Accelerate deep learning model development with cloud custom environments

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Published Feb 20, 2025

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.

Learning Objectives:
* Objective 1: How to launch and use an Deep Learning Amazon Machine Image (AWS DLAMI).
* Objective 2: How to pull, customize, and extend Deep Learning Containers (AWS DLC).
* Objective 3: How to run large-scale training experiments with Amazon EKS and Amazon SageMaker.

***To learn more about the services featured in this talk, please visit: https://aws.amazon.com/machine-learning/containers/
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