Key Features &
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A new hybrid front-end seamlessly transitions between eager mode and graph mode to provide both flexibility and speed.
Scalable distributed training and performance optimization in research and production is enabled by the torch.distributed backend.
Deep integration into Python allows popular libraries and packages to be used for easily writing neural network layers in Python.
Tools & Libraries
A rich ecosystem of tools and libraries extends PyTorch and supports development in computer vision, NLP and more.
Select your preferences and run the install command. Please ensure that you are on the latest pip and numpy packages. Anaconda is our recommended package manager. You can also install previous versions of PyTorch.
Previous versions of PyTorch
EcosystemSee all Projects
Explore a rich ecosystem of libraries, tools, and more to support development.
Join the PyTorch developer community to contribute, learn, and get your questions answered.