent request.

', ], ], 'ParameterKey' => [ 'base' => NULL, 'refs' => [ 'CategoricalParameterRange$Name' => '

The name of the categorical hyperparameter to tune.

', 'ContinuousParameterRange$Name' => '

The name of the continuous hyperparameter to tune.

', 'HyperParameters$key' => NULL, 'IntegerParameterRange$Name' => '

The name of the hyperparameter to search.

', ], ], 'ParameterName' => [ 'base' => NULL, 'refs' => [ 'HyperParameterSpecification$Name' => '

The name of this hyperparameter. The name must be unique.

', ], ], 'ParameterRange' => [ 'base' => '

Defines the possible values for categorical, continuous, and integer hyperparameters to be used by an algorithm.

', 'refs' => [ 'HyperParameterSpecification$Range' => '

The allowed range for this hyperparameter.

', ], ], 'ParameterRanges' => [ 'base' => '

Specifies ranges of integer, continuous, and categorical hyperparameters that a hyperparameter tuning job searches. The hyperparameter tuning job launches training jobs with hyperparameter values within these ranges to find the combination of values that result in the training job with the best performance as measured by the objective metric of the hyperparameter tuning job.

You can specify a maximum of 20 hyperparameters that a hyperparameter tuning job can search over. Every possible value of a categorical parameter range counts against this limit.

', 'refs' => [ 'HyperParameterTuningJobConfig$ParameterRanges' => '

The ParameterRanges object that specifies the ranges of hyperparameters that this tuning job searches.

', ], ], 'ParameterType' => [ 'base' => NULL, 'refs' => [ 'HyperParameterSpecification$Type' => '

The type of this hyperparameter. The valid types are Integer, Continuous, Categorical, and FreeText.

', ], ], 'ParameterValue' => [ 'base' => NULL, 'refs' => [ 'ContinuousParameterRange$MinValue' => '

The minimum value for the hyperparameter. The tuning job uses floating-point values between this value and MaxValuefor tuning.

', 'ContinuousParameterRange$MaxValue' => '

The maximum value for the hyperparameter. The tuning job uses floating-point values between MinValue value and this value for tuning.

', 'ContinuousParameterRangeSpecification$MinValue' => '

The minimum floating-point value allowed.

', 'ContinuousParameterRangeSpecification$MaxValue' => '

The maximum floating-point value allowed.

', 'HyperParameterSpecification$DefaultValue' => '

The default value for this hyperparameter. If a default value is specified, a hyperparameter cannot be required.

', 'HyperParameters$value' => NULL, 'IntegerParameterRange$MinValue' => '

The minimum value of the hyperparameter to search.

', 'IntegerParameterRange$MaxValue' => '

The maximum value of the hyperparameter to search.

', 'IntegerParameterRangeSpecification$MinValue' => '

The minimum integer value allowed.

', 'IntegerParameterRangeSpecification$MaxValue' => '

The maximum integer value allowed.

', 'ParameterValues$member' => NULL, ], ], 'ParameterValues' => [ 'base' => NULL, 'refs' => [ 'CategoricalParameterRange$Values' => '

A list of the categories for the hyperparameter.

', 'CategoricalParameterRangeSpecification$Values' => '

The allowed categories for the hyperparameter.

', ], ], 'ParentHyperParameterTuningJob' => [ 'base' => '

A previously completed or stopped hyperparameter tuning job to be used as a starting point for a new hyperparameter tuning job.

', 'refs' => [ 'ParentHyperParameterTuningJobs$member' => NULL, ], ], 'ParentHyperParameterTuningJobs' => [ 'base' => NULL, 'refs' => [ 'HyperParameterTuningJobWarmStartConfig$ParentHyperParameterTuningJobs' => '

An array of hyperparameter tuning jobs that are used as the starting point for the new hyperparameter tuning job. For more information about warm starting a hyperparameter tuning job, see Using a Previous Hyperparameter Tuning Job as a Starting Point.

Hyperparameter tuning jobs created before October 1, 2018 cannot be used as parent jobs for warm start tuning jobs.

', ], ], 'ProductId' => [ 'base' => NULL, 'refs' => [ 'DescribeAlgorithmOutput$ProductId' => '

The product identifier of the algorithm.

', 'ModelPackageContainerDefinition$ProductId' => '

The AWS Marketplace product ID of the model package.

', ], ], 'ProductListings' => [ 'base' => NULL, 'refs' => [ 'Workteam$ProductListingIds' => '

The Amazon Marketplace identifier for a vendor\'s work team.

', ], ], 'ProductionVariant' => [ 'base' => '

Identifies a model that you want to host and the resources to deploy for hosting it. If you are deploying multiple models, tell Amazon SageMaker how to distribute traffic among the models by specifying variant weights.

', 'refs' => [ 'ProductionVariantList$member' => NULL, ], ], 'ProductionVariantAcceleratorType' => [ 'base' => NULL, 'refs' => [ 'ProductionVariant$AcceleratorType' => '

The size of the Elastic Inference (EI) instance to use for the production variant. EI instances provide on-demand GPU computing for inference. For more information, see Using Elastic Inference in Amazon SageMaker. For more information, see Using Elastic Inference in Amazon SageMaker.

', ], ], 'ProductionVariantInstanceType' => [ 'base' => NULL, 'refs' => [ 'ProductionVariant$InstanceType' => '

The ML compute instance type.

', 'RealtimeInferenceInstanceTypes$member' => NULL, ], ], 'ProductionVariantList' => [ 'base' => NULL, 'refs' => [ 'CreateEndpointConfigInput$ProductionVariants' => '

An list of ProductionVariant objects, one for each model that you want to host at this endpoint.

', 'DescribeEndpointConfigOutput$ProductionVariants' => '

An array of ProductionVariant objects, one for each model that you want to host at this endpoint.

', ], ], 'ProductionVariantSummary' => [ 'base' => '

Describes weight and capacities for a production variant associated with an endpoint. If you sent a request to the UpdateEndpointWeightsAndCapacities API and the endpoint status is Updating, you get different desired and current values.

', 'refs' => [ 'ProductionVariantSummaryList$member' => NULL, ], ], 'ProductionVariantSummaryList' => [ 'base' => NULL, 'refs' => [ 'DescribeEndpointOutput$ProductionVariants' => '

An array of ProductionVariantSummary objects, one for each model hosted behind this endpoint.

', ], ], 'PropertyNameHint' => [ 'base' => NULL, 'refs' => [ 'PropertyNameQuery$PropertyNameHint' => '

Text that is part of a property\'s name. The property names of hyperparameter, metric, and tag key names that begin with the specified text in the PropertyNameHint.

', ], ], 'PropertyNameQuery' => [ 'base' => '

A type of SuggestionQuery. A suggestion query for retrieving property names that match the specified hint.

', 'refs' => [ 'SuggestionQuery$PropertyNameQuery' => '

A type of SuggestionQuery. Defines a property name hint. Only property names that match the specified hint are included in the response.

', ], ], 'PropertyNameSuggestion' => [ 'base' => '

A property name returned from a GetSearchSuggestions call that specifies a value in the PropertyNameQuery field.

', 'refs' => [ 'PropertyNameSuggestionList$member' => NULL, ], ], 'PropertyNameSuggestionList' => [ 'base' => NULL, 'refs' => [ 'GetSearchSuggestionsResponse$PropertyNameSuggestions' => '

A list of property names for a Resource that match a SuggestionQuery.

', ], ], 'PublicWorkforceTaskPrice' => [ 'base' => '

Defines the amount of money paid to an Amazon Mechanical Turk worker for each task performed.

Use one of the following prices for bounding box tasks. Prices are in US dollars.

  • 0.036

  • 0.048

  • 0.060

  • 0.072

  • 0.120

  • 0.240

  • 0.360

  • 0.480

  • 0.600

  • 0.720

  • 0.840

  • 0.960

  • 1.080

  • 1.200

Use one of the following prices for image classification, text classification, and custom tasks. Prices are in US dollars.

  • 0.012

  • 0.024

  • 0.036

  • 0.048

  • 0.060

  • 0.072

  • 0.120

  • 0.240

  • 0.360

  • 0.480

  • 0.600

  • 0.720

  • 0.840

  • 0.960

  • 1.080

  • 1.200

Use one of the following prices for semantic segmentation tasks. Prices are in US dollars.

  • 0.840

  • 0.960

  • 1.080

  • 1.200

', 'refs' => [ 'HumanTaskConfig$PublicWorkforceTaskPrice' => '

The price that you pay for each task performed by a public worker.

', ], ], 'RealtimeInferenceInstanceTypes' => [ 'base' => NULL, 'refs' => [ 'InferenceSpecification$SupportedRealtimeInferenceInstanceTypes' => '

A list of the instance types that are used to generate inferences in real-time.

', ], ], 'RecordWrapper' => [ 'base' => NULL, 'refs' => [ 'Channel$RecordWrapperType' => '

Specify RecordIO as the value when input data is in raw format but the training algorithm requires the RecordIO format. In this case, Amazon SageMaker wraps each individual S3 object in a RecordIO record. If the input data is already in RecordIO format, you don\'t need to set this attribute. For more information, see Create a Dataset Using RecordIO.

In File mode, leave this field unset or set it to None.

', ], ], 'RenderUiTemplateRequest' => [ 'base' => NULL, 'refs' => [], ], 'RenderUiTemplateResponse' => [ 'base' => NULL, 'refs' => [], ], 'RenderableTask' => [ 'base' => '

Contains input values for a task.

', 'refs' => [ 'RenderUiTemplateRequest$Task' => '

A RenderableTask object containing a representative task to render.

', ], ], 'RenderingError' => [ 'base' => '

A description of an error that occurred while rendering the template.

', 'refs' => [ 'RenderingErrorList$member' => NULL, ], ], 'RenderingErrorList' => [ 'base' => NULL, 'refs' => [ 'RenderUiTemplateResponse$Errors' => '

A list of one or more RenderingError objects if any were encountered while rendering the template. If there were no errors, the list is empty.

', ], ], 'ResourceArn' => [ 'base' => NULL, 'refs' => [ 'AddTagsInput$ResourceArn' => '

The Amazon Resource Name (ARN) of the resource that you want to tag.

', 'DeleteTagsInput$ResourceArn' => '

The Amazon Resource Name (ARN) of the resource whose tags you want to delete.

', 'ListTagsInput$ResourceArn' => '

The Amazon Resource Name (ARN) of the resource whose tags you want to retrieve.

', ], ], 'ResourceConfig' => [ 'base' => '

Describes the resources, including ML compute instances and ML storage volumes, to use for model training.

', 'refs' => [ 'CreateTrainingJobRequest$ResourceConfig' => '

The resources, including the ML compute instances and ML storage volumes, to use for model training.

ML storage volumes store model artifacts and incremental states. Training algorithms might also use ML storage volumes for scratch space. If you want Amazon SageMaker to use the ML storage volume to store the training data, choose File as the TrainingInputMode in the algorithm specification. For distributed training algorithms, specify an instance count greater than 1.

