Custom Docker containers with SageMaker AI
You can adapt an existing Docker image to work with SageMaker AI. You may need to use an existing, external Docker image with SageMaker AI when you have a container that satisfies feature or safety requirements that are not currently supported by a pre-built SageMaker AI image. There are two toolkits that allow you to bring your own container and adapt it to work with SageMaker AI:
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SageMaker Training Toolkit
– Use this toolkit for training models with SageMaker AI. -
SageMaker AI Inference Toolkit
– Use this toolkit for deploying models with SageMaker AI.
The following topics show how to adapt your existing image using the SageMaker Training and Inference toolkits:
Topics
Individual Framework Libraries
In addition to the SageMaker Training Toolkit and SageMaker AI Inference Toolkit, SageMaker AI also provides toolkits specialized for TensorFlow, MXNet, PyTorch, and Chainer. The following table provides links to the GitHub repositories that contain the source code for each framework and their respective serving toolkits. The instructions linked are for using the Python SDK to run training algorithms and host models on SageMaker AI. The functionality for these individual libraries is included in the SageMaker AI Training Toolkit and SageMaker AI Inference Toolkit.
Framework | Toolkit Source Code |
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TensorFlow |
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MXNet |
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PyTorch |
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Chainer |