FAQ
With the Azure Machine Learning for Visual Studio Code extension you can easily build, train, and deploy machine learning models to the cloud or the edge with Azure Machine Learning service from the Visual Studio Code interface. Earlier versions of this extension were released under the name Visual Studio Code Tools for AI.
The Azure Machine Learning extension for VS Code provides a user interface to:
- Manage Azure Machine Learning resources (experiments, virtual machines, models, deployments, etc.)
- Develop locally using remote compute instances
- Train machine learning models
- Debug machine learning experiments locally
- Schema-based language support, autocompletion and diagnostics for specification file authoring
This extension is complementary to the Azure Machine Learning extension and is used to seamlessly connect VS Code to a remote Compute Instance.
Once connected to the compute instance, you can:
- Run and debug Python scripts and Notebooks, executing directly on the CI
- Traverse the remote filesystem and mounted fileshare
- Invoke commands in a remote terminal
- Clone and manage git repos
To set up Azure Machine Learning extension in VSCode, follow the instructions here
To connect to a remote Compute instance using Azure ML extension, follow the instructions here
How to interactively debug Azure Machine Learning experiments, pipelines, and deployments using Azure ML extensions in VSCode ?
To use interactive debugging in VSCode using Azure ML extension, follow the instructions here
To learn how to manage Azure Machine Learning resources with the VS Code extension, follow the instructions here
How to train an image classification model using TensorFlow and the Azure Machine Learning Extension ?
To learn how to train an image classification model to recognize hand-written numbers using TensorFlow and the Azure Machine Learning Visual Studio Code Extension, follow the instructions here
In case the latest version of the extension is not working for you, please open an issue in our repo. In the meanwhile, you can install a previous version of the extension by following the steps here
To create resources using the Azure ML extension, you need to install the CLI (v2). For setup instructions, see Install, set up, and use the CLI (v2).
- You can enable Trace logging by running the command Developer: Set Log Level and then selecting the Azure ML option and then selecting Trace
- Then just use the features that you want to capture the logs for, open the Output Channel (Ctrl + Shift + U or View -> Output), and select the Azure ML output:
- You can enable Trace logging by changing the user setting azureML.yaml.trace.server to messages
- Then just use the YAML language features, open the Output Channel (Ctrl + Shift + U or View -> Output), and select the Azure ML YAML Support output:
Please refer to the steps below to get trace logs in Azure Machine Learning - Remote extension:
- You can enable Trace logging by running the command Developer: Set Log Level and then selecting the Azure ML - Remote option and then selecting Trace
- Then just try to make a remote connection in the VSCode window and after the connection either succeeds or fails, open the Output Channel (Ctrl + Shift + U or View -> Output), and select the Azure ML - Remote output:
Please refer to the steps below to get logs for Azure Machine Learning - Remote (Web) connection in VS Code Web (vscode.dev):
- Connect to VS Code Web using Azure Machine Learning Studio or AI Studio, open the Output Channel (Ctrl + Shift + U or View -> Output), and select the Azure ML - Remote Web output:
- Now select, vscode.dev output:
To install a pre-release version of an extension in Visual Studio Code, please follow the instructions here
To install an extension from VSIX in Visual Studio Code, please follow the instructions here
You can install a previous release of VS Code by uninstalling your current version and then installing the download provided at the top of a specific release notes page.
Note: On Linux: If the VS Code repository was installed correctly then your system package manager should handle auto-updating in the same way as other packages on the system. See Installing VS Code on Linux.