', 'DescribeTrainingJobResponse$ResourceConfig' => '

Resources, including ML compute instances and ML storage volumes, that are configured for model training.

', 'HyperParameterTrainingJobDefinition$ResourceConfig' => '

The resources, including the compute instances and storage volumes, to use for the training jobs that the tuning job launches.

Storage volumes store model artifacts and incremental states. Training algorithms might also use storage volumes for scratch space. If you want Amazon SageMaker to use the storage volume to store the training data, choose File as the TrainingInputMode in the algorithm specification. For distributed training algorithms, specify an instance count greater than 1.

', 'TrainingJob$ResourceConfig' => '

Resources, including ML compute instances and ML storage volumes, that are configured for model training.

', 'TrainingJobDefinition$ResourceConfig' => '

The resources, including the ML compute instances and ML storage volumes, to use for model training.

', ], ], 'ResourceInUse' => [ 'base' => '

Resource being accessed is in use.

', 'refs' => [], ], 'ResourceLimitExceeded' => [ 'base' => '

You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.

', 'refs' => [], ], 'ResourceLimits' => [ 'base' => '

Specifies the maximum number of training jobs and parallel training jobs that a hyperparameter tuning job can launch.

', 'refs' => [ 'HyperParameterTuningJobConfig$ResourceLimits' => '

The ResourceLimits object that specifies the maximum number of training jobs and parallel training jobs for this tuning job.

', 'HyperParameterTuningJobSummary$ResourceLimits' => '

The ResourceLimits object that specifies the maximum number of training jobs and parallel training jobs allowed for this tuning job.

', ], ], 'ResourceNotFound' => [ 'base' => '

Resource being access is not found.

', 'refs' => [], ], 'ResourcePropertyName' => [ 'base' => NULL, 'refs' => [ 'Filter$Name' => '

A property name. For example, TrainingJobName. For the list of valid property names returned in a search result for each supported resource, see TrainingJob properties. You must specify a valid property name for the resource.

', 'NestedFilters$NestedPropertyName' => '

The name of the property to use in the nested filters. The value must match a listed property name, such as InputDataConfig.

', 'PropertyNameSuggestion$PropertyName' => '

A suggested property name based on what you entered in the search textbox in the Amazon SageMaker console.

', 'SearchRequest$SortBy' => '

The name of the resource property used to sort the SearchResults. The default is LastModifiedTime.

', ], ], 'ResourceType' => [ 'base' => NULL, 'refs' => [ 'GetSearchSuggestionsRequest$Resource' => '

The name of the Amazon SageMaker resource to Search for. The only valid Resource value is TrainingJob.

', 'SearchRequest$Resource' => '

The name of the Amazon SageMaker resource to search for. Currently, the only valid Resource value is TrainingJob.

', ], ], 'ResponseMIMEType' => [ 'base' => NULL, 'refs' => [ 'ResponseMIMETypes$member' => NULL, ], ], 'ResponseMIMETypes' => [ 'base' => NULL, 'refs' => [ 'InferenceSpecification$SupportedResponseMIMETypes' => '

The supported MIME types for the output data.

', ], ], 'RoleArn' => [ 'base' => NULL, 'refs' => [ 'AlgorithmValidationSpecification$ValidationRole' => '

The IAM roles that Amazon SageMaker uses to run the training jobs.

', 'CreateCompilationJobRequest$RoleArn' => '

The Amazon Resource Name (ARN) of an IAM role that enables Amazon SageMaker to perform tasks on your behalf.

During model compilation, Amazon SageMaker needs your permission to:

  • Read input data from an S3 bucket

  • Write model artifacts to an S3 bucket

  • Write logs to Amazon CloudWatch Logs

  • Publish metrics to Amazon CloudWatch

You grant permissions for all of these tasks to an IAM role. To pass this role to Amazon SageMaker, the caller of this API must have the iam:PassRole permission. For more information, see Amazon SageMaker Roles.

', 'CreateLabelingJobRequest$RoleArn' => '

The Amazon Resource Number (ARN) that Amazon SageMaker assumes to perform tasks on your behalf during data labeling. You must grant this role the necessary permissions so that Amazon SageMaker can successfully complete data labeling.

', 'CreateModelInput$ExecutionRoleArn' => '

The Amazon Resource Name (ARN) of the IAM role that Amazon SageMaker can assume to access model artifacts and docker image for deployment on ML compute instances or for batch transform jobs. Deploying on ML compute instances is part of model hosting. For more information, see Amazon SageMaker Roles.

To be able to pass this role to Amazon SageMaker, the caller of this API must have the iam:PassRole permission.

', 'CreateNotebookInstanceInput$RoleArn' => '

When you send any requests to AWS resources from the notebook instance, Amazon SageMaker assumes this role to perform tasks on your behalf. You must grant this role necessary permissions so Amazon SageMaker can perform these tasks. The policy must allow the Amazon SageMaker service principal (sagemaker.amazonaws.com) permissions to assume this role. For more information, see Amazon SageMaker Roles.

To be able to pass this role to Amazon SageMaker, the caller of this API must have the iam:PassRole permission.

', 'CreateTrainingJobRequest$RoleArn' => '

The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.

During model training, Amazon SageMaker needs your permission to read input data from an S3 bucket, download a Docker image that contains training code, write model artifacts to an S3 bucket, write logs to Amazon CloudWatch Logs, and publish metrics to Amazon CloudWatch. You grant permissions for all of these tasks to an IAM role. For more information, see Amazon SageMaker Roles.

To be able to pass this role to Amazon SageMaker, the caller of this API must have the iam:PassRole permission.

', 'DescribeCompilationJobResponse$RoleArn' => '

The Amazon Resource Name (ARN) of the model compilation job.

', 'DescribeLabelingJobResponse$RoleArn' => '

The Amazon Resource Name (ARN) that Amazon SageMaker assumes to perform tasks on your behalf during data labeling.

', 'DescribeModelOutput$ExecutionRoleArn' => '

The Amazon Resource Name (ARN) of the IAM role that you specified for the model.

', 'DescribeNotebookInstanceOutput$RoleArn' => '

The Amazon Resource Name (ARN) of the IAM role associated with the instance.

', 'DescribeTrainingJobResponse$RoleArn' => '

The AWS Identity and Access Management (IAM) role configured for the training job.

', 'HyperParameterTrainingJobDefinition$RoleArn' => '

The Amazon Resource Name (ARN) of the IAM role associated with the training jobs that the tuning job launches.

', 'ModelPackageValidationSpecification$ValidationRole' => '

The IAM roles to be used for the validation of the model package.

', 'RenderUiTemplateRequest$RoleArn' => '

The Amazon Resource Name (ARN) that has access to the S3 objects that are used by the template.

', 'TrainingJob$RoleArn' => '

The AWS Identity and Access Management (IAM) role configured for the training job.

', 'UpdateNotebookInstanceInput$RoleArn' => '

The Amazon Resource Name (ARN) of the IAM role that Amazon SageMaker can assume to access the notebook instance. For more information, see Amazon SageMaker Roles.

To be able to pass this role to Amazon SageMaker, the caller of this API must have the iam:PassRole permission.

', ], ], 'RootAccess' => [ 'base' => NULL, 'refs' => [ 'CreateNotebookInstanceInput$RootAccess' => '

Whether root access is enabled or disabled for users of the notebook instance. The default value is Enabled.

Lifecycle configurations need root access to be able to set up a notebook instance. Because of this, lifecycle configurations associated with a notebook instance always run with root access even if you disable root access for users.

', 'DescribeNotebookInstanceOutput$RootAccess' => '

Whether root access is enabled or disabled for users of the notebook instance.

Lifecycle configurations need root access to be able to set up a notebook instance. Because of this, lifecycle configurations associated with a notebook instance always run with root access even if you disable root access for users.

', 'UpdateNotebookInstanceInput$RootAccess' => '

Whether root access is enabled or disabled for users of the notebook instance. The default value is Enabled.

If you set this to Disabled, users don\'t have root access on the notebook instance, but lifecycle configuration scripts still run with root permissions.

', ], ], 'S3DataDistribution' => [ 'base' => NULL, 'refs' => [ 'S3DataSource$S3DataDistributionType' => '

If you want Amazon SageMaker to replicate the entire dataset on each ML compute instance that is launched for model training, specify FullyReplicated.

If you want Amazon SageMaker to replicate a subset of data on each ML compute instance that is launched for model training, specify ShardedByS3Key. If there are n ML compute instances launched for a training job, each instance gets approximately 1/n of the number of S3 objects. In this case, model training on each machine uses only the subset of training data.

Don\'t choose more ML compute instances for training than available S3 objects. If you do, some nodes won\'t get any data and you will pay for nodes that aren\'t getting any training data. This applies in both File and Pipe modes. Keep this in mind when developing algorithms.

In distributed training, where you use multiple ML compute EC2 instances, you might choose ShardedByS3Key. If the algorithm requires copying training data to the ML storage volume (when TrainingInputMode is set to File), this copies 1/n of the number of objects.

', ], ], 'S3DataSource' => [ 'base' => '

Describes the S3 data source.

', 'refs' => [ 'DataSource$S3DataSource' => '

The S3 location of the data source that is associated with a channel.

', ], ], 'S3DataType' => [ 'base' => NULL, 'refs' => [ 'S3DataSource$S3DataType' => '

If you choose S3Prefix, S3Uri identifies a key name prefix. Amazon SageMaker uses all objects that match the specified key name prefix for model training.

If you choose ManifestFile, S3Uri identifies an object that is a manifest file containing a list of object keys that you want Amazon SageMaker to use for model training.

If you choose AugmentedManifestFile, S3Uri identifies an object that is an augmented manifest file in JSON lines format. This file contains the data you want to use for model training. AugmentedManifestFile can only be used if the Channel\'s input mode is Pipe.

', 'TransformS3DataSource$S3DataType' => '

If you choose S3Prefix, S3Uri identifies a key name prefix. Amazon SageMaker uses all objects with the specified key name prefix for batch transform.

If you choose ManifestFile, S3Uri identifies an object that is a manifest file containing a list of object keys that you want Amazon SageMaker to use for batch transform.

The following values are compatible: ManifestFile, S3Prefix

The following value is not compatible: AugmentedManifestFile

', ], ], 'S3Uri' => [ 'base' => NULL, 'refs' => [ 'CreateLabelingJobRequest$LabelCategoryConfigS3Uri' => '

The S3 URL of the file that defines the categories used to label the data objects.

The file is a JSON structure in the following format:

{

"document-version": "2018-11-28"

"labels": [

{

"label": "label 1"

},

{

"label": "label 2"

},

...

{

"label": "label n"

}

]

}

', 'DescribeLabelingJobResponse$LabelCategoryConfigS3Uri' => '

The S3 location of the JSON file that defines the categories used to label data objects.

