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articles/machine-learning/how-to-access-terminal.md

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ms.subservice: compute
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ms.topic: how-to
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ms.date: 09/20/2024
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ms.custom: FY25Q1-Linter
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#Customer intent: As a data scientist, I want to use Git, install packages and add kernels to a compute instance in my workspace in Azure Machine Learning studio.
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articles/machine-learning/how-to-create-attach-compute-studio.md

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ms.date: 09/17/2024
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ms.topic: how-to
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ms.custom: build-2023
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ms.custom: build-2023, FY25Q1-Linter
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# customer intent: As a professional data scientist, I want to manage compute resources for model training and deployment in Azure Machine Learning studio.
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articles/machine-learning/how-to-run-jupyter-notebooks.md

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ms.custom: devx-track-python
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ms.custom: devx-track-python, FY25Q1-Linter
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ms.topic: how-to
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ms.date: 08/14/2024
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#Customer intent: As a data scientist, I want to run Jupyter notebooks in my workspace in Azure Machine Learning studio.
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* **File upload limit**: When uploading a file through the notebook's file explorer, you're limited files that are smaller than 5 TB. If you need to upload a file larger than this, we recommend that you use the SDK to upload the data to a datastore. For more information, see [Create data assets](how-to-create-data-assets.md?tabs=Python-SDK).
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## Next steps
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## Related content
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* [Run your first experiment](tutorial-1st-experiment-sdk-train.md)
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* [Backup your file storage with snapshots](/azure/storage/files/storage-snapshots-files)

articles/machine-learning/how-to-train-pytorch.md

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ms.custom: sdkv2, update-code1
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ms.custom: sdkv2, update-code1, FY25Q1-Linter
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#Customer intent: As a Python PyTorch developer, I need to combine open-source with a cloud platform to train, evaluate, and deploy my deep learning models at scale.
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