GitHub Action for training Machine Learning Models using Azure

Deprecation notice

This Action is deprecated. Instead, consider using the CLI (v2) to manage and interact with Azure Machine Learning jobs (runs) in GitHub Actions.

Important: The CLI (v2) is not recommended for production use while in preview.

Usage

The Azure Machine Learning training action will help you train your models on Azure Machine Learning using GitHub Actions.

Get started today with a free Azure account!

This repository contains a GitHub Action for training machine learning models using Azure Machine Learning in a few different ways, each with different capabilities. To submit a training run, you have to define your python file(s) that should run remotely as well as a config file corresponding to one of the supported methods of training

Dependencies on other GitHub Actions

• Checkout Checkout your Git repository content into GitHub Actions agent.
• aml-workspace This action requires an Azure Machine Learning workspace to be present. You can either create a new one or re-use an existing one using the action.
• aml-compute You can use this action to create a new traininig environment if your workspace doesnt have one already.

Utilize GitHub Actions and Azure Machine Learning to train and deploy a machine learning model

This action is one in a series of actions that can be used to setup an ML Ops process. We suggest getting started with one of our template repositories, which will allow you to create an ML Ops process in less than 5 minutes.

1. Simple template repository: ml-template-azure

   Go to this template and follow the getting started guide to setup an ML Ops process within minutes and learn how to use the Azure Machine Learning GitHub Actions in combination. This template demonstrates a very simple process for training and deploying machine learning models.

2. Advanced template repository: aml-template

   This template demonstrates how the actions can be extended to include the normal pull request approval process and how training and deployment workflows can be split. More enhancements will be added to this template in the future to make it more enterprise ready.

Example workflow for training Machine Learning Models using Azure

name: My Workflow
on: [push, pull_request]
jobs:
  build:
    runs-on: ubuntu-latest
    steps:
    # Checks-out your repository under $GITHUB_WORKSPACE, so your job can access it
    - name: Check Out Repository
      id: checkout_repository
      uses: actions/checkout@v2

    # AML Workspace Action
    - uses: Azure/aml-workspace@v1
      id: aml_workspace
      with:
        azure_credentials: ${{ secrets.AZURE_CREDENTIALS }}

    # AML Run Action
    - uses: Azure/aml-run@v1
      id: aml_run
      with:
        # required inputs as secrets
        azure_credentials: ${{ secrets.AZURE_CREDENTIALS }}
        # optional
        parameters_file: "run.json"

Inputs

Input	Required	Default	Description
azure_credentials	x	-	Output of az ad sp create-for-rbac --name <your-sp-name> --role contributor --scopes /subscriptions/<your-subscriptionId>/resourceGroups/<your-rg> --sdk-auth. This should be stored in your secrets
parameters_file		"run.json"	We expect a JSON file in the .cloud/.azure folder in root of your repository specifying details of your Azure Machine Learning Run. If you have want to provide these details in a file other than "run.json" you need to provide this input in the action.

azure_credentials (Azure Credentials)

Azure credentials are required to connect to your Azure Machine Learning Workspace. These may have been created for an action you are already using in your repository, if so, you can skip the steps below.

Install the Azure CLI on your computer or use the Cloud CLI and execute the following command to generate the required credentials:

# Replace {service-principal-name}, {subscription-id} and {resource-group} with your Azure subscription id and resource group name and any name for your service principle
az ad sp create-for-rbac --name {service-principal-name} \
                         --role contributor \
                         --scopes /subscriptions/{subscription-id}/resourceGroups/{resource-group} \
                         --sdk-auth

This will generate the following JSON output:

{
  "clientId": "<GUID>",
  "clientSecret": "<GUID>",
  "subscriptionId": "<GUID>",
  "tenantId": "<GUID>",
  (...)
}

Add this JSON output as a secret with the name AZURE_CREDENTIALS in your GitHub repository.

parameters_file (Parameter File)

The action tries to load a JSON file in the .cloud/.azure folder in your repository, which specifies details of your Azure Machine Learning Run. By default, the action is looking for a file with the name "run.json". If your JSON file has a different name, you can specify it with this parameter. Note that none of these values are required and in the absence, defaults will be created with a combination of the repo name and branch name.