The file is a JSON structure in the following format:

{

"document-version": "2018-11-28"

"labels": [

{

"label": "label 1"

},

{

"label": "label 2"

},

...

{

"label": "label n"

}

]

}

', 'InputConfig$S3Uri' => '

The S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).

', 'LabelingJobOutput$OutputDatasetS3Uri' => '

The Amazon S3 bucket location of the manifest file for labeled data.

', 'LabelingJobOutputConfig$S3OutputPath' => '

The Amazon S3 location to write output data.

', 'LabelingJobS3DataSource$ManifestS3Uri' => '

The Amazon S3 location of the manifest file that describes the input data objects.

', 'ModelArtifacts$S3ModelArtifacts' => '

The path of the S3 object that contains the model artifacts. For example, s3://bucket-name/keynameprefix/model.tar.gz.

', 'OutputConfig$S3OutputLocation' => '

Identifies the S3 path where you want Amazon SageMaker to store the model artifacts. For example, s3://bucket-name/key-name-prefix.

', 'OutputDataConfig$S3OutputPath' => '

Identifies the S3 path where you want Amazon SageMaker to store the model artifacts. For example, s3://bucket-name/key-name-prefix.

', 'S3DataSource$S3Uri' => '

Depending on the value specified for the S3DataType, identifies either a key name prefix or a manifest. For example:

  • A key name prefix might look like this: s3://bucketname/exampleprefix.

  • A manifest might look like this: s3://bucketname/example.manifest

    The manifest is an S3 object which is a JSON file with the following format:

    [

    {"prefix": "s3://customer_bucket/some/prefix/"},

    "relative/path/to/custdata-1",

    "relative/path/custdata-2",

    ...

    ]

    The preceding JSON matches the following s3Uris:

    s3://customer_bucket/some/prefix/relative/path/to/custdata-1

    s3://customer_bucket/some/prefix/relative/path/custdata-2

    ...

    The complete set of s3uris in this manifest is the input data for the channel for this datasource. The object that each s3uris points to must be readable by the IAM role that Amazon SageMaker uses to perform tasks on your behalf.

', 'TransformOutput$S3OutputPath' => '

The Amazon S3 path where you want Amazon SageMaker to store the results of the transform job. For example, s3://bucket-name/key-name-prefix.

For every S3 object used as input for the transform job, batch transform stores the transformed data with an .out suffix in a corresponding subfolder in the location in the output prefix. For example, for the input data stored at s3://bucket-name/input-name-prefix/dataset01/data.csv, batch transform stores the transformed data at s3://bucket-name/output-name-prefix/input-name-prefix/data.csv.out. Batch transform doesn\'t upload partially processed objects. For an input S3 object that contains multiple records, it creates an .out file only if the transform job succeeds on the entire file. When the input contains multiple S3 objects, the batch transform job processes the listed S3 objects and uploads only the output for successfully processed objects. If any object fails in the transform job batch transform marks the job as failed to prompt investigation.

', 'TransformS3DataSource$S3Uri' => '

Depending on the value specified for the S3DataType, identifies either a key name prefix or a manifest. For example:

  • A key name prefix might look like this: s3://bucketname/exampleprefix.

  • A manifest might look like this: s3://bucketname/example.manifest

    The manifest is an S3 object which is a JSON file with the following format:

    [

    {"prefix": "s3://customer_bucket/some/prefix/"},

    "relative/path/to/custdata-1",

    "relative/path/custdata-2",

    ...

    ]

    The preceding JSON matches the following S3Uris:

    s3://customer_bucket/some/prefix/relative/path/to/custdata-1

    s3://customer_bucket/some/prefix/relative/path/custdata-1

    ...

    The complete set of S3Uris in this manifest constitutes the input data for the channel for this datasource. The object that each S3Uris points to must be readable by the IAM role that Amazon SageMaker uses to perform tasks on your behalf.

', 'UiConfig$UiTemplateS3Uri' => '

The Amazon S3 bucket location of the UI template. For more information about the contents of a UI template, see Creating Your Custom Labeling Task Template.

', ], ], 'SearchExpression' => [ 'base' => '

A multi-expression that searches for the specified resource or resources in a search. All resource objects that satisfy the expression\'s condition are included in the search results. You must specify at least one subexpression, filter, or nested filter. A SearchExpression can contain up to twenty elements.

A SearchExpression contains the following components:

  • A list of Filter objects. Each filter defines a simple Boolean expression comprised of a resource property name, Boolean operator, and value.

  • A list of NestedFilter objects. Each nested filter defines a list of Boolean expressions using a list of resource properties. A nested filter is satisfied if a single object in the list satisfies all Boolean expressions.

  • A list of SearchExpression objects. A search expression object can be nested in a list of search expression objects.

  • A Boolean operator: And or Or.

', 'refs' => [ 'SearchExpressionList$member' => NULL, 'SearchRequest$SearchExpression' => '

A Boolean conditional statement. Resource objects must satisfy this condition to be included in search results. You must provide at least one subexpression, filter, or nested filter. The maximum number of recursive SubExpressions, NestedFilters, and Filters that can be included in a SearchExpression object is 50.

', ], ], 'SearchExpressionList' => [ 'base' => NULL, 'refs' => [ 'SearchExpression$SubExpressions' => '

A list of search expression objects.

', ], ], 'SearchRecord' => [ 'base' => '

An individual search result record that contains a single resource object.

', 'refs' => [ 'SearchResultsList$member' => NULL, ], ], 'SearchRequest' => [ 'base' => NULL, 'refs' => [], ], 'SearchResponse' => [ 'base' => NULL, 'refs' => [], ], 'SearchResultsList' => [ 'base' => NULL, 'refs' => [ 'SearchResponse$Results' => '

A list of SearchResult objects.

', ], ], 'SearchSortOrder' => [ 'base' => NULL, 'refs' => [ 'SearchRequest$SortOrder' => '

How SearchResults are ordered. Valid values are Ascending or Descending. The default is Descending.

', ], ], 'SecondaryStatus' => [ 'base' => NULL, 'refs' => [ 'DescribeTrainingJobResponse$SecondaryStatus' => '

Provides detailed information about the state of the training job. For detailed information on the secondary status of the training job, see StatusMessage under SecondaryStatusTransition.

Amazon SageMaker provides primary statuses and secondary statuses that apply to each of them:

InProgress

  • Starting - Starting the training job.

  • Downloading - An optional stage for algorithms that support File training input mode. It indicates that data is being downloaded to the ML storage volumes.

  • Training - Training is in progress.

  • Uploading - Training is complete and the model artifacts are being uploaded to the S3 location.

Completed

  • Completed - The training job has completed.

Failed

  • Failed - The training job has failed. The reason for the failure is returned in the FailureReason field of DescribeTrainingJobResponse.

Stopped

  • MaxRuntimeExceeded - The job stopped because it exceeded the maximum allowed runtime.

  • Stopped - The training job has stopped.

Stopping

  • Stopping - Stopping the training job.

Valid values for SecondaryStatus are subject to change.

We no longer support the following secondary statuses:

  • LaunchingMLInstances

  • PreparingTrainingStack

  • DownloadingTrainingImage

', 'SecondaryStatusTransition$Status' => '

Contains a secondary status information from a training job.

Status might be one of the following secondary statuses:

InProgress

  • Starting - Starting the training job.

  • Downloading - An optional stage for algorithms that support File training input mode. It indicates that data is being downloaded to the ML storage volumes.

  • Training - Training is in progress.

  • Uploading - Training is complete and the model artifacts are being uploaded to the S3 location.

Completed

  • Completed - The training job has completed.

Failed

  • Failed - The training job has failed. The reason for the failure is returned in the FailureReason field of DescribeTrainingJobResponse.

Stopped

  • MaxRuntimeExceeded - The job stopped because it exceeded the maximum allowed runtime.

  • Stopped - The training job has stopped.

Stopping

  • Stopping - Stopping the training job.

We no longer support the following secondary statuses:

  • LaunchingMLInstances

  • PreparingTrainingStack

  • DownloadingTrainingImage

', 'TrainingJob$SecondaryStatus' => '

Provides detailed information about the state of the training job. For detailed information about the secondary status of the training job, see StatusMessage under SecondaryStatusTransition.

Amazon SageMaker provides primary statuses and secondary statuses that apply to each of them:

InProgress

  • Starting - Starting the training job.

  • Downloading - An optional stage for algorithms that support File training input mode. It indicates that data is being downloaded to the ML storage volumes.

  • Training - Training is in progress.

  • Uploading - Training is complete and the model artifacts are being uploaded to the S3 location.

Completed

  • Completed - The training job has completed.

Failed

  • Failed - The training job has failed. The reason for the failure is returned in the FailureReason field of DescribeTrainingJobResponse.

Stopped

  • MaxRuntimeExceeded - The job stopped because it exceeded the maximum allowed runtime.

  • Stopped - The training job has stopped.

Stopping

  • Stopping - Stopping the training job.

Valid values for SecondaryStatus are subject to change.

We no longer support the following secondary statuses:

  • LaunchingMLInstances

  • PreparingTrainingStack

  • DownloadingTrainingImage

', ], ], 'SecondaryStatusTransition' => [ 'base' => '

An array element of DescribeTrainingJobResponse$SecondaryStatusTransitions. It provides additional details about a status that the training job has transitioned through. A training job can be in one of several states, for example, starting, downloading, training, or uploading. Within each state, there are a number of intermediate states. For example, within the starting state, Amazon SageMaker could be starting the training job or launching the ML instances. These transitional states are referred to as the job\'s secondary status.

', 'refs' => [ 'SecondaryStatusTransitions$member' => NULL, ], ], 'SecondaryStatusTransitions' => [ 'base' => NULL, 'refs' => [ 'DescribeTrainingJobResponse$SecondaryStatusTransitions' => '

A history of all of the secondary statuses that the training job has transitioned through.

', 'TrainingJob$SecondaryStatusTransitions' => '

A history of all of the secondary statuses that the training job has transitioned through.

', ], ], 'SecretArn' => [ 'base' => NULL, 'refs' => [ 'GitConfig$SecretArn' => '

The Amazon Resource Name (ARN) of the AWS Secrets Manager secret that contains the credentials used to access the git repository. The secret must have a staging label of AWSCURRENT and must be in the following format:

{"username": UserName, "password": Password}

', 'GitConfigForUpdate$SecretArn' => '

The Amazon Resource Name (ARN) of the AWS Secrets Manager secret that contains the credentials used to access the git repository. The secret must have a staging label of AWSCURRENT and must be in the following format:

{"username": UserName, "password": Password}

', ], ], 'SecurityGroupId' => [ 'base' => NULL, 'refs' => [ 'SecurityGroupIds$member' => NULL, 'VpcSecurityGroupIds$member' => NULL, ], ], 'SecurityGroupIds' => [ 'base' => NULL, 'refs' => [ 'CreateNotebookInstanceInput$SecurityGroupIds' => '

The VPC security group IDs, in the form sg-xxxxxxxx. The security groups must be for the same VPC as specified in the subnet.

