Azure机器学习模板.zip

# ML Ops with GitHub Actions and Azure Machine Learning <p align="center"> <img src="docs/images/aml.svg" height="80"/> <img src="https://i.ya-webdesign.com/images/a-plus-png-2.png" alt="plus" height="40"/> <img src="docs/images/actions.png" alt="Azure Machine Learning + Actions" height="80"/> </p> This template can be used for easily setting up a data science or machine learning project with automated training and deployment using [GitHub Actions](https://github.com/features/actions) and [Azure Machine Learning](https://docs.microsoft.com/en-us/azure/machine-learning/). For a more comprehensive version of this automated pipeline, see the [aml-template](https://github.com/Azure/aml-template) repository. # Getting started ### YouTube Video Click on the image to view the video on YouTube. The video shows you the setup process, which is also described below: [](http://www.youtube.com/watch?v=bmFr0LYo_6o "Azure Machine Learning GitHub Actions - Setup Guide") ### 1. Prerequisites The following prerequisites are required to make this repository work: - Azure subscription - Contributor access to the Azure subscription - Access to [GitHub Actions](https://github.com/features/actions) If you don’t have an Azure subscription, create a free account before you begin. Try the [free or paid version of Azure Machine Learning](https://aka.ms/AMLFree) today. ### 2. Create repository To get started with ML Ops, simply create a new repo based off this template, by clicking on the green "Use this template" button: <p align="center"> <img src="https://help.github.com/assets/images/help/repository/use-this-template-button.png" alt="GitHub Template repository" width="700"/> </p> ### 3. Setting up the required secrets A service principal needs to be generated for authentication and getting access to your Azure subscription. We suggest adding a service principal with contributor rights to a new resource group or to the one where you have deployed your existing Azure Machine Learning workspace. Just go to the Azure Portal to find the details of your resource group or workspace. Then start the Cloud CLI or install the [Azure CLI](https://docs.microsoft.com/en-us/cli/azure/install-azure-cli?view=azure-cli-latest) on your computer 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: <p align="center"> <img src="docs/images/secrets.png" alt="GitHub Template repository" width="700"/> </p> To do so, click on the Settings tab in your repository, then click on Secrets and finally add the new secret with the name `AZURE_CREDENTIALS` to your repository. Please follow [this link](https://help.github.com/en/actions/configuring-and-managing-workflows/creating-and-storing-encrypted-secrets#creating-encrypted-secrets) for more details. ### 4. Define your workspace parameters You have to modify the parameters in the <a href="/.cloud/.azure/workspace.json">`/.cloud/.azure/workspace.json"` file</a> in your repository, so that the GitHub Actions create or connect to the desired Azure Machine Learning workspace. Just click on the link and edit the file. Please use the same value for the `resource_group` parameter that you have used when generating the azure credentials. If you already have an Azure ML Workspace under that resource group, change the `name` parameter in the JSON file to the name of your workspace, if you want the Action to create a new workspace in that resource group, pick a name for your new workspace, and assign it to the `name` parameter. You can also delete the `name` parameter, if you want the action to use the default value, which is the repository name. Once you save your changes to the file, the predefined GitHub workflow that trains and deploys a model on Azure Machine Learning gets triggered. Check the actions tab to view if your actions have successfully run. <p align="center"> <img src="docs/images/actions_tab.png" alt="GitHub Actions Tab" width="700"/> </p> ### 5. Modify the code Now you can start modifying the code in the <a href="/code">`code` folder</a>, so that your model and not the provided sample model gets trained on Azure. Where required, modify the environment yaml so that the training and deployment environments will have the correct packages installed in the conda environment for your training and deployment. Upon pushing the changes, actions will kick off your training and deployment run. Check the actions tab to view if your actions have successfully run. Comment lines 39 to 55 in your <a href="/.github/workflows/train_deploy.yml">`"/.github/workflows/train_deploy.yml"` file</a> if you only want to train the model. Uncomment line 7 to 8, if you only want to kick off the workflow when pushing changes to the `"/code/"` file. ### 6. Viewing your AML resources and runs The log outputs of your action will provide URLs for you to view the resources that have been created in AML. Alternatively, you can visit the [Machine Learning Studio](https://ml.azure.com/) to view the progress of your runs, etc. For more details, read the documentation below. # Documentation ## Code structure | File/folder | Description | | ----------------------------- | ------------------------------------------ | | `code` | Sample data science source code that will be submitted to Azure Machine Learning to train and deploy machine learning models. | | `code/train` | Sample code that is required for training a model on Azure Machine Learning. | | `code/train/train.py` | Training script that gets executed on a cluster on Azure Machine Learning. | | `code/train/environment.yml` | Conda environment specification, which describes the dependencies of `train.py`. These packages will be installed inside a Docker image on the Azure Machine Learning compute cluster, when executing your `train.py`. | | `code/train/run_config.yml` | YAML files, which describes the execution of your training run on Azure Machine Learning. This file also references your `environment.yml`. Please look at the comments in the file for more details. | | `code/deploy` | Sample code that is required for deploying a model on Azure Machine Learning. | | `code/deploy/score.py` | Inference script that is used to build a Docker image and that gets executed within the container when you send data to the deployed model on Azure Machine Learning. | | `code/deploy/environment.yml` | Conda environment specification, which describes the dependencies of `score.py`. These packages will be installed inside the Docker image that will be used for deploying your model. | | `code/test/test.py` | Test script that can be used for testing your deployed webservice. Add a `deploy.json` to the `.cloud/.azure` folder and add the following code `{ "test_enabled": true }` to enable tests of your webservice. Change the code