A sample file can be found in this repository in the folder .cloud/.azure. The JSON file can include the following parameters:

Parameter Name	Required	Allowed Values	Default	Description
experiment_name		str	<REPOSITORY_NAME>-<BRANCH_NAME>	The name of your experiment in AML, which must be 3-36 characters, start with a letter or a number, and can only contain letters, numbers, underscores, and dashes.
tags		dict: {"": "", ...}	null	Tags to be added to the submitted run.
wait_for_completion		bool	true	Indicates whether the action will wait for completion of the run
download_artifacts		bool	false	Indicates whether the created artifacts and logs from runs, pipelines and steps will be downloaded to your GitHub workspace. This only works if wait_for_completion is set to true.
pipeline_publish		bool	false	Indicates whether the action will publish the pipeline after submitting it to Azure Machine Learning. This only works if you submitted a pipeline.
pipeline_name		str	<REPOSITORY_NAME>-<BRANCH_NAME>	The name of the published pipeline. This only works if you submitted a pipeline.
pipeline_version		str	null	The version of the published pipeline. This only works if you submitted a pipeline.
pipeline_continue_on_step_failure		bool	false	Indicates whether the published pipeline will continue execution of other steps in the PipelineRun if a step fails. This only works if you submitted a pipeline.

Inputs specific to method of training

• Using a python script (default code/train/run_config.py) which includes a function (default def main(workspace):) that describes your run that you want to submit. If you want to change the default values for the python script, you can specify it with the runconfig_python_file and runconfig_python_function_name parameters. The python script gets the workspace object injected and has to return one of the following objects - Estimator, TensorFlow Estimator, PyTorch Estimator, Scikit Learn Estimator, Chainer Estimator, Pipeline or AutoMLConfig

Parameter Name	Required	Allowed Values	Default	Description
runconfig_python_file		str	"code/train/run_config.py"	Path to the python script in your repository in which you define your run and return an Estimator, Pipeline, AutoMLConfig or ScriptRunConfig object.
runconfig_python_function_name		str	"main"	The name of the function in your python script in your repository in which you define your run and return an Estimator, Pipeline, AutoMLConfig or ScriptRunConfig object. The function gets the workspace object passed as an argument.

• Using a runconfig YAML file (default "code/train/run_config.yml"), which describes your Azure Machine Learning Script Run that you want to submit. You can change the default value with the runconfig_yaml_file parameter.

Parameter Name	Required	Allowed Values	Default	Description
runconfig_yaml_file		str	"code/train/run_config.yml"	The name of your runconfig YAML file.

• Using a Pipeline YAML file (default "code/train/pipeline.yml"), which describes your Azure Machine Learning Pipeline that you want to submit. You can change the default value with the pipeline_yaml_file parameter.

Parameter Name	Required	Allowed Values	Default	Description
pipeline_yaml_file		str	"code/train/pipeline.yml"	The name of your pipeline YAML file.

Outputs

Output	Description
experiment_name	Name of the experiment of the run
run_id	ID of the run
run_url	URL to the run in the Azure Machine Learning Studio
run_metrics	Metrics of the run (will only be provided if wait_for_completion is set to True)
run_metrics_markdown	Metrics of the run formatted as markdown table (will only be provided if wait_for_completion is set to True)
published_pipeline_id	Id of the published pipeline (will only be provided if you submitted a pipeline and pipeline_publish is set to True)
published_pipeline_status	Status of the published pipeline (will only be provided if you submitted a pipeline and pipeline_publish is set to True)
published_pipeline_endpoint	Endpoint of the published pipeline (will only be provided if you submitted a pipeline and pipeline_publish is set to True)
artifact_path	Path of downloaded artifacts and logs from Azure Machine Learning (pipeline) run (will only be provided if wait_for_completion and download_artifacts is set to True)

Other Azure Machine Learning Actions

• aml-workspace - Connects to or creates a new workspace
• aml-compute - Connects to or creates a new compute target in Azure Machine Learning
• aml-run - Submits a ScriptRun, an Estimator or a Pipeline to Azure Machine Learning
• aml-registermodel - Registers a model to Azure Machine Learning
• aml-deploy - Deploys a model and creates an endpoint for the model

Contributing

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.

When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact [email protected] with any additional questions or comments.