', 'DescribeNotebookInstanceOutput$SecurityGroups' => '

The IDs of the VPC security groups.

', ], ], 'Seed' => [ 'base' => NULL, 'refs' => [ 'ShuffleConfig$Seed' => '

Determines the shuffling order in ShuffleConfig value.

', ], ], 'SessionExpirationDurationInSeconds' => [ 'base' => NULL, 'refs' => [ 'CreatePresignedNotebookInstanceUrlInput$SessionExpirationDurationInSeconds' => '

The duration of the session, in seconds. The default is 12 hours.

', ], ], 'ShuffleConfig' => [ 'base' => '

A configuration for a shuffle option for input data in a channel. If you use S3Prefix for S3DataType, the results of the S3 key prefix matches are shuffled. If you use ManifestFile, the order of the S3 object references in the ManifestFile is shuffled. If you use AugmentedManifestFile, the order of the JSON lines in the AugmentedManifestFile is shuffled. The shuffling order is determined using the Seed value.

For Pipe input mode, shuffling is done at the start of every epoch. With large datasets, this ensures that the order of the training data is different for each epoch, and it helps reduce bias and possible overfitting. In a multi-node training job when ShuffleConfig is combined with S3DataDistributionType of ShardedByS3Key, the data is shuffled across nodes so that the content sent to a particular node on the first epoch might be sent to a different node on the second epoch.

', 'refs' => [ 'Channel$ShuffleConfig' => '

A configuration for a shuffle option for input data in a channel. If you use S3Prefix for S3DataType, this shuffles the results of the S3 key prefix matches. If you use ManifestFile, the order of the S3 object references in the ManifestFile is shuffled. If you use AugmentedManifestFile, the order of the JSON lines in the AugmentedManifestFile is shuffled. The shuffling order is determined using the Seed value.

For Pipe input mode, shuffling is done at the start of every epoch. With large datasets this ensures that the order of the training data is different for each epoch, it helps reduce bias and possible overfitting. In a multi-node training job when ShuffleConfig is combined with S3DataDistributionType of ShardedByS3Key, the data is shuffled across nodes so that the content sent to a particular node on the first epoch might be sent to a different node on the second epoch.

', ], ], 'SortBy' => [ 'base' => NULL, 'refs' => [ 'ListLabelingJobsRequest$SortBy' => '

The field to sort results by. The default is CreationTime.

', 'ListTrainingJobsRequest$SortBy' => '

The field to sort results by. The default is CreationTime.

', 'ListTransformJobsRequest$SortBy' => '

The field to sort results by. The default is CreationTime.

', ], ], 'SortOrder' => [ 'base' => NULL, 'refs' => [ 'ListAlgorithmsInput$SortOrder' => '

The sort order for the results. The default is Ascending.

', 'ListCompilationJobsRequest$SortOrder' => '

The sort order for results. The default is Ascending.

', 'ListHyperParameterTuningJobsRequest$SortOrder' => '

The sort order for results. The default is Ascending.

', 'ListLabelingJobsForWorkteamRequest$SortOrder' => '

The sort order for results. The default is Ascending.

', 'ListLabelingJobsRequest$SortOrder' => '

The sort order for results. The default is Ascending.

', 'ListModelPackagesInput$SortOrder' => '

The sort order for the results. The default is Ascending.

', 'ListTrainingJobsForHyperParameterTuningJobRequest$SortOrder' => '

The sort order for results. The default is Ascending.

', 'ListTrainingJobsRequest$SortOrder' => '

The sort order for results. The default is Ascending.

', 'ListTransformJobsRequest$SortOrder' => '

The sort order for results. The default is Descending.

', 'ListWorkteamsRequest$SortOrder' => '

The sort order for results. The default is Ascending.

', ], ], 'SourceAlgorithm' => [ 'base' => '

Specifies an algorithm that was used to create the model package. The algorithm must be either an algorithm resource in your Amazon SageMaker account or an algorithm in AWS Marketplace that you are subscribed to.

', 'refs' => [ 'SourceAlgorithmList$member' => NULL, ], ], 'SourceAlgorithmList' => [ 'base' => NULL, 'refs' => [ 'SourceAlgorithmSpecification$SourceAlgorithms' => '

A list of the algorithms that were used to create a model package.

', ], ], 'SourceAlgorithmSpecification' => [ 'base' => '

A list of algorithms that were used to create a model package.

', 'refs' => [ 'CreateModelPackageInput$SourceAlgorithmSpecification' => '

Details about the algorithm that was used to create the model package.

', 'DescribeModelPackageOutput$SourceAlgorithmSpecification' => '

Details about the algorithm that was used to create the model package.

', ], ], 'SplitType' => [ 'base' => NULL, 'refs' => [ 'TransformInput$SplitType' => '

The method to use to split the transform job\'s data files into smaller batches. Splitting is necessary when the total size of each object is too large to fit in a single request. You can also use data splitting to improve performance by processing multiple concurrent mini-batches. The default value for SplitType is None, which indicates that input data files are not split, and request payloads contain the entire contents of an input object. Set the value of this parameter to Line to split records on a newline character boundary. SplitType also supports a number of record-oriented binary data formats.

When splitting is enabled, the size of a mini-batch depends on the values of the BatchStrategy and MaxPayloadInMB parameters. When the value of BatchStrategy is MultiRecord, Amazon SageMaker sends the maximum number of records in each request, up to the MaxPayloadInMB limit. If the value of BatchStrategy is SingleRecord, Amazon SageMaker sends individual records in each request.

Some data formats represent a record as a binary payload wrapped with extra padding bytes. When splitting is applied to a binary data format, padding is removed if the value of BatchStrategy is set to SingleRecord. Padding is not removed if the value of BatchStrategy is set to MultiRecord.

For more information about the RecordIO, see Data Format in the MXNet documentation. For more information about the TFRecord, see Consuming TFRecord data in the TensorFlow documentation.

', ], ], 'StartNotebookInstanceInput' => [ 'base' => NULL, 'refs' => [], ], 'StatusMessage' => [ 'base' => NULL, 'refs' => [ 'SecondaryStatusTransition$StatusMessage' => '

A detailed description of the progress within a secondary status.

Amazon SageMaker provides secondary statuses and status messages that apply to each of them:

Starting

  • Starting the training job.

  • Launching requested ML instances.

  • Insufficient capacity error from EC2 while launching instances, retrying!

  • Launched instance was unhealthy, replacing it!

  • Preparing the instances for training.

Training

  • Downloading the training image.

  • Training image download completed. Training in progress.

Status messages are subject to change. Therefore, we recommend not including them in code that programmatically initiates actions. For examples, don\'t use status messages in if statements.

To have an overview of your training job\'s progress, view TrainingJobStatus and SecondaryStatus in DescribeTrainingJob, and StatusMessage together. For example, at the start of a training job, you might see the following:

  • TrainingJobStatus - InProgress

  • SecondaryStatus - Training

  • StatusMessage - Downloading the training image

', ], ], 'StopCompilationJobRequest' => [ 'base' => NULL, 'refs' => [], ], 'StopHyperParameterTuningJobRequest' => [ 'base' => NULL, 'refs' => [], ], 'StopLabelingJobRequest' => [ 'base' => NULL, 'refs' => [], ], 'StopNotebookInstanceInput' => [ 'base' => NULL, 'refs' => [], ], 'StopTrainingJobRequest' => [ 'base' => NULL, 'refs' => [], ], 'StopTransformJobRequest' => [ 'base' => NULL, 'refs' => [], ], 'StoppingCondition' => [ 'base' => '

Specifies a limit to how long a model training or compilation job can run. When the job reaches the time limit, Amazon SageMaker ends the training or compilation job. Use this API to cap model training costs.

To stop a job, Amazon SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds. Algorithms can use this 120-second window to save the model artifacts, so the results of training are not lost.

The training algorithms provided by Amazon SageMaker automatically save the intermediate results of a model training job when possible. This attempt to save artifacts is only a best effort case as model might not be in a state from which it can be saved. For example, if training has just started, the model might not be ready to save. When saved, this intermediate data is a valid model artifact. You can use it to create a model with CreateModel.

The Neural Topic Model (NTM) currently does not support saving intermediate model artifacts. When training NTMs, make sure that the maximum runtime is sufficient for the training job to complete.

', 'refs' => [ 'CreateCompilationJobRequest$StoppingCondition' => '

Specifies a limit to how long a model compilation job can run. When the job reaches the time limit, Amazon SageMaker ends the compilation job. Use this API to cap model training costs.

', 'CreateTrainingJobRequest$StoppingCondition' => '

Specifies a limit to how long a model training job can run. When the job reaches the time limit, Amazon SageMaker ends the training job. Use this API to cap model training costs.

To stop a job, Amazon SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds. Algorithms can use this 120-second window to save the model artifacts, so the results of training are not lost.

', 'DescribeCompilationJobResponse$StoppingCondition' => '

Specifies a limit to how long a model compilation job can run. When the job reaches the time limit, Amazon SageMaker ends the compilation job. Use this API to cap model training costs.

', 'DescribeTrainingJobResponse$StoppingCondition' => '

Specifies a limit to how long a model training job can run. When the job reaches the time limit, Amazon SageMaker ends the training job. Use this API to cap model training costs.

To stop a job, Amazon SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds. Algorithms can use this 120-second window to save the model artifacts, so the results of training are not lost.

', 'HyperParameterTrainingJobDefinition$StoppingCondition' => '

Specifies a limit to how long a model hyperparameter training job can run. When the job reaches the time limit, Amazon SageMaker ends the training job. Use this API to cap model training costs.

', 'TrainingJob$StoppingCondition' => '

Specifies a limit to how long a model training job can run. When the job reaches the time limit, Amazon SageMaker ends the training job. Use this API to cap model training costs.

To stop a job, Amazon SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds. Algorithms can use this 120-second window to save the model artifacts, so the results of training are not lost.

', 'TrainingJobDefinition$StoppingCondition' => '

Specifies a limit to how long a model training job can run. When the job reaches the time limit, Amazon SageMaker ends the training job. Use this API to cap model training costs.

To stop a job, Amazon SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds. Algorithms can use this 120-second window to save the model artifacts.

', ], ], 'String' => [ 'base' => NULL, 'refs' => [ 'AlgorithmStatusItem$FailureReason' => '

if the overall status is Failed, the reason for the failure.

', 'ModelPackageStatusItem$FailureReason' => '

if the overall status is Failed, the reason for the failure.

', 'ProductListings$member' => NULL, 'RenderUiTemplateResponse$RenderedContent' => '

A Liquid template that renders the HTML for the worker UI.

', 'RenderingError$Code' => '

A unique identifier for a specific class of errors.

', 'RenderingError$Message' => '

A human-readable message describing the error.

', 'SubscribedWorkteam$SellerName' => '

The name of the vendor in the Amazon Marketplace.

