The easiest way to get started with PyTorch on AWS is using Amazon SageMaker, a fully managed service that provides every developer and data scientist with the ability to build, train, and deploy PyTorch models quickly. SageMaker removes the heavy lifting from each step of the machine learning process to make it easier to develop high quality models. To get started with PyTorch on SageMaker, use the following resources:
AWS Deep Learning Containers
AWS Deep Learning Containers are Docker images pre-installed with PyTorch to make it easy to deploy custom machine learning environments quickly by letting you skip the complicated process of building and optimizing your environments from scratch. To get started with PyTorch on AWS DL Containers, use the following resources:
Amazon EC2 Inf1 instances/ AWS Inferentia
Amazon EC2 Inf1 instances are built from the ground up to support machine learning inference applications. Inf1 instances feature up to 16 AWS Inferentia chips, high-performance machine learning inference chips designed and built by AWS. Inf1 instances deliver up to 3x higher throughput and up to 40% lower cost per inference than Amazon EC2 G4 instances, which were already the lowest cost instance for machine learning inference available in the cloud. Using Inf1 instances, you can run large scale machine learning inference with PyTorch models at the lowest cost in the cloud. To get started, see our tutorial on running PyTorch models on Inf1.
Amazon Elastic Inference
Amazon Elastic Inference allows you to attach low-cost GPU-powered acceleration to Amazon EC2 and SageMaker instances or Amazon ECS tasks, to reduce the cost of running inference with PyTorch models by up to 75%. To get started with PyTorch on Elastic Inference, see the following resources.
AWS Deep Learning AMI
AWS Deep Learning AMIs are machine images pre-installed with PyTorch, allowing you to quickly experiment with new algorithms or learn new skills and techniques. To get started, see the PyTorch on AWS Deep Learning AMIs tutorial.