PyTorch
Product information, use cases, and access for PyTorch.
Pricing information
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What is PyTorch?
PyTorch is an open-source deep learning framework and ecosystem supported by the PyTorch Foundation and its community. It is built for machine learning research, model development, and production-oriented applications, with entry points for local installation and supported cloud platforms. The product is relevant to developers and researchers who need to build, train, optimize, or move deep learning work toward deployment.
Its workflow supports both eager and graph modes, giving users room to explore models while providing paths toward production use. PyTorch also combines distributed-training capabilities, cloud support, documentation, tutorials, and an ecosystem of related libraries for areas such as computer vision and natural language processing. Users can select development options involving Python, C++, or Java and configure the installation for CPU, CUDA, or ROCm computing environments.
Key features of PyTorch
Flexible development modes
PyTorch supports both eager mode and graph mode, allowing developers to explore models flexibly and then move toward an execution path suited to production needs.
Distributed training
The torch.distributed backend provides distributed training and performance-optimization capabilities for research and production work, supporting teams that train machine learning models at larger computational scale.
Broader ecosystem
PyTorch is accompanied by a broad ecosystem of tools and libraries that extends development into areas such as computer vision and natural language processing, with documentation, tutorials, and community resources for learning.
Platform and cloud support
The official installation flow lets users choose an operating system, package, language, and compute platform, while supported cloud-platform options provide additional ways to start PyTorch.