Awesome

GitHub Action for deploying Machine Learning Models to Azure

Deprecation notice

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

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

Usage

The Deploy Machine Learning Models to Azure action will deploy your model on Azure Machine Learning using GitHub Actions.

Get started today with a free Azure account!

This repository contains GitHub Action for deploying Machine Learning Models to Azure Machine Learning and creates a real-time endpoint on the model to integrate models in other systems. The endpoint can be hosted either on an Azure Container Instance or on an Azure Kubernetes Service.

This GitHub Action also allows you to provide a python script that executes tests against the Webservice endpoint after the model deployment has completed successfully. You can enable tests by setting the parameter test_enabled to true. In addition to that, you have to provide a python script (default code/test/test.py) which includes a function (default def main(webservice):) that describes your tests that you want to execute against the service object. The python script gets the webservice object injected. The action fails, if the test script fails.

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-registermodel Before deploying the model, you need to register the model with Azure Machine Learning. If not already registered, you can use this action and use its output in deploy action.
• aml-compute You don't need this if you want to host your endpoint on an ACI instance. But, if you want to host your endpoint on an AKS cluster, you can manage the AKS Cluster via the action.

Create Azure Machine Learning and deploy an machine learning model using GitHub Actions

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: mlops-enterprise-template

   This template demonstrates how approval processes can be included in the process and how training and deployment workflows can be splitted. It also shows how workflows (e.g. deployment) can be triggered from pull requests. More enhancements will be added to this template in the future to make it more enterprise ready.

Example workflow

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 Register Model Action
    - uses: Azure/aml-registermodel@v1
      id: aml_registermodel
      with:
        azure_credentials: ${{ secrets.AZURE_CREDENTIALS }}
        run_id:  "<your-run-id>"
        experiment_name: "<your-experiment-name>"
    
    # Deploy model in Azure Machine Learning to ACI
    - name: Deploy model
      id: aml_deploy
      uses: Azure/aml-deploy@v1
      with:
        # required inputs
        azure_credentials: ${{ secrets.AZURE_CREDENTIALS }}
        model_name:  ${{ steps.aml_registermodel.outputs.model_name }}
        model_version: ${{ steps.aml_registermodel.outputs.model_version }}
        # optional inputs
        parameters_file: "deploy.json"

Inputs

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 (Parameters File)

The action tries to load a JSON file in the .cloud/.azure folder in your repository, which specifies details for the model deployment to your Azure Machine Learning Workspace. By default, the action expects a file with the name deploy.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. There are separate parameters that are used for the ACI deployment, the AKS deployment and some that are common for both deployment options.

Common parameters

Please visit this website and this website for more details.

ACI specific parameters

ACI is the default deployment resource. A sample file for an aci deployment can be found in the .cloud/.azure folder.

Please visit this website for more details.

AKS Deployment

For the deployment of the model to AKS, you must configure an AKS resource and specify the name of the AKS cluster with the deployment_compute_target parameter. Additional parameters allow you to finetune your deployment on AKS with options like autoscaling and the liveness probe requirements. These will be set to default parameters if not provided.

Please visit this website for more details. More Information on autoscaling parameters can be found here and for liveness probe here.

Outputs

Environment variables

Certain parameters are considered secrets and should therefore be passed as environment variables from your secrets, if you want to use custom values.

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 opencode@microsoft.com with any additional questions or comments.