Deep learning (DL) projects often require integrating custom libraries with popular open-source frameworks such as TensorFlow, PyTorch, and Hugging Face. Setting up, managing, and scaling custom ML environments can be time consuming and cumbersome, even for experts. With AWS Deep Learning Containers, you get access to prepackaged and optimized DL frameworks that make it easy for you to customize, extend, and scale your environments. In this session, learn how to use Deep Learning Containers to build your custom ML environment and how to implement model training and inference with Deep Learning Containers in Amazon SageMaker.
In this session, explore how to choose the proper instance for ML training and inference based on model siz...
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