', 'SubscribedWorkteam$ListingId' => '

', 'Workteam$SubDomain' => '

The URI of the labeling job\'s user interface. Workers open this URI to start labeling your data objects.

', ], ], 'String200' => [ 'base' => NULL, 'refs' => [ 'CreateWorkteamRequest$Description' => '

A description of the work team.

', 'SubscribedWorkteam$MarketplaceTitle' => '

The title of the service provided by the vendor in the Amazon Marketplace.

', 'SubscribedWorkteam$MarketplaceDescription' => '

The description of the vendor from the Amazon Marketplace.

', 'UpdateWorkteamRequest$Description' => '

An updated description for the work team.

', 'Workteam$Description' => '

A description of the work team.

', ], ], 'SubnetId' => [ 'base' => NULL, 'refs' => [ 'CreateNotebookInstanceInput$SubnetId' => '

The ID of the subnet in a VPC to which you would like to have a connectivity from your ML compute instance.

', 'DescribeNotebookInstanceOutput$SubnetId' => '

The ID of the VPC subnet.

', 'Subnets$member' => NULL, ], ], 'Subnets' => [ 'base' => NULL, 'refs' => [ 'VpcConfig$Subnets' => '

The ID of the subnets in the VPC to which you want to connect your training job or model.

Amazon EC2 P3 accelerated computing instances are not available in the c/d/e availability zones of region us-east-1. If you want to create endpoints with P3 instances in VPC mode in region us-east-1, create subnets in a/b/f availability zones instead.

', ], ], 'SubscribedWorkteam' => [ 'base' => '

Describes a work team of a vendor that does the a labelling job.

', 'refs' => [ 'DescribeSubscribedWorkteamResponse$SubscribedWorkteam' => '

A Workteam instance that contains information about the work team.

', 'SubscribedWorkteams$member' => NULL, ], ], 'SubscribedWorkteams' => [ 'base' => NULL, 'refs' => [ 'ListSubscribedWorkteamsResponse$SubscribedWorkteams' => '

An array of Workteam objects, each describing a work team.

', ], ], 'Success' => [ 'base' => NULL, 'refs' => [ 'DeleteWorkteamResponse$Success' => '

Returns true if the work team was successfully deleted; otherwise, returns false.

', ], ], 'SuggestionQuery' => [ 'base' => '

Limits the property names that are included in the response.

', 'refs' => [ 'GetSearchSuggestionsRequest$SuggestionQuery' => '

Limits the property names that are included in the response.

', ], ], 'Tag' => [ 'base' => '

Describes a tag.

', 'refs' => [ 'TagList$member' => NULL, ], ], 'TagKey' => [ 'base' => NULL, 'refs' => [ 'Tag$Key' => '

The tag key.

', 'TagKeyList$member' => NULL, ], ], 'TagKeyList' => [ 'base' => NULL, 'refs' => [ 'DeleteTagsInput$TagKeys' => '

An array or one or more tag keys to delete.

', ], ], 'TagList' => [ 'base' => NULL, 'refs' => [ 'AddTagsInput$Tags' => '

An array of Tag objects. Each tag is a key-value pair. Only the key parameter is required. If you don\'t specify a value, Amazon SageMaker sets the value to an empty string.

', 'AddTagsOutput$Tags' => '

A list of tags associated with the Amazon SageMaker resource.

', 'CreateEndpointConfigInput$Tags' => '

A list of key-value pairs. For more information, see Using Cost Allocation Tags in the AWS Billing and Cost Management User Guide.

', 'CreateEndpointInput$Tags' => '

An array of key-value pairs. For more information, see Using Cost Allocation Tagsin the AWS Billing and Cost Management User Guide.

', 'CreateHyperParameterTuningJobRequest$Tags' => '

An array of key-value pairs. You can use tags to categorize your AWS resources in different ways, for example, by purpose, owner, or environment. For more information, see AWS Tagging Strategies.

Tags that you specify for the tuning job are also added to all training jobs that the tuning job launches.

', 'CreateLabelingJobRequest$Tags' => '

An array of key/value pairs. For more information, see Using Cost Allocation Tags in the AWS Billing and Cost Management User Guide.

', 'CreateModelInput$Tags' => '

An array of key-value pairs. For more information, see Using Cost Allocation Tags in the AWS Billing and Cost Management User Guide.

', 'CreateNotebookInstanceInput$Tags' => '

A list of tags to associate with the notebook instance. You can add tags later by using the CreateTags API.

', 'CreateTrainingJobRequest$Tags' => '

An array of key-value pairs. For more information, see Using Cost Allocation Tags in the AWS Billing and Cost Management User Guide.

', 'CreateTransformJobRequest$Tags' => '

(Optional) An array of key-value pairs. For more information, see Using Cost Allocation Tags in the AWS Billing and Cost Management User Guide.

', 'CreateWorkteamRequest$Tags' => '

', 'DescribeLabelingJobResponse$Tags' => '

An array of key/value pairs. For more information, see Using Cost Allocation Tags in the AWS Billing and Cost Management User Guide.

', 'ListTagsOutput$Tags' => '

An array of Tag objects, each with a tag key and a value.

', 'TrainingJob$Tags' => '

An array of key-value pairs. For more information, see Using Cost Allocation Tags in the AWS Billing and Cost Management User Guide.

', ], ], 'TagValue' => [ 'base' => NULL, 'refs' => [ 'Tag$Value' => '

The tag value.

', ], ], 'TargetDevice' => [ 'base' => NULL, 'refs' => [ 'CompilationJobSummary$CompilationTargetDevice' => '

The type of device that the model will run on after compilation has completed.

', 'OutputConfig$TargetDevice' => '

Identifies the device that you want to run your model on after it has been compiled. For example: ml_c5.

', ], ], 'TaskAvailabilityLifetimeInSeconds' => [ 'base' => NULL, 'refs' => [ 'HumanTaskConfig$TaskAvailabilityLifetimeInSeconds' => '

The length of time that a task remains available for labelling by human workers.

', ], ], 'TaskCount' => [ 'base' => NULL, 'refs' => [ 'DesiredWeightAndCapacity$DesiredInstanceCount' => '

The variant\'s capacity.

', 'ProductionVariant$InitialInstanceCount' => '

Number of instances to launch initially.

', 'ProductionVariantSummary$CurrentInstanceCount' => '

The number of instances associated with the variant.

', 'ProductionVariantSummary$DesiredInstanceCount' => '

The number of instances requested in the UpdateEndpointWeightsAndCapacities request.

', ], ], 'TaskDescription' => [ 'base' => NULL, 'refs' => [ 'HumanTaskConfig$TaskDescription' => '

A description of the task for your human workers.

', ], ], 'TaskInput' => [ 'base' => NULL, 'refs' => [ 'RenderableTask$Input' => '

A JSON object that contains values for the variables defined in the template. It is made available to the template under the substitution variable task.input. For example, if you define a variable task.input.text in your template, you can supply the variable in the JSON object as "text": "sample text".

', ], ], 'TaskKeyword' => [ 'base' => NULL, 'refs' => [ 'TaskKeywords$member' => NULL, ], ], 'TaskKeywords' => [ 'base' => NULL, 'refs' => [ 'HumanTaskConfig$TaskKeywords' => '

Keywords used to describe the task so that workers on Amazon Mechanical Turk can discover the task.

', ], ], 'TaskTimeLimitInSeconds' => [ 'base' => NULL, 'refs' => [ 'HumanTaskConfig$TaskTimeLimitInSeconds' => '

The amount of time that a worker has to complete a task.

', ], ], 'TaskTitle' => [ 'base' => NULL, 'refs' => [ 'HumanTaskConfig$TaskTitle' => '

A title for the task for your human workers.

', ], ], 'TemplateContent' => [ 'base' => NULL, 'refs' => [ 'UiTemplate$Content' => '

The content of the Liquid template for the worker user interface.

', ], ], 'TenthFractionsOfACent' => [ 'base' => NULL, 'refs' => [ 'USD$TenthFractionsOfACent' => '

Fractions of a cent, in tenths.

', ], ], 'Timestamp' => [ 'base' => NULL, 'refs' => [ 'CompilationJobSummary$CompilationStartTime' => '

The time when the model compilation job started.

', 'CompilationJobSummary$CompilationEndTime' => '

The time when the model compilation job completed.

', 'DeployedImage$ResolutionTime' => '

The date and time when the image path for the model resolved to the ResolvedImage

', 'DescribeCompilationJobResponse$CompilationStartTime' => '

The time when the model compilation job started the CompilationJob instances.

You are billed for the time between this timestamp and the timestamp in the DescribeCompilationJobResponse$CompilationEndTime field. In Amazon CloudWatch Logs, the start time might be later than this time. That\'s because it takes time to download the compilation job, which depends on the size of the compilation job container.

', 'DescribeCompilationJobResponse$CompilationEndTime' => '

The time when the model compilation job on a compilation job instance ended. For a successful or stopped job, this is when the job\'s model artifacts have finished uploading. For a failed job, this is when Amazon SageMaker detected that the job failed.

', 'DescribeEndpointConfigOutput$CreationTime' => '

A timestamp that shows when the endpoint configuration was created.

', 'DescribeEndpointOutput$CreationTime' => '

A timestamp that shows when the endpoint was created.

', 'DescribeEndpointOutput$LastModifiedTime' => '

A timestamp that shows when the endpoint was last modified.

', 'DescribeHyperParameterTuningJobResponse$CreationTime' => '

The date and time that the tuning job started.

', 'DescribeHyperParameterTuningJobResponse$HyperParameterTuningEndTime' => '

The date and time that the tuning job ended.

', 'DescribeHyperParameterTuningJobResponse$LastModifiedTime' => '

The date and time that the status of the tuning job was modified.

', 'DescribeLabelingJobResponse$CreationTime' => '

The date and time that the labeling job was created.

', 'DescribeLabelingJobResponse$LastModifiedTime' => '

The date and time that the labeling job was last updated.

', 'DescribeModelOutput$CreationTime' => '

A timestamp that shows when the model was created.

', 'DescribeTrainingJobResponse$CreationTime' => '

A timestamp that indicates when the training job was created.

', 'DescribeTrainingJobResponse$TrainingStartTime' => '

Indicates the time when the training job starts on training instances. You are billed for the time interval between this time and the value of TrainingEndTime. The start time in CloudWatch Logs might be later than this time. The difference is due to the time it takes to download the training data and to the size of the training container.

', 'DescribeTrainingJobResponse$TrainingEndTime' => '

Indicates the time when the training job ends on training instances. You are billed for the time interval between the value of TrainingStartTime and this time. For successful jobs and stopped jobs, this is the time after model artifacts are uploaded. For failed jobs, this is the time when Amazon SageMaker detects a job failure.

', 'DescribeTrainingJobResponse$LastModifiedTime' => '

A timestamp that indicates when the status of the training job was last modified.

', 'DescribeTransformJobResponse$CreationTime' => '

A timestamp that shows when the transform Job was created.

