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PyTorch 2.2

PyTorch 2.2 offers ~2x performance improvement.

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Key Features &

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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. Anaconda is our recommended package manager since it installs all dependencies. You can also install previous versions of PyTorch. Note that LibTorch is only available for C++.

NOTE: Latest PyTorch requires Python 3.8 or later. For more details, see Python section below.

PyTorch Build
Your OS
Compute Platform
Run this Command:
PyTorch Build
Stable (1.13.0)
Preview (Nightly)
Your OS
C++ / Java
Compute Platform
CUDA 11.6
CUDA 11.7
ROCm 5.2
Run this Command:
conda install pytorch torchvision -c pytorch

Previous versions of PyTorch

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Using PyTorch

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

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

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

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Access comprehensive developer documentation for PyTorch

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Get in-depth tutorials for beginners and advanced developers

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Find development resources and get your questions answered

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