aml-run：GitHub操作，可让您将运行提交到Azure机器学习工作区

  # GitHub Action for training Machine Learning Models using Azure ## Usage The Azure Machine Learning training action will help you train your models on [Azure Machine Learning](https://azure.microsoft.com/en-us/services/machine-learning/) using GitHub Actions. Get started today with a [free Azure account](https://azure.com/free/open-source)! 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](https://docs.microsoft.com/en-us/azure/machine-learning/concept-train-machine-learning-model#python-sdk) ## Dependencies on other GitHub Actions * [Checkout](https://github.com/actions/checkout) Checkout your Git repository content into GitHub Actions agent. * [aml-workspace](https://github.com/Azure/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](https://github.com/Azure/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](https://github.com/machine-learning-apps/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](https://github.com/Azure/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 ```yaml 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](https://docs.microsoft.com/en-us/cli/azure/install-azure-cli?view=azure-cli-latest) on your computer or use the Cloud CLI and execute the following command to generate the required credentials: ```sh # 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: ```sh { "clientId": "<GUID>", "clientSecret": "<GUID>", "subscriptionId": "<GUID>", "tenantId": "<GUID>", (...) } ``` Add this JSON output as [a secret](https://help.github.com/en/actions/configuring-and-managing-workflows/creating-and-storing-encrypted-secrets#creating-encrypted-secrets) 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: {"<your-run-tag-key>": "<your-run-tag-value>", ...} | 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 t