![]() ![]() ![]() You can alternatively use the VS Code Notebook Editor if you prefer to work with. To choose your default workspace, select the Set Azure ML Workspace button on the Visual Studio Code status bar and follow the prompts to set your workspace.Īlternatively, use the > Azure ML: Set Default Workspace command in the command palette and follow the prompts to set your workspace.This tutorial focuses on editing plain text Quarto. For more information, see manage Azure Machine Learning resources with the VS Code extension. If you don't have a workspace, create one. Choose your default workspaceĬhoosing a default Azure Machine Learning workspace enables the following when authoring CLI (v2) YAML specification files: To sign into you Azure account, select the Azure: Sign In button in the bottom right corner on the Visual Studio Code status bar to start the sign in process. Visit the following site to learn more about the Azure Account extension. To assist with account management, Azure Machine Learning automatically installs the Azure Account extension. In order to provision resources and job workloads on Azure, you have to sign in with your Azure account credentials. For more information on modifying your settings in Visual Studio, see the user and workspace settings documentation. To switch to the 1.0 CLI, set the azureML.CLI Compatibility Mode setting in Visual Studio Code to 1.0. ![]() ![]() The Azure Machine Learning VS Code extension uses the CLI (v2) by default. In the Extensions view search bar, type "Azure Machine Learning" and select the first extension. Select Extensions icon from the Activity Bar to open the Extensions view. For setup instructions, see Install, set up, and use the CLI (v2). (Optional) To create resources using the extension, you need to install the CLI (v2).If you don't have one, sign up to try the free or paid version of Azure Machine Learning. Schema-based language support, autocompletion and diagnostics for specification file authoring.Debug machine learning experiments locally.Develop locally using remote compute instances.Manage Azure Machine Learning resources (experiments, virtual machines, models, deployments, etc.).The Azure Machine Learning extension for VS Code provides a user interface to: Learn how to set up the Azure Machine Learning Visual Studio Code extension for your machine learning workflows. ![]()
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