azure 机器学习工作区笔记本的版本控制

Question

我正在尝试使用新的 Azure ML 工作区的容量，但找不到任何选项来在 git 上跟踪我的笔记本。

这是可能的，以及您可以使用 Azure 笔记本做的事情吗？ 如果不可能......如何使用这款笔记本电脑？ 仅在此工作空间内？

谢谢！

Answer 1

围绕这个有一个完整的概念，称为ML Ops 。 还有很多关于此的示例笔记本，例如如何将 Azure ML 与 Azure DevOps 结合使用。 例如这里。

Answer 2

AFAIK，Azure 机器学习笔记本目前不支持 Git。 如果您正在寻找功能更全面的开发环境，我建议您在本地设置一个。 前面还有更多工作要做，但它会让您能够进行版本控制。 查看此开发环境设置指南。 https://docs.microsoft.com/en-us/azure/machine-learning/how-to-configure-environment

| Environment                                                   | Pros                                                                                                                                                                                                                                    | Cons                                                                                                                                                                                 |
|---------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Cloud-based Azure Machine Learning compute instance (preview) | Easiest way to get started. The entire SDK is already installed in your workspace VM, and notebook tutorials are pre-cloned and ready to run.                                                                                           | Lack of control over your development environment and dependencies. Additional cost incurred for Linux VM (VM can be stopped when not in use to avoid charges). See pricing details. |
| Local environment                                             | Full control of your development environment and dependencies. Run with any build tool, environment, or IDE of your choice.                                                                                                             | Takes longer to get started. Necessary SDK packages must be installed, and an environment must also be installed if you don't already have one.                                      |
| Azure Databricks                                              | Ideal for running large-scale intensive machine learning workflows on the scalable Apache Spark platform.                                                                                                                               | Overkill for experimental machine learning, or smaller-scale experiments and workflows. Additional cost incurred for Azure Databricks. See pricing details.                          |
| The Data Science Virtual Machine (DSVM)                       | Similar to the cloud-based compute instance (Python and the SDK are pre-installed), but with additional popular data science and machine learning tools pre-installed. Easy to scale and combine with other custom tools and workflows. | A slower getting started experience compared to the cloud-based compute instance.                                                                                                    |