Download a model notebook - Amazon SageMaker
Services or capabilities described in Amazon Web Services documentation might vary by Region. To see the differences applicable to the China Regions, see Getting Started with Amazon Web Services in China (PDF).

Download a model notebook

Note

The model notebook feature is only available for tabular models and fine-tuned foundation models. Model notebooks aren't supported for image prediction, text prediciton, or time series forecasting models.

If you'd like to generate a model notebook for a tabular model built before this feature was launched, you must rebuild the model to generate a notebook.

For eligible models that you successfully build in Amazon SageMaker Canvas, a Jupyter notebook containing a report of all the model building steps is generated. This Jupyter notebook contains Python code that you can run locally or run in an environment like Amazon SageMaker Studio Classic to replicate the steps necessary to build your model. The notebook can be useful if you’d like to experiment with the code or see the backend details of how Canvas builds models.

To access the model notebook, do the following:

  1. Open the SageMaker Canvas application.

  2. In the left navigation pane, choose My models.

  3. Choose the model and version that you built.

  4. On the model version’s page, choose the More options icon ( ) in the header.

  5. From the dropdown menu, choose View notebook.

  6. A popup appears with the notebook content. You can choose Download and then do one of the following:

    1. Choose Download to save the notebook content to your local device.

    2. Choose Copy S3 URI to copy the Amazon S3 location where the notebook is stored. The notebook is stored in the Amazon S3 bucket specified in your Canvas storage configuration, which is configured in the Prerequisites for setting up Amazon SageMaker Canvas section.

You should now be able to view the notebook either locally or as an object in Amazon S3. You can upload the notebook to an IDE to edit and run the code, or you can share the notebook with others in your organization to review.