Think PyTorch is just one library? Think again.

Here’s the PyTorch ecosystem every ML engineer should know:

PyTorch – Core deep learning framework https://pytorch.org

LibTorch – C++ API for production inference https://pytorch.org/cppdocs

ExecuTorch – On-device AI for mobile, edge & embedded devices https://pytorch.org/executorch

TorchServe – Model serving for production deployments https://github.com/pytorch/serve

TorchAO – Quantization & low-precision optimization https://github.com/pytorch/ao

TorchTitan – Train large language models at scale https://github.com/pytorch/torchtitan…

TorchRL – Reinforcement Learning library https://pytorch.org/rl

TorchVision – Computer Vision models & datasets https://pytorch.org/vision

TorchAudio – Speech & audio processing https://pytorch.org/audio

TorchText – NLP datasets & preprocessing https://pytorch.org/text

PyTorch Geometric (PyG) – Graph Neural Networks https://pytorch-geometric.readthedocs.io

Pyro – Probabilistic programming on PyTorch https://pyro.ai

Lightning – High-level training framework for PyTorch https://lightning.ai

Mastering the PyTorch ecosystem is just as important as mastering PyTorch itself.

PyTorch

Source: https://pytorch.org/ Get Started: Install PyTorch Locally or Launch Instantly on Supported Cloud Platforms

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Join PyTorch Foundation

As a member of the PyTorch Foundation, you’ll have access to resources that allow you to be stewards of stable, secure, and long-lasting codebases. You can collaborate on training, local and regional events, open-source developer tooling, academic research, and guides to help new users and contributors have a productive experience.

EXPLORE BENEFITS

Key Features & Capabilities

Production Ready

Transition seamlessly between eager and graph modes with TorchScript, and accelerate the path to production with TorchServe.

Distributed Training

Scalable distributed training and performance optimization in research and production is enabled by the torch.distributed backend.

Robust Ecosystem

A rich ecosystem of tools and libraries extends PyTorch and supports development in computer vision, NLP and more.

Cloud Support

PyTorch is well supported on major cloud platforms, providing frictionless development and easy scaling.

Install PyTorch

Select your preferences and run the install command. Stable represents the most currently tested and supported version of PyTorch. This should be suitable for many users. Preview is available if you want the latest, not fully tested and supported, builds that are generated nightly. Please ensure that you havemet the prerequisites below (e.g., numpy), depending on your package manager. You can alsoinstall previous versions of PyTorch. Note that LibTorch is only available for C++.

**NOTE:**Latest Stable PyTorch requires Python 3.10 or later.

pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118

Previous versions of PyTorch

Quick Start With Cloud Partners

Get up and running with PyTorch quickly through popular cloud platforms and machine learning services.

Ecosystem

Featured Projects

Explore a rich ecosystem of libraries, tools, and more to support development.

Captum

Captum (“comprehension” in Latin) is an open source, extensible library for model interpretability built on PyTorch.

PyTorch Geometric

PyTorch Geometric is a library for deep learning on irregular input data such as graphs, point clouds, and manifolds.

skorch

skorch is a high-level library for PyTorch that provides full scikit-learn compatibility.

Companies & Universities Using PyTorch

Amazon Advertising

Reduce inference costs by 71% and scale out using PyTorch, TorchServe, and AWS Inferentia.

READ CASE STUDIES

Salesforce

Pushing the state of the art in NLP and Multi-task learning.

Stanford University

Using PyTorch’s flexibility to efficiently research new algorithmic approaches.