PyTorch on AWS

Flexible open-source machine learning framework

PyTorch is an open source, deep learning framework that makes it easy to develop machine learning models and deploy them to production. PyTorch allows developers to iterate quickly on their models in the prototyping stage without sacrificing performance in the production stage. Using PyTorch’s TorchScript, developers can seamlessly transition between eager mode, which performs computations immediately for easy development, and graph mode which creates computational graphs for efficient execution in production environments. PyTorch supports dynamic computation graphs, which provides a flexible structure that is intuitive to work with and easy to debug. PyTorch also offers distributed training, deep integration into Python, and a rich ecosystem of tools and libraries, making it popular with researchers and engineers.

You can get started with PyTorch on AWS using Amazon SageMaker, a fully managed machine learning service that makes it easy and cost-effective to build, train, and deploy PyTorch models at scale. If you prefer to manage the infrastructure yourself, you can use the AWS Deep Learning AMIs or the AWS Deep Learning Containers, which come pre-installed with PyTorch to quickly deploy custom machine learning environments.


Easy to use

Build using deep Python integrations that make it easy to develop and debug your models. Deploy your models easily to production using TorchServe, a PyTorch model serving library. Use TorchScript to easily switch between eager mode (used for iterative model development) and graph mode (used for efficient training once the model is built).

Built for high performance

Train your models rapidly using distributed training backends that leverage clusters of powerful GPU instances, and libraries such as TorchElastic that provide fault-tolerance and elasticity so you can iterate faster and bring models to market sooner.

Rich ecosystem

A rich ecosystem of tools and models, including torchvision, torchaudio, torchtext, torchelastic, torch_xla, extends PyTorch and supports development in computer vision, natural language processing, privacy preserving ML, model interpretability, and more.

How it works

How it works - PyTorch on AWS

AWS Open-Source Contributions to PyTorch


TorchServe is an open-source model serving framework for PyTorch that makes it easy to deploy trained PyTorch models performantly at scale without having to write custom code. TorchServe delivers lightweight serving with low latency, so you can deploy your models for high performance inference. It provides default handlers for the most common applications such as object detection and text classification, so you don’t have to write custom code to deploy your models. With powerful TorchServe features including multi-model serving, model versioning for A/B testing, metrics for monitoring, and RESTful endpoints for application integration, you can take your models from research to production quickly. TorchServe supports any machine learning environment, including Amazon SageMaker, Kubernetes, Amazon EKS, and Amazon EC2. To get started with TorchServe, see the documentation and our blog post.

TorchElastic Controller for Kubernetes

TorchElastic is a library for training large-scale deep learning models where it’s critical to scale compute resources dynamically based on availability. Elastic and fault-tolerant training with TorchElastic can help you take ML models to production faster and adopt state-of-the-art approaches to model exploration as architectures continue to increase in size and complexity. The TorchElastic Controller for Kubernetes is a native Kubernetes implementation for TorchElastic that automatically manages the lifecycle of the pods and services required for TorchElastic training. It allows you to start training jobs with a portion of the requested compute resources, and dynamically scale later as more resources become available, without having to stop and restart the jobs. In addition, jobs can recover from nodes that are replaced due to node failures or reclamation. With TorchElastic Controller for Kubernetes, you can reduce distributed training time and cost by limiting idle cluster resources and training on Amazon EC2 Spot Instances. To get started with the TorchElastic Controller on your Kubernetes cluster, see the tutorial.

Customer Testimonials

Toyota Research Institute (TRI)

Toyota Research Institute Advanced Development, Inc. (TRI-AD) is applying artificial intelligence to help Toyota produce cars in the future that are safer, more accessible, and more environmentally friendly. Using PyTorch on Amazon EC2 P3 instances, TRI-AD reduced ML model training time from days to hours. “We continuously optimize and improve our computer vision models, which are critical to TRI-AD’s mission of achieving safe mobility for all with autonomous driving. Our models are trained with PyTorch on AWS, but until now PyTorch lacked a model serving framework. As a result, we spent significant engineering effort in creating and maintaining software for deploying PyTorch models to our fleet of vehicles and cloud servers. With TorchServe, we now have a performant and lightweight model server that is officially supported and maintained by AWS and the PyTorch community,” Yusuke Yachide, Lead of ML Tools at TRI-AD.


Matroid, maker of computer vision software that detects objects and events in video footage, develops a rapidly growing number of machine learning models using PyTorch on AWS and on-premise environments. The models are deployed using a custom model server that requires converting the models to a different format, which is time-consuming and burdensome. TorchServe allows Matroid to simplify model deployment using a single servable file that also serves as the single source of truth, and is easy to share and manage.


Pinterest has 3 billion images and 18 billion associations connecting those images. The company has developed PyTorch deep learning models to contextualize these images and deliver a personalized user experience. Pinterest uses Amazon EC2 P3 instances to speed up model training and deliver low latency inference for an interactive user experience. Read more.

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