', 'DescribeTransformJobResponse$TransformStartTime' => '

Indicates when the transform job starts on ML instances. You are billed for the time interval between this time and the value of TransformEndTime.

', 'DescribeTransformJobResponse$TransformEndTime' => '

Indicates when the transform job has been completed, or has stopped or failed. You are billed for the time interval between this time and the value of TransformStartTime.

', 'EndpointConfigSummary$CreationTime' => '

A timestamp that shows when the endpoint configuration was created.

', 'EndpointSummary$CreationTime' => '

A timestamp that shows when the endpoint was created.

', 'EndpointSummary$LastModifiedTime' => '

A timestamp that shows when the endpoint was last modified.

', 'HyperParameterTrainingJobSummary$CreationTime' => '

The date and time that the training job was created.

', 'HyperParameterTrainingJobSummary$TrainingStartTime' => '

The date and time that the training job started.

', 'HyperParameterTrainingJobSummary$TrainingEndTime' => '

Specifies the time when the training job ends on training instances. You are billed for the time interval between the value of TrainingStartTime and this time. For successful jobs and stopped jobs, this is the time after model artifacts are uploaded. For failed jobs, this is the time when Amazon SageMaker detects a job failure.

', 'HyperParameterTuningJobSummary$CreationTime' => '

The date and time that the tuning job was created.

', 'HyperParameterTuningJobSummary$HyperParameterTuningEndTime' => '

The date and time that the tuning job ended.

', 'HyperParameterTuningJobSummary$LastModifiedTime' => '

The date and time that the tuning job was modified.

', 'LabelingJobForWorkteamSummary$CreationTime' => '

The date and time that the labeling job was created.

', 'LabelingJobSummary$CreationTime' => '

The date and time that the job was created (timestamp).

', 'LabelingJobSummary$LastModifiedTime' => '

The date and time that the job was last modified (timestamp).

', 'ListCodeRepositoriesInput$LastModifiedTimeAfter' => '

A filter that returns only Git repositories that were last modified after the specified time.

', 'ListCodeRepositoriesInput$LastModifiedTimeBefore' => '

A filter that returns only Git repositories that were last modified before the specified time.

', 'ListEndpointConfigsInput$CreationTimeBefore' => '

A filter that returns only endpoint configurations created before the specified time (timestamp).

', 'ListEndpointConfigsInput$CreationTimeAfter' => '

A filter that returns only endpoint configurations with a creation time greater than or equal to the specified time (timestamp).

', 'ListEndpointsInput$CreationTimeBefore' => '

A filter that returns only endpoints that were created before the specified time (timestamp).

', 'ListEndpointsInput$CreationTimeAfter' => '

A filter that returns only endpoints with a creation time greater than or equal to the specified time (timestamp).

', 'ListEndpointsInput$LastModifiedTimeBefore' => '

A filter that returns only endpoints that were modified before the specified timestamp.

', 'ListEndpointsInput$LastModifiedTimeAfter' => '

A filter that returns only endpoints that were modified after the specified timestamp.

', 'ListHyperParameterTuningJobsRequest$CreationTimeAfter' => '

A filter that returns only tuning jobs that were created after the specified time.

', 'ListHyperParameterTuningJobsRequest$CreationTimeBefore' => '

A filter that returns only tuning jobs that were created before the specified time.

', 'ListHyperParameterTuningJobsRequest$LastModifiedTimeAfter' => '

A filter that returns only tuning jobs that were modified after the specified time.

', 'ListHyperParameterTuningJobsRequest$LastModifiedTimeBefore' => '

A filter that returns only tuning jobs that were modified before the specified time.

', 'ListLabelingJobsForWorkteamRequest$CreationTimeAfter' => '

A filter that returns only labeling jobs created after the specified time (timestamp).

', 'ListLabelingJobsForWorkteamRequest$CreationTimeBefore' => '

A filter that returns only labeling jobs created before the specified time (timestamp).

', 'ListLabelingJobsRequest$CreationTimeAfter' => '

A filter that returns only labeling jobs created after the specified time (timestamp).

', 'ListLabelingJobsRequest$CreationTimeBefore' => '

A filter that returns only labeling jobs created before the specified time (timestamp).

', 'ListLabelingJobsRequest$LastModifiedTimeAfter' => '

A filter that returns only labeling jobs modified after the specified time (timestamp).

', 'ListLabelingJobsRequest$LastModifiedTimeBefore' => '

A filter that returns only labeling jobs modified before the specified time (timestamp).

', 'ListModelsInput$CreationTimeBefore' => '

A filter that returns only models created before the specified time (timestamp).

', 'ListModelsInput$CreationTimeAfter' => '

A filter that returns only models with a creation time greater than or equal to the specified time (timestamp).

', 'ListTrainingJobsRequest$CreationTimeAfter' => '

A filter that returns only training jobs created after the specified time (timestamp).

', 'ListTrainingJobsRequest$CreationTimeBefore' => '

A filter that returns only training jobs created before the specified time (timestamp).

', 'ListTrainingJobsRequest$LastModifiedTimeAfter' => '

A filter that returns only training jobs modified after the specified time (timestamp).

', 'ListTrainingJobsRequest$LastModifiedTimeBefore' => '

A filter that returns only training jobs modified before the specified time (timestamp).

', 'ListTransformJobsRequest$CreationTimeAfter' => '

A filter that returns only transform jobs created after the specified time.

', 'ListTransformJobsRequest$CreationTimeBefore' => '

A filter that returns only transform jobs created before the specified time.

', 'ListTransformJobsRequest$LastModifiedTimeAfter' => '

A filter that returns only transform jobs modified after the specified time.

', 'ListTransformJobsRequest$LastModifiedTimeBefore' => '

A filter that returns only transform jobs modified before the specified time.

', 'MetricData$Timestamp' => '

The date and time that the algorithm emitted the metric.

', 'ModelSummary$CreationTime' => '

A timestamp that indicates when the model was created.

', 'SecondaryStatusTransition$StartTime' => '

A timestamp that shows when the training job transitioned to the current secondary status state.

', 'SecondaryStatusTransition$EndTime' => '

A timestamp that shows when the training job transitioned out of this secondary status state into another secondary status state or when the training job has ended.

', 'TrainingJob$CreationTime' => '

A timestamp that indicates when the training job was created.

', 'TrainingJob$TrainingStartTime' => '

Indicates the time when the training job starts on training instances. You are billed for the time interval between this time and the value of TrainingEndTime. The start time in CloudWatch Logs might be later than this time. The difference is due to the time it takes to download the training data and to the size of the training container.

', 'TrainingJob$TrainingEndTime' => '

Indicates the time when the training job ends on training instances. You are billed for the time interval between the value of TrainingStartTime and this time. For successful jobs and stopped jobs, this is the time after model artifacts are uploaded. For failed jobs, this is the time when Amazon SageMaker detects a job failure.

', 'TrainingJob$LastModifiedTime' => '

A timestamp that indicates when the status of the training job was last modified.

', 'TrainingJobSummary$CreationTime' => '

A timestamp that shows when the training job was created.

', 'TrainingJobSummary$TrainingEndTime' => '

A timestamp that shows when the training job ended. This field is set only if the training job has one of the terminal statuses (Completed, Failed, or Stopped).

', 'TrainingJobSummary$LastModifiedTime' => '

Timestamp when the training job was last modified.

', 'TransformJobSummary$CreationTime' => '

A timestamp that shows when the transform Job was created.

', 'TransformJobSummary$TransformEndTime' => '

Indicates when the transform job ends on compute instances. For successful jobs and stopped jobs, this is the exact time recorded after the results are uploaded. For failed jobs, this is when Amazon SageMaker detected that the job failed.

', 'TransformJobSummary$LastModifiedTime' => '

Indicates when the transform job was last modified.

', 'Workteam$CreateDate' => '

The date and time that the work team was created (timestamp).

', 'Workteam$LastUpdatedDate' => '

The date and time that the work team was last updated (timestamp).

', ], ], 'TrainingInputMode' => [ 'base' => NULL, 'refs' => [ 'AlgorithmSpecification$TrainingInputMode' => '

The input mode that the algorithm supports. For the input modes that Amazon SageMaker algorithms support, see Algorithms. If an algorithm supports the File input mode, Amazon SageMaker downloads the training data from S3 to the provisioned ML storage Volume, and mounts the directory to docker volume for training container. If an algorithm supports the Pipe input mode, Amazon SageMaker streams data directly from S3 to the container.

In File mode, make sure you provision ML storage volume with sufficient capacity to accommodate the data download from S3. In addition to the training data, the ML storage volume also stores the output model. The algorithm container use ML storage volume to also store intermediate information, if any.

For distributed algorithms using File mode, training data is distributed uniformly, and your training duration is predictable if the input data objects size is approximately same. Amazon SageMaker does not split the files any further for model training. If the object sizes are skewed, training won\'t be optimal as the data distribution is also skewed where one host in a training cluster is overloaded, thus becoming bottleneck in training.

', 'Channel$InputMode' => '

(Optional) The input mode to use for the data channel in a training job. If you don\'t set a value for InputMode, Amazon SageMaker uses the value set for TrainingInputMode. Use this parameter to override the TrainingInputMode setting in a AlgorithmSpecification request when you have a channel that needs a different input mode from the training job\'s general setting. To download the data from Amazon Simple Storage Service (Amazon S3) to the provisioned ML storage volume, and mount the directory to a Docker volume, use File input mode. To stream data directly from Amazon S3 to the container, choose Pipe input mode.

To use a model for incremental training, choose File input model.

', 'HyperParameterAlgorithmSpecification$TrainingInputMode' => '

The input mode that the algorithm supports: File or Pipe. In File input mode, Amazon SageMaker downloads the training data from Amazon S3 to the storage volume that is attached to the training instance and mounts the directory to the Docker volume for the training container. In Pipe input mode, Amazon SageMaker streams data directly from Amazon S3 to the container.

If you specify File mode, make sure that you provision the storage volume that is attached to the training instance with enough capacity to accommodate the training data downloaded from Amazon S3, the model artifacts, and intermediate information.

For more information about input modes, see Algorithms.

', 'InputModes$member' => NULL, 'TrainingJobDefinition$TrainingInputMode' => '

The input mode used by the algorithm for the training job. For the input modes that Amazon SageMaker algorithms support, see Algorithms.

If an algorithm supports the File input mode, Amazon SageMaker downloads the training data from S3 to the provisioned ML storage Volume, and mounts the directory to docker volume for training container. If an algorithm supports the Pipe input mode, Amazon SageMaker streams data directly from S3 to the container.

', ], ], 'TrainingInstanceCount' => [ 'base' => NULL, 'refs' => [ 'ResourceConfig$InstanceCount' => '

The number of ML compute instances to use. For distributed training, provide a value greater than 1.

', ], ], 'TrainingInstanceType' => [ 'base' => NULL, 'refs' => [ 'ResourceConfig$InstanceType' => '

The ML compute instance type.

