Overview
The AWS Deep Learning Containers for PyTorch include containers for training and inference for CPU and GPU, optimized for performance and scale on AWS. These Docker images have been tested with SageMaker, EC2, ECS, and EKS and provide stable versions of NVIDIA CUDA, cuDNN, Intel MKL, Horovod and other required software components to provide a seamless user experience for deep learning workloads. All software components in these images are scanned for security vulnerabilities and updated or patched in accordance with AWS Security best practices.
Highlights
- Optimized and stable Docker images for training and inference with PyTorch
- Built for use with Amazon SageMaker, Amazon EKS, Amazon ECS and Amazon EC2
- Get started with AWS Deep Learning Containers https://docs.aws.amazon.com/dlami/latest/devguide/deep-learning-containers.html
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PyTorch Training
- Amazon EKS
- Amazon ECS
Container image
Containers are lightweight, portable execution environments that wrap server application software in a filesystem that includes everything it needs to run. Container applications run on supported container runtimes and orchestration services, such as Amazon Elastic Container Service (Amazon ECS) or Amazon Elastic Kubernetes Service (Amazon EKS). Both eliminate the need for you to install and operate your own container orchestration software by managing and scheduling containers on a scalable cluster of virtual machines.
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AWS Deep Learning Containers for PyTorch
Support is available through AWS Premium Support, AWS forums, technical FAQs, and the Service Help Dashboard. Post your questions to the AWS Deep Learning Containers Discussion Forum
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The AWS Deep Learning Containers for PyTorch include containers for training and inference for CPU and GPU, optimized for performance and scale on AWS. These Docker images have been tested with SageMaker, EC2, ECS, and EKS and provide stable versions of NVIDIA CUDA, cuDNN, Intel MKL
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Working with AWS DLC significantly accelerates the ML deployment.
1) ML model portability
2) ML deployment speed
3) reduced ML production time