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Choose Your Path: Install PyTorch Locally or Launch Instantly on Supported Cloud Platforms

Get started December 18, 2025 in Blog

Deploying Smarter: Hardware-Software Co-design in PyTorch

If you want powerful on-device AI that doesn’t blow your memory budget or turn your phone into a hand-warmer, you need tools more refined than post-training quantization, where the model… Read More December 17, 2025 in Blog

Enabling Cluster Launch Control with TLX

What is cluster launch control (CLC)? Blackwell brings in cluster launch control (CLC) to enable dynamic scheduling. This capability allows the kernel to launch a grid with as many threadblocks… Read More December 17, 2025 in Announcements, Blog

PyTorch Ecosystem Working Group Update & Project Spotlights, Q4 2025

As part of this blog series, we share updates on new projects joining the PyTorch Ecosystem and highlight projects currently under consideration that may benefit from broader community engagement. Ecosystem… Read More

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 have met the prerequisites below (e.g., numpy), depending on your package manager. You can also install previous versions of PyTorch. Note that LibTorch is only available for C++.

NOTE: Latest Stable PyTorch requires Python 3.10 or later.

PyTorch Build Your OS Package Language Compute Platform Run this Command: PyTorch Build Stable (2.7.0) Preview (Nightly) Your OS Linux Mac Windows Package Pip LibTorch Source Language Python C++ / Java Compute Platform CUDA 11.8 CUDA 12.6 CUDA 12.8 ROCm 6.3 CPU Run this Command: 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.

Amazon Web Services

• PyTorch on AWS
• Amazon SageMaker
• AWS Deep Learning Containers
• AWS Deep Learning AMIs

Google Cloud Platform

• Cloud Deep Learning VM Image
• Deep Learning Containers

Microsoft Azure

• PyTorch on Azure
• Azure Machine Learning
• Azure Functions

Lightning Studios

• lightning.ai

Ecosystem

BROWSE PROJECTS

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.

Docs

Access comprehensive developer documentation for PyTorch

View Docs ›

Tutorials

Get in-depth tutorials for beginners and advanced developers

View Tutorials ›

Resources

Find development resources and get your questions answered

View Resources ›

Stay in touch for updates, event info, and the latest news

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