', 'TrainingInstanceTypes$member' => NULL, ], ], 'TrainingInstanceTypes' => [ 'base' => NULL, 'refs' => [ 'TrainingSpecification$SupportedTrainingInstanceTypes' => '

A list of the instance types that this algorithm can use for training.

', ], ], 'TrainingJob' => [ 'base' => '

Contains information about a training job.

', 'refs' => [ 'SearchRecord$TrainingJob' => '

A TrainingJob object that is returned as part of a Search request.

', ], ], 'TrainingJobArn' => [ 'base' => NULL, 'refs' => [ 'CreateTrainingJobResponse$TrainingJobArn' => '

The Amazon Resource Name (ARN) of the training job.

', 'DescribeTrainingJobResponse$TrainingJobArn' => '

The Amazon Resource Name (ARN) of the training job.

', 'HyperParameterTrainingJobSummary$TrainingJobArn' => '

The Amazon Resource Name (ARN) of the training job.

', 'TrainingJob$TrainingJobArn' => '

The Amazon Resource Name (ARN) of the training job.

', 'TrainingJobSummary$TrainingJobArn' => '

The Amazon Resource Name (ARN) of the training job.

', ], ], 'TrainingJobDefinition' => [ 'base' => '

Defines the input needed to run a training job using the algorithm.

', 'refs' => [ 'AlgorithmValidationProfile$TrainingJobDefinition' => '

The TrainingJobDefinition object that describes the training job that Amazon SageMaker runs to validate your algorithm.

', ], ], 'TrainingJobEarlyStoppingType' => [ 'base' => NULL, 'refs' => [ 'HyperParameterTuningJobConfig$TrainingJobEarlyStoppingType' => '

Specifies whether to use early stopping for training jobs launched by the hyperparameter tuning job. This can be one of the following values (the default value is OFF):

OFF

Training jobs launched by the hyperparameter tuning job do not use early stopping.

AUTO

Amazon SageMaker stops training jobs launched by the hyperparameter tuning job when they are unlikely to perform better than previously completed training jobs. For more information, see Stop Training Jobs Early.

', ], ], 'TrainingJobName' => [ 'base' => NULL, 'refs' => [ 'CreateTrainingJobRequest$TrainingJobName' => '

The name of the training job. The name must be unique within an AWS Region in an AWS account.

', 'DescribeTrainingJobRequest$TrainingJobName' => '

The name of the training job.

', 'DescribeTrainingJobResponse$TrainingJobName' => '

Name of the model training job.

', 'HyperParameterTrainingJobSummary$TrainingJobName' => '

The name of the training job.

', 'StopTrainingJobRequest$TrainingJobName' => '

The name of the training job to stop.

', 'TrainingJob$TrainingJobName' => '

The name of the training job.

', 'TrainingJobSummary$TrainingJobName' => '

The name of the training job that you want a summary for.

', ], ], 'TrainingJobSortByOptions' => [ 'base' => NULL, 'refs' => [ 'ListTrainingJobsForHyperParameterTuningJobRequest$SortBy' => '

The field to sort results by. The default is Name.

If the value of this field is FinalObjectiveMetricValue, any training jobs that did not return an objective metric are not listed.

', ], ], 'TrainingJobStatus' => [ 'base' => NULL, 'refs' => [ 'DescribeTrainingJobResponse$TrainingJobStatus' => '

The status of the training job.

Amazon SageMaker provides the following training job statuses:

  • InProgress - The training is in progress.

  • Completed - The training job has completed.

  • Failed - The training job has failed. To see the reason for the failure, see the FailureReason field in the response to a DescribeTrainingJobResponse call.

  • Stopping - The training job is stopping.

  • Stopped - The training job has stopped.

For more detailed information, see SecondaryStatus.

', 'HyperParameterTrainingJobSummary$TrainingJobStatus' => '

The status of the training job.

', 'ListTrainingJobsForHyperParameterTuningJobRequest$StatusEquals' => '

A filter that returns only training jobs with the specified status.

', 'ListTrainingJobsRequest$StatusEquals' => '

A filter that retrieves only training jobs with a specific status.

', 'TrainingJob$TrainingJobStatus' => '

The status of the training job.

Training job statuses are:

  • InProgress - The training is in progress.

  • Completed - The training job has completed.

  • Failed - The training job has failed. To see the reason for the failure, see the FailureReason field in the response to a DescribeTrainingJobResponse call.

  • Stopping - The training job is stopping.

  • Stopped - The training job has stopped.

For more detailed information, see SecondaryStatus.

', 'TrainingJobSummary$TrainingJobStatus' => '

The status of the training job.

', ], ], 'TrainingJobStatusCounter' => [ 'base' => NULL, 'refs' => [ 'TrainingJobStatusCounters$Completed' => '

The number of completed training jobs launched by the hyperparameter tuning job.

', 'TrainingJobStatusCounters$InProgress' => '

The number of in-progress training jobs launched by a hyperparameter tuning job.

', 'TrainingJobStatusCounters$RetryableError' => '

The number of training jobs that failed, but can be retried. A failed training job can be retried only if it failed because an internal service error occurred.

', 'TrainingJobStatusCounters$NonRetryableError' => '

The number of training jobs that failed and can\'t be retried. A failed training job can\'t be retried if it failed because a client error occurred.

', 'TrainingJobStatusCounters$Stopped' => '

The number of training jobs launched by a hyperparameter tuning job that were manually stopped.

', ], ], 'TrainingJobStatusCounters' => [ 'base' => '

The numbers of training jobs launched by a hyperparameter tuning job, categorized by status.

', 'refs' => [ 'DescribeHyperParameterTuningJobResponse$TrainingJobStatusCounters' => '

The TrainingJobStatusCounters object that specifies the number of training jobs, categorized by status, that this tuning job launched.

', 'HyperParameterTuningJobSummary$TrainingJobStatusCounters' => '

The TrainingJobStatusCounters object that specifies the numbers of training jobs, categorized by status, that this tuning job launched.

', ], ], 'TrainingJobSummaries' => [ 'base' => NULL, 'refs' => [ 'ListTrainingJobsResponse$TrainingJobSummaries' => '

An array of TrainingJobSummary objects, each listing a training job.

', ], ], 'TrainingJobSummary' => [ 'base' => '

Provides summary information about a training job.

', 'refs' => [ 'TrainingJobSummaries$member' => NULL, ], ], 'TrainingSpecification' => [ 'base' => '

Defines how the algorithm is used for a training job.

', 'refs' => [ 'CreateAlgorithmInput$TrainingSpecification' => '

Specifies details about training jobs run by this algorithm, including the following:

  • The Amazon ECR path of the container and the version digest of the algorithm.

  • The hyperparameters that the algorithm supports.

  • The instance types that the algorithm supports for training.

  • Whether the algorithm supports distributed training.

  • The metrics that the algorithm emits to Amazon CloudWatch.

  • Which metrics that the algorithm emits can be used as the objective metric for hyperparameter tuning jobs.

  • The input channels that the algorithm supports for training data. For example, an algorithm might support train, validation, and test channels.

', 'DescribeAlgorithmOutput$TrainingSpecification' => '

Details about training jobs run by this algorithm.

', ], ], 'TransformDataSource' => [ 'base' => '

Describes the location of the channel data.

', 'refs' => [ 'TransformInput$DataSource' => '

Describes the location of the channel data, which is, the S3 location of the input data that the model can consume.

', ], ], 'TransformEnvironmentKey' => [ 'base' => NULL, 'refs' => [ 'TransformEnvironmentMap$key' => NULL, ], ], 'TransformEnvironmentMap' => [ 'base' => NULL, 'refs' => [ 'CreateTransformJobRequest$Environment' => '

The environment variables to set in the Docker container. We support up to 16 key and values entries in the map.

', 'DescribeTransformJobResponse$Environment' => '

The environment variables to set in the Docker container. We support up to 16 key and values entries in the map.

', 'TransformJobDefinition$Environment' => '

The environment variables to set in the Docker container. We support up to 16 key and values entries in the map.

', ], ], 'TransformEnvironmentValue' => [ 'base' => NULL, 'refs' => [ 'TransformEnvironmentMap$value' => NULL, ], ], 'TransformInput' => [ 'base' => '

Describes the input source of a transform job and the way the transform job consumes it.

', 'refs' => [ 'CreateTransformJobRequest$TransformInput' => '

Describes the input source and the way the transform job consumes it.

', 'DescribeTransformJobResponse$TransformInput' => '

Describes the dataset to be transformed and the Amazon S3 location where it is stored.

', 'TransformJobDefinition$TransformInput' => '

A description of the input source and the way the transform job consumes it.

', ], ], 'TransformInstanceCount' => [ 'base' => NULL, 'refs' => [ 'TransformResources$InstanceCount' => '

The number of ML compute instances to use in the transform job. For distributed transform jobs, specify a value greater than 1. The default value is 1.

', ], ], 'TransformInstanceType' => [ 'base' => NULL, 'refs' => [ 'TransformInstanceTypes$member' => NULL, 'TransformResources$InstanceType' => '

The ML compute instance type for the transform job. If you are using built-in algorithms to transform moderately sized datasets, we recommend using ml.m4.xlarge or ml.m5.largeinstance types.

', ], ], 'TransformInstanceTypes' => [ 'base' => NULL, 'refs' => [ 'InferenceSpecification$SupportedTransformInstanceTypes' => '

A list of the instance types on which a transformation job can be run or on which an endpoint can be deployed.

', ], ], 'TransformJobArn' => [ 'base' => NULL, 'refs' => [ 'CreateTransformJobResponse$TransformJobArn' => '

The Amazon Resource Name (ARN) of the transform job.

', 'DescribeTransformJobResponse$TransformJobArn' => '

The Amazon Resource Name (ARN) of the transform job.

', 'TransformJobSummary$TransformJobArn' => '

The Amazon Resource Name (ARN) of the transform job.

', ], ], 'TransformJobDefinition' => [ 'base' => '

Defines the input needed to run a transform job using the inference specification specified in the algorithm.

', 'refs' => [ 'AlgorithmValidationProfile$TransformJobDefinition' => '

The TransformJobDefinition object that describes the transform job that Amazon SageMaker runs to validate your algorithm.

', 'ModelPackageValidationProfile$TransformJobDefinition' => '

The TransformJobDefinition object that describes the transform job used for the validation of the model package.

', ], ], 'TransformJobName' => [ 'base' => NULL, 'refs' => [ 'CreateTransformJobRequest$TransformJobName' => '

The name of the transform job. The name must be unique within an AWS Region in an AWS account.

', 'DescribeTransformJobRequest$TransformJobName' => '

The name of the transform job that you want to view details of.

', 'DescribeTransformJobResponse$TransformJobName' => '

The name of the transform job.

', 'StopTransformJobRequest$TransformJobName' => '

The name of the transform job to stop.

', 'TransformJobSummary$TransformJobName' => '

The name of the transform job.

