You can create Azure Machine Learning datasets using the extension.
You can choose from any of the Azure Machine Learning curated or create your own. Alternatively, if you already have one, select it from the dropdown. The run configuration defines the script you want to run, dependencies, and datasets used. Select Create new Run Configuration to create your run configuration.
Selecting no will run your experiment locally without attaching to the debugger. Additionally, it also allows Docker to store the logs and outputs from your run in a temporary directory on your system. When you enable file share, it allows Docker to mount the directory containing your script to the container. When prompted to allow File Share, select Yes. When the prompt appears, provide a name for your experiment.Įxpand the Experiments node, right-click the experiment you want to run and select Run Experiment.įrom the list of options, select Locally.įirst time use on Windows only. Right-click the Experiments node and select Create experiment. If you don't already have one, you can create an Azure Machine Learning workspace using the extension. In VS Code, open the Azure Machine Learning extension view.Įxpand the subscription node containing your workspace. The azureML.CLI Compatibility Mode setting in Visual Studio Code is set to 1.0 as specified in the prerequisites.Before running your experiment locally make sure that:
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