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articles/machine-learning/how-to-manage-workspace.md

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> * [v1](v1/how-to-manage-workspace.md)
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> * [v2 (current)](how-to-manage-workspace.md)
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In this article, you create, view, and delete [**Azure Machine Learning workspaces**](concept-workspace.md) for [Azure Machine Learning](overview-what-is-azure-machine-learning.md), using the [Azure portal](https://portal.azure.com) or the [SDK for Python](https://aka.ms/sdk-v2-install).
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In this article, you create, view, and delete [**Azure Machine Learning workspaces**](concept-workspace.md) for [Azure Machine Learning](overview-what-is-azure-machine-learning.md), using the [Azure portal](https://portal.azure.com) or the [SDK for Python](/python/api/overview/azure/ml/).
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As your needs change or requirements for automation increase you can also manage workspaces [using the CLI](how-to-manage-workspace-cli.md), or [via the VS Code extension](how-to-setup-vs-code.md).
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articles/machine-learning/how-to-run-jupyter-notebooks.md

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Only you can see and use the compute instances you create. Your **User files** are stored separately from the VM and are shared among all compute instances in the workspace.
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### View logs and output
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Use [notebook widgets](/python/api/azureml-widgets/azureml.widgets) to view the progress of the run and logs. A widget is asynchronous and provides updates until training finishes. Azure Machine Learning widgets are also supported in Jupyter and JupterLab.
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:::image type="content" source="media/how-to-run-jupyter-notebooks/jupyter-widget.png" alt-text="Screenshot: Jupyter notebook widget ":::
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## Explore variables in the notebook
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