The file is named config.Do you sweat every time you have to organize a bunch of new Python code? Should you make a module, a package, or both? Don't worry, I've been there thousand times. The workspace configuration file is a JSON file that tells the SDK how to communicate with your Azure Machine Learning workspace. Local and DSVM only: Create a workspace configuration file If you don't have one, you can create an Azure Machine Learning workspace through the Azure portal, Azure CLI, and Azure Resource Manager templates. Visual Studio Code: If you use Visual Studio Code, the Azure Machine Learning extension includes language support for Python, and features to make working with the Azure Machine Learning much more convenient and productive. Jupyter Notebooks: If you're already using Jupyter Notebooks, the SDK has some extras that you should install. This article also provides additional usage tips for the following tools: Additional cost incurred for Linux VM (VM can be stopped when not in use to avoid charges). Lack of control over your development environment and dependencies. The SDK is already installed in your workspace VM, and notebook tutorials are pre-cloned and ready to run. Easy to scale and combine with other custom tools and workflows.Ī slower getting started experience compared to the cloud-based compute instance.Įasiest way to get started. Similar to the cloud-based compute instance (Python is pre-installed), but with additional popular data science and machine learning tools pre-installed. Necessary SDK packages must be installed, and an environment must also be installed if you don't already have one. Run with any build tool, environment, or IDE of your choice. Environmentįull control of your development environment and dependencies. The following table shows each development environment covered in this article, along with pros and cons. Learn how to configure a Python development environment for Azure Machine Learning.

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