', ], ], 'TransformJobStatus' => [ 'base' => NULL, 'refs' => [ 'DescribeTransformJobResponse$TransformJobStatus' => '

The status of the transform job. If the transform job failed, the reason is returned in the FailureReason field.

', 'ListTransformJobsRequest$StatusEquals' => '

A filter that retrieves only transform jobs with a specific status.

', 'TransformJobSummary$TransformJobStatus' => '

The status of the transform job.

', ], ], 'TransformJobSummaries' => [ 'base' => NULL, 'refs' => [ 'ListTransformJobsResponse$TransformJobSummaries' => '

An array of TransformJobSummary objects.

', ], ], 'TransformJobSummary' => [ 'base' => '

Provides a summary of a transform job. Multiple TransformJobSummary objects are returned as a list after in response to a ListTransformJobs call.

', 'refs' => [ 'TransformJobSummaries$member' => NULL, ], ], 'TransformOutput' => [ 'base' => '

Describes the results of a transform job.

', 'refs' => [ 'CreateTransformJobRequest$TransformOutput' => '

Describes the results of the transform job.

', 'DescribeTransformJobResponse$TransformOutput' => '

Identifies the Amazon S3 location where you want Amazon SageMaker to save the results from the transform job.

', 'TransformJobDefinition$TransformOutput' => '

Identifies the Amazon S3 location where you want Amazon SageMaker to save the results from the transform job.

', ], ], 'TransformResources' => [ 'base' => '

Describes the resources, including ML instance types and ML instance count, to use for transform job.

', 'refs' => [ 'CreateTransformJobRequest$TransformResources' => '

Describes the resources, including ML instance types and ML instance count, to use for the transform job.

', 'DescribeTransformJobResponse$TransformResources' => '

Describes the resources, including ML instance types and ML instance count, to use for the transform job.

', 'TransformJobDefinition$TransformResources' => '

Identifies the ML compute instances for the transform job.

', ], ], 'TransformS3DataSource' => [ 'base' => '

Describes the S3 data source.

', 'refs' => [ 'TransformDataSource$S3DataSource' => '

The S3 location of the data source that is associated with a channel.

', ], ], 'USD' => [ 'base' => '

Represents an amount of money in United States dollars/

', 'refs' => [ 'PublicWorkforceTaskPrice$AmountInUsd' => '

Defines the amount of money paid to a worker in United States dollars.

', ], ], 'UiConfig' => [ 'base' => '

Provided configuration information for the worker UI for a labeling job.

', 'refs' => [ 'HumanTaskConfig$UiConfig' => '

Information about the user interface that workers use to complete the labeling task.

', ], ], 'UiTemplate' => [ 'base' => '

The Liquid template for the worker user interface.

', 'refs' => [ 'RenderUiTemplateRequest$UiTemplate' => '

A Template object containing the worker UI template to render.

', ], ], 'UpdateCodeRepositoryInput' => [ 'base' => NULL, 'refs' => [], ], 'UpdateCodeRepositoryOutput' => [ 'base' => NULL, 'refs' => [], ], 'UpdateEndpointInput' => [ 'base' => NULL, 'refs' => [], ], 'UpdateEndpointOutput' => [ 'base' => NULL, 'refs' => [], ], 'UpdateEndpointWeightsAndCapacitiesInput' => [ 'base' => NULL, 'refs' => [], ], 'UpdateEndpointWeightsAndCapacitiesOutput' => [ 'base' => NULL, 'refs' => [], ], 'UpdateNotebookInstanceInput' => [ 'base' => NULL, 'refs' => [], ], 'UpdateNotebookInstanceLifecycleConfigInput' => [ 'base' => NULL, 'refs' => [], ], 'UpdateNotebookInstanceLifecycleConfigOutput' => [ 'base' => NULL, 'refs' => [], ], 'UpdateNotebookInstanceOutput' => [ 'base' => NULL, 'refs' => [], ], 'UpdateWorkteamRequest' => [ 'base' => NULL, 'refs' => [], ], 'UpdateWorkteamResponse' => [ 'base' => NULL, 'refs' => [], ], 'Url' => [ 'base' => NULL, 'refs' => [ 'ContainerDefinition$ModelDataUrl' => '

The S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix). The S3 path is required for Amazon SageMaker built-in algorithms, but not if you use your own algorithms. For more information on built-in algorithms, see Common Parameters.

If you provide a value for this parameter, Amazon SageMaker uses AWS Security Token Service to download model artifacts from the S3 path you provide. AWS STS is activated in your IAM user account by default. If you previously deactivated AWS STS for a region, you need to reactivate AWS STS for that region. For more information, see Activating and Deactivating AWS STS in an AWS Region in the AWS Identity and Access Management User Guide.

If you use a built-in algorithm to create a model, Amazon SageMaker requires that you provide a S3 path to the model artifacts in ModelDataUrl.

', 'ModelPackageContainerDefinition$ModelDataUrl' => '

The Amazon S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).

', 'SourceAlgorithm$ModelDataUrl' => '

The Amazon S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).

', ], ], 'VariantName' => [ 'base' => NULL, 'refs' => [ 'DesiredWeightAndCapacity$VariantName' => '

The name of the variant to update.

', 'ProductionVariant$VariantName' => '

The name of the production variant.

', 'ProductionVariantSummary$VariantName' => '

The name of the variant.

', ], ], 'VariantWeight' => [ 'base' => NULL, 'refs' => [ 'DesiredWeightAndCapacity$DesiredWeight' => '

The variant\'s weight.

', 'ProductionVariant$InitialVariantWeight' => '

Determines initial traffic distribution among all of the models that you specify in the endpoint configuration. The traffic to a production variant is determined by the ratio of the VariantWeight to the sum of all VariantWeight values across all ProductionVariants. If unspecified, it defaults to 1.0.

', 'ProductionVariantSummary$CurrentWeight' => '

The weight associated with the variant.

', 'ProductionVariantSummary$DesiredWeight' => '

The requested weight, as specified in the UpdateEndpointWeightsAndCapacities request.

', ], ], 'VolumeSizeInGB' => [ 'base' => NULL, 'refs' => [ 'ResourceConfig$VolumeSizeInGB' => '

The size of the ML storage volume that you want to provision.

ML storage volumes store model artifacts and incremental states. Training algorithms might also use the ML storage volume for scratch space. If you want to store the training data in the ML storage volume, choose File as the TrainingInputMode in the algorithm specification.

You must specify sufficient ML storage for your scenario.

Amazon SageMaker supports only the General Purpose SSD (gp2) ML storage volume type.

', ], ], 'VpcConfig' => [ 'base' => '

Specifies a VPC that your training jobs and hosted models have access to. Control access to and from your training and model containers by configuring the VPC. For more information, see Protect Endpoints by Using an Amazon Virtual Private Cloud and Protect Training Jobs by Using an Amazon Virtual Private Cloud.

', 'refs' => [ 'CreateModelInput$VpcConfig' => '

A VpcConfig object that specifies the VPC that you want your model to connect to. Control access to and from your model container by configuring the VPC. VpcConfig is used in hosting services and in batch transform. For more information, see Protect Endpoints by Using an Amazon Virtual Private Cloud and Protect Data in Batch Transform Jobs by Using an Amazon Virtual Private Cloud.

', 'CreateTrainingJobRequest$VpcConfig' => '

A VpcConfig object that specifies the VPC that you want your training job to connect to. Control access to and from your training container by configuring the VPC. For more information, see Protect Training Jobs by Using an Amazon Virtual Private Cloud.

', 'DescribeModelOutput$VpcConfig' => '

A VpcConfig object that specifies the VPC that this model has access to. For more information, see Protect Endpoints by Using an Amazon Virtual Private Cloud

', 'DescribeTrainingJobResponse$VpcConfig' => '

A VpcConfig object that specifies the VPC that this training job has access to. For more information, see Protect Training Jobs by Using an Amazon Virtual Private Cloud.

', 'HyperParameterTrainingJobDefinition$VpcConfig' => '

The VpcConfig object that specifies the VPC that you want the training jobs that this hyperparameter tuning job launches to connect to. Control access to and from your training container by configuring the VPC. For more information, see Protect Training Jobs by Using an Amazon Virtual Private Cloud.

', 'TrainingJob$VpcConfig' => '

A VpcConfig object that specifies the VPC that this training job has access to. For more information, see Protect Training Jobs by Using an Amazon Virtual Private Cloud.

', ], ], 'VpcSecurityGroupIds' => [ 'base' => NULL, 'refs' => [ 'VpcConfig$SecurityGroupIds' => '

The VPC security group IDs, in the form sg-xxxxxxxx. Specify the security groups for the VPC that is specified in the Subnets field.

', ], ], 'Workteam' => [ 'base' => '

Provides details about a labeling work team.

', 'refs' => [ 'DescribeWorkteamResponse$Workteam' => '

A Workteam instance that contains information about the work team.

', 'UpdateWorkteamResponse$Workteam' => '

A Workteam object that describes the updated work team.

', 'Workteams$member' => NULL, ], ], 'WorkteamArn' => [ 'base' => NULL, 'refs' => [ 'CreateWorkteamResponse$WorkteamArn' => '

The Amazon Resource Name (ARN) of the work team. You can use this ARN to identify the work team.

', 'DescribeSubscribedWorkteamRequest$WorkteamArn' => '

The Amazon Resource Name (ARN) of the subscribed work team to describe.

', 'HumanTaskConfig$WorkteamArn' => '

The Amazon Resource Name (ARN) of the work team assigned to complete the tasks.

', 'LabelingJobSummary$WorkteamArn' => '

The Amazon Resource Name (ARN) of the work team assigned to the job.

', 'ListLabelingJobsForWorkteamRequest$WorkteamArn' => '

The Amazon Resource Name (ARN) of the work team for which you want to see labeling jobs for.

', 'SubscribedWorkteam$WorkteamArn' => '

The Amazon Resource Name (ARN) of the vendor that you have subscribed.

', 'Workteam$WorkteamArn' => '

The Amazon Resource Name (ARN) that identifies the work team.

', ], ], 'WorkteamName' => [ 'base' => NULL, 'refs' => [ 'CreateWorkteamRequest$WorkteamName' => '

The name of the work team. Use this name to identify the work team.

', 'DeleteWorkteamRequest$WorkteamName' => '

The name of the work team to delete.

', 'DescribeWorkteamRequest$WorkteamName' => '

The name of the work team to return a description of.

', 'ListSubscribedWorkteamsRequest$NameContains' => '

A string in the work team name. This filter returns only work teams whose name contains the specified string.

', 'ListWorkteamsRequest$NameContains' => '

A string in the work team\'s name. This filter returns only work teams whose name contains the specified string.

', 'UpdateWorkteamRequest$WorkteamName' => '

The name of the work team to update.

', 'Workteam$WorkteamName' => '

The name of the work team.

', ], ], 'Workteams' => [ 'base' => NULL, 'refs' => [ 'ListWorkteamsResponse$Workteams' => '

An array of Workteam objects, each describing a work team.

', ], ], ],];