If using the Python SDK, set up your development environment with a workspace. The Azure CLI extension for Machine Learning service (v2), Azure Machine Learning Python SDK (v2), or the Azure Machine Learning Visual Studio Code extension. For more information, see Create an Azure Machine Learning workspace.

PrerequisitesĪn Azure Machine Learning workspace. If you are still using CLI v1, see Create an Azure Machine Learning compute cluster CLI v1). This article shows CLI v2 in the sections below. The job executes in a containerized environment and packages your model dependencies in a Docker container. You can also use a setup script to create the compute instance with your own custom environment.Ĭompute instances can run jobs securely in a virtual network environment, without requiring enterprises to open up SSH ports.

Create a schedule to automatically start and stop the compute instance.

Manage (start, stop, restart, delete) a compute instance.

As a development environment, a compute instance can't be shared with other users in your workspace.

A compute instance can run multiple jobs in parallel and has a job queue. For development and testing, you can also use the instance as a training compute target. Use a compute instance as your fully configured and managed development environment in the cloud. Learn how to create and manage a compute instance in your Azure Machine Learning workspace.

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