InstallCustomizedAlgos.rst

How to register customized algorithms as builtin tuners, assessors and advisors

Contents

• Overview
• Register customized algorithms as builtin tuners, assessors and advisors
  • 1. Create a customized tuner/assessor/advisor
  • 2. (Optional) Create a validator to validate classArgs
  • 3. Install your customized algorithms into python environment
  • 4. Prepare meta file
  • 5. Register customized algorithms into NNI
• Use the installed builtin algorithms in experiment
• Manage builtin algorithms using nnictl algo
  • List builtin algorithms
  • Unregister builtin algorithms
• Porting customized algorithms from v1.x to v2.x
• Example: Register a customized tuner as a builtin tuner
  • Install the customized tuner package into python environment
    • Option 1: install from directory
    • Option 2: install from whl file
  • Register the customized tuner as builtin tuner:
  • Check the registered builtin algorithms

Overview

NNI provides a lot of builtin tuners, advisors and assessors can be used directly for Hyper Parameter Optimization, and some extra algorithms can be registered via nnictl algo register --meta <path_to_meta_file> after NNI is installed. You can check builtin algorithms via nnictl algo list command.

NNI also provides the ability to build your own customized tuners, advisors and assessors. To use the customized algorithm, users can simply follow the spec in experiment config file to properly reference the algorithm, which has been illustrated in the tutorials of customized tuners / advisors / assessors.

NNI also allows users to install the customized algorithm as a builtin algorithm, in order for users to use the algorithm in the same way as NNI builtin tuners/advisors/assessors. More importantly, it becomes much easier for users to share or distribute their implemented algorithm to others. Customized tuners/advisors/assessors can be installed into NNI as builtin algorithms, once they are installed into NNI, you can use your customized algorithms the same way as builtin tuners/advisors/assessors in your experiment configuration file. For example, you built a customized tuner and installed it into NNI using a builtin name mytuner, then you can use this tuner in your configuration file like below:

tuner:
  builtinTunerName: mytuner

Register customized algorithms as builtin tuners, assessors and advisors

You can follow below steps to build a customized tuner/assessor/advisor, and register it into NNI as builtin algorithm.

1. Create a customized tuner/assessor/advisor

Reference following instructions to create:

• customized tuner
• customized assessor
• customized advisor

2. (Optional) Create a validator to validate classArgs

NNI provides a ClassArgsValidator interface for customized algorithms author to validate the classArgs parameters in experiment configuration file which are passed to customized algorithms constructors. The ClassArgsValidator interface is defined as:

class ClassArgsValidator(object):
    def validate_class_args(self, **kwargs):
        """
        The classArgs fields in experiment configuration are packed as a dict and
        passed to validator as kwargs.
        """
        pass

For example, you can implement your validator such as:

from schema import Schema, Optional
from nni import ClassArgsValidator

class MedianstopClassArgsValidator(ClassArgsValidator):
    def validate_class_args(self, **kwargs):
        Schema({
            Optional('optimize_mode'): self.choices('optimize_mode', 'maximize', 'minimize'),
            Optional('start_step'): self.range('start_step', int, 0, 9999),
        }).validate(kwargs)

The validator will be invoked before experiment is started to check whether the classArgs fields are valid for your customized algorithms.

3. Install your customized algorithms into python environment

Firstly, the customized algorithms need to be prepared as a python package. Then you can install the package into python environment via:

• Run command python setup.py develop from the package directory, this command will install the package in development mode, this is recommended if your algorithm is under development.
• Run command python setup.py bdist_wheel from the package directory, this command build a whl file which is a pip installation source. Then run pip install <wheel file> to install it.

4. Prepare meta file

Create a yaml file with following keys as meta file:

• algoType: type of algorithms, could be one of tuner, assessor, advisor
• builtinName: builtin name used in experiment configuration file
• className: tuner class name, including its module name, for example: demo_tuner.DemoTuner
• classArgsValidator: class args validator class name, including its module name, for example: demo_tuner.MyClassArgsValidator

Following is an example of the yaml file:

algoType: tuner
builtinName: demotuner
className: demo_tuner.DemoTuner
classArgsValidator: demo_tuner.MyClassArgsValidator

5. Register customized algorithms into NNI

Run following command to register the customized algorithms as builtin algorithms in NNI:

nnictl algo register --meta <path_to_meta_file>

The <path_to_meta_file> is the path to the yaml file your created in above section.

