我正在尝试使用新的Azure ML工作区的功能,但找不到任何在git上跟踪笔记本的选项。
使用Azure笔记本可以做到这一点吗?如果不可能的话……该笔记本该如何使用?仅在此工作空间内?
谢谢!
答案 0 :(得分:1)
答案 1 :(得分:1)
AFAIK,Azure机器学习笔记本当前不支持Git。如果您正在寻找功能更全的开发环境,建议您在本地设置一个。前面还有更多工作要做,但这将使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. |