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.project-metadata.yaml
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.project-metadata.yaml
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name: Deep Learning for Anomaly Detection
description: Applying deep learning models on the task of anomaly detection
author: Cloudera Inc.
specification_version: 1.0
prototype_version: 2.0
date: "2022-03-29"
runtimes:
- editor: Workbench
kernel: Python 3.9
edition: Standard
tasks:
- type: create_job
name: Install dependencies
entity_label: install_deps
script: cml/install_deps.py
arguments: None
cpu: 1
memory: 4
short_summary: Create job to install project dependencies.
environment:
TASK_TYPE: CREATE/RUN_JOB
- type: run_job
entity_label: install_deps
short_summary: Running install dependencies job.
long_summary: >-
Running the job to install dependencies. Note that this requires at least 2GB of memory
- type: create_job
name: Train Model
entity_label: train_model
script: train.py
arguments: None
short_summary: Job to train and export model.
long_summary: Job to train and export model. Note that this requires at least 2GB of memory.
cpu: 1
memory: 3
environment:
TASK_TYPE: CREATE/RUN_JOB
- type: run_job
entity_label: train_model
short_summary: Running model training job.
long_summary: >-
Running the job to train a model.
- type: start_application
name: Application to serve Deep Learning for Anomaly Detectionn UI
short_summary: Create an application to serve the Anomaly Detection UI.
long_summary: Create an application to serve the Anomaly Detection UI.
subdomain: deepad
script: app/backend/app.py
environment_variables:
TASK_TYPE: START_APPLICATION