PyTorch enables fast, flexible experimentation and efficient production through a user-friendly front-end, distributed training, and ecosystem of tools and libraries. It is an open source machine learning framework that accelerates the path from research prototyping to production deployment.
It is an open source machine learning framework that accelerates the path from research prototyping to production deployment. This video explains the fundamental concepts behind deep learning, and how tools like PyTorch enable developers to build and deploy AI.
PRODUCTION READY
With TorchScript, PyTorch provides ease-of-use and flexibility in eager mode, while seamlessly transitioning to graph mode for speed, optimization, and functionality in C++ runtime environments.
TORCHSERVE (EXPERIMENTAL)
TorchServe is an easy to use tool for deploying PyTorch models at scale. It is cloud and environment agnostic and supports features such as multi-model serving, logging, metrics and the creation of RESTful endpoints for application integration.
DISTRIBUTED TRAINING
Optimize performance in both research and production by taking advantage of native support for asynchronous execution of collective operations and peer-to-peer communication that is accessible from Python and C++.
MOBILE (EXPERIMENTAL)
PyTorch supports an end-to-end workflow from Python to deployment on iOS and Android. It extends the PyTorch API to cover common preprocessing and integration tasks needed for incorporating ML in mobile applications.
ROBUST ECOSYSTEM
An active community of researchers and developers have built a rich ecosystem of tools and libraries for extending PyTorch and supporting development in areas from computer vision to reinforcement learning.
NATIVE ONNX SUPPORT
Export models in the standard ONNX (Open Neural Network Exchange) format for direct access to ONNX-compatible platforms, runtimes, visualizers, and more.
C++ FRONT-END
The C++ frontend is a pure C++ interface to PyTorch that follows the design and architecture of the established Python frontend. It is intended to enable research in high performance, low latency and bare metal C++ applications.
CLOUD SUPPORT
PyTorch is well supported on major cloud platforms, providing frictionless development and easy scaling through prebuilt images, large scale training on GPUs, ability to run models in a production scale environment, and more.
Find out how to get started with PyTorch on the PyTorch website.
