Example notebooks - Amazon SageMaker
Text generationImage generationModel customization
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Example notebooks

For step-by-step examples on how to use publicly available JumpStart foundation models with the SageMaker Python SDK, refer to the following notebooks on text generation, image generation, and model customization.

Note

Proprietary and publicly available JumpStart foundation models have different SageMaker Python SDK deployment workflows. Discover proprietary foundation model example notebooks through Amazon SageMaker Studio Classic or the SageMaker console. For more information, see How to use JumpStart foundation models.

You can clone the Amazon SageMaker examples repository to run the available JumpStart foundation model examples in the Jupyter environment of your choice within Studio. For more information on applications that you can use to create and access Jupyter in SageMaker, see Applications supported in Amazon SageMaker Studio.

Text generation

Explore text generation example notebooks, including guidance on general text generation workflows, multilingual text classification, real-time batch inference, few-shot learning, chatbot interactions, and more.

Image generation

Get started with text-to-image Stable Diffusion models, learn how to deploy an inpainting model, and experiment with a simple workflow to generate images of your dog.

Model customization

Sometimes your use case requires greater foundation model customization for specific tasks. For more information on model customization approaches, see Customize a foundation model or explore one of the following example notebooks.