If you're using a notebook inside studio, you'll need to copy one value, close the area and paste, then come back for the next one.

Copy the value for workspace, resource group and subscription ID into the code.

In the upper right Azure Machine Learning studio toolbar, select your workspace name.

Replace your Subscription ID, Resource Group name and Workspace name in the code below. Run this code to connect to your Azure ML workspace. Once your environment is set up, attach to the workspace in your Python script:ĪPPLIES TO: Python SDK azure-ai-ml v2 (current) 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.

Create an r package in rstudio