PyTorch is an optimized tensor library for deep learning using GPUs and CPUs.
Features described in this documentation are classified by release status:
Stable: These features will be maintained long-term and there should generally be no major performance limitations or gaps in documentation. We also expect to maintain backwards compatibility (although breaking changes can happen and notice will be given one release ahead of time).
Beta: Features are tagged as Beta because the API may change based on user feedback, because the performance needs to improve, or because coverage across operators is not yet complete. For Beta features, we are committing to seeing the feature through to the Stable classification. We are not, however, committing to backwards compatibility.
Prototype: These features are typically not available as part of binary distributions like PyPI or Conda, except sometimes behind run-time flags, and are at an early stage for feedback and testing.
- Automatic Mixed Precision examples
- Autograd mechanics
- Broadcasting semantics
- CPU threading and TorchScript inference
- CUDA semantics
- Distributed Data Parallel
- Extending PyTorch
- Frequently Asked Questions
- Features for large-scale deployments
- Multiprocessing best practices
- Serialization semantics
- Windows FAQ
- Tensor Attributes
- Tensor Views
- Complex Numbers
- DDP Communication Hooks
- Pipeline Parallelism
- Distributed RPC Framework
- Type Info
