azure 機器學習工作區筆記本的版本控制

Question

我正在嘗試使用新的 Azure ML 工作區的容量，但找不到任何選項來在 git 上跟蹤我的筆記本。

這是可能的，以及您可以使用 Azure 筆記本做的事情嗎？ 如果不可能......如何使用這款筆記本電腦？ 僅在此工作空間內？

謝謝！

Answer 1

圍繞這個有一個完整的概念，稱為ML Ops 。 還有很多關於此的示例筆記本，例如如何將 Azure ML 與 Azure DevOps 結合使用。 例如這里。

Answer 2

AFAIK，Azure 機器學習筆記本目前不支持 Git。 如果您正在尋找功能更全面的開發環境，我建議您在本地設置一個。 前面還有更多工作要做，但它會讓您能夠進行版本控制。 查看此開發環境設置指南。 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.                                                                                                    |