Reference customized tuner example for a full example.

Use the installed builtin algorithms in experiment

Once your customized algorithms is installed, you can use it in experiment configuration file the same way as other builtin tuners/assessors/advisors, for example:

tuner:
  builtinTunerName: demotuner
  classArgs:
    #choice: maximize, minimize
    optimize_mode: maximize

Manage builtin algorithms using nnictl algo

List builtin algorithms

Run following command to list the registered builtin algorithms:

nnictl algo list
+-----------------+------------+-----------+--------=-------------+------------------------------------------+
|      Name       |    Type    | Source    |      Class Name      |               Module Name                |
+-----------------+------------+-----------+----------------------+------------------------------------------+
| TPE             | tuners     | nni       | HyperoptTuner        | nni.hyperopt_tuner.hyperopt_tuner        |
| Random          | tuners     | nni       | HyperoptTuner        | nni.hyperopt_tuner.hyperopt_tuner        |
| Anneal          | tuners     | nni       | HyperoptTuner        | nni.hyperopt_tuner.hyperopt_tuner        |
| Evolution       | tuners     | nni       | EvolutionTuner       | nni.evolution_tuner.evolution_tuner      |
| BatchTuner      | tuners     | nni       | BatchTuner           | nni.batch_tuner.batch_tuner              |
| GridSearch      | tuners     | nni       | GridSearchTuner      | nni.gridsearch_tuner.gridsearch_tuner    |
| NetworkMorphism | tuners     | nni       | NetworkMorphismTuner | nni.networkmorphism_tuner.networkmo...   |
| MetisTuner      | tuners     | nni       | MetisTuner           | nni.metis_tuner.metis_tuner              |
| GPTuner         | tuners     | nni       | GPTuner              | nni.gp_tuner.gp_tuner                    |
| PBTTuner        | tuners     | nni       | PBTTuner             | nni.pbt_tuner.pbt_tuner                  |
| SMAC            | tuners     | nni       | SMACTuner            | nni.smac_tuner.smac_tuner                |
| PPOTuner        | tuners     | nni       | PPOTuner             | nni.ppo_tuner.ppo_tuner                  |
| Medianstop      | assessors  | nni       | MedianstopAssessor   | nni.medianstop_assessor.medianstop_...   |
| Curvefitting    | assessors  | nni       | CurvefittingAssessor | nni.curvefitting_assessor.curvefitt...   |
| Hyperband       | advisors   | nni       | Hyperband            | nni.hyperband_advisor.hyperband_adv...   |
| BOHB            | advisors   | nni       | BOHB                 | nni.bohb_advisor.bohb_advisor            |
+-----------------+------------+-----------+----------------------+------------------------------------------+

Unregister builtin algorithms

Run following command to uninstall an installed package:

nnictl algo unregister <builtin name>

For example:

nnictl algo unregister demotuner

Porting customized algorithms from v1.x to v2.x

All that needs to be modified is to delete NNI Package :: tuner metadata in setup.py and add a meta file mentioned in 4. Prepare meta file. Then you can follow Register customized algorithms as builtin tuners, assessors and advisors to register your customized algorithms.

Example: Register a customized tuner as a builtin tuner

You can following below steps to register a customized tuner in nni/examples/tuners/customized_tuner as a builtin tuner.

Install the customized tuner package into python environment

There are 2 options to install the package into python environment:

Option 1: install from directory

From nni/examples/tuners/customized_tuner directory, run:

python setup.py develop

This command will build the nni/examples/tuners/customized_tuner directory as a pip installation source.

Option 2: install from whl file

Step 1: From nni/examples/tuners/customized_tuner directory, run:

python setup.py bdist_wheel

This command build a whl file which is a pip installation source.

Step 2: Run command:

pip install dist/demo_tuner-0.1-py3-none-any.whl

Register the customized tuner as builtin tuner:

Run following command:

nnictl algo register --meta meta_file.yml

Check the registered builtin algorithms

Then run command nnictl algo list, you should be able to see that demotuner is installed:

+-----------------+------------+-----------+--------=-------------+------------------------------------------+
|      Name       |    Type    |   source  |      Class Name      |               Module Name                |
+-----------------+------------+-----------+----------------------+------------------------------------------+
| demotuner       | tuners     |    User   | DemoTuner            | demo_tuner                               |
+-----------------+------------+-----------+----------------------+------------------------------------------+