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. Stable represents the most currently tested and supported version of PyTorch 1.1. This should be suitable for many users. Preview is available if you want the latest, not fully tested and supported, 1.1 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++.
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.