Azure Machine Learning for Visual Studio Code, previously called Visual Studio Code Tools for AI, is an extension to easily build, train, and deploy machine learning models to the cloud or the edge with Azure Machine Learning service.
Note: Public releases are always even numbered (e.g. 0.14.0). Nightly releases are odd numbered (0.13.0), which the build system handles automatically. When you release, make sure to bump the version to the next even number. Once that is merged, nightly releases will start releasing with the next odd number. Currently we are bumping the minor version for each release.
Checklist
Testing/endgame week
[x] Create a release branch from version of master intended to be released. Name it "Rel#.#.#", e : Rel0.14.0
[x] Update version in package.json in the release branch
[x] Update CHANGELOG.md with changes made in the release branch.
[x] Submit the above edits as a PR and have it reviewed.
[x] Run through the Testing Checklist below to find any potential regressions using the latest pre-release version published to the marketplace by using insiders.vscode.dev (it will automatically use the latest pre-release version)
Release week Target date for release should match VS Code, which is usually Wednesday
[x] Merge the release PR. Note: the build pipeline only will allow a release from main, so we must merge before we can trigger the release pipeline run. In the future we will be able to release from the release branch.
[x] Tag the commit in main with the version number (e.g. v0.14.0). This will trigger a pipeline run in the release pipeline: https://dev.azure.com/monacotools/Monaco/_build?definitionId=320&_a=summary There is a manual review step, so this pipeline will build the release candidate, but won't release until you approve.
[x] Approve the Publish step in the release pipeline to allow the pipeline to upload/publish the vsix to the marketplace.
[x] Download the latest version from the marketplace (note, it may take up to 30 minutes and restarting vscode multiple times to force it to refresh the marketplace). Do a quick test to ensure the extension works as expected.
Testing Checklist
Connection
[x] Test simple connection (without any query parameter)
[x] Refresh the tab from the browser refresh button
[x] Run command Developer: Reload Window
[x] Open a different folder through VS Code (File -> Open Folder...)
Honor active notebook
[x] Open a notebook in ML Studio, click Open in VS Code (Web) and validate notebook is automatically opened
[ ] Validate ML Studio kernel session is selected
Not a regression, caused by changes in the Jupyter extension
Notebook execution
[x] Execute notebook
Azure ML Extension
[x] Azure ML Extension tree view is displayed
[x] Validate default workspace is automatically focused
[x] Expand resources under the default workspace
[x] Execute a YAML file to create an Azure ML Environment
Private Link
[x] Test all previous steps in a private link environment
Environment containers connection
[x] Validate connection to an environment container
[x] Validate VS Code remote label is Azure AI: <compute instance>/<environment>
[x] Validate the Azure ML extension is not installed
[x] Validate Python, Jupyter and Prompflow extension are installed
[x] Validate default python interpreter is working.
[x] Create a python file
[x] Check the interpreter is set correctly to Python 3.10
[x] Validate recommended kernel is the Python 3.10
[x] Create a notebook
[x] Click the kernel selector > Python environments
[x] Validate the recommended label is at the Python 3.10
Note: Public releases are always even numbered (e.g. 0.14.0). Nightly releases are odd numbered (0.13.0), which the build system handles automatically. When you release, make sure to bump the version to the next even number. Once that is merged, nightly releases will start releasing with the next odd number. Currently we are bumping the minor version for each release.
Checklist
Testing/endgame week
Release week Target date for release should match VS Code, which is usually Wednesday
Publish
step in the release pipeline to allow the pipeline to upload/publish the vsix to the marketplace.Testing Checklist
Connection
Developer: Reload Window
File
->Open Folder...
)Honor active notebook
Open in VS Code (Web)
and validate notebook is automatically openedNotebook execution
Azure ML Extension
Private Link
Environment containers connection
Azure AI: <compute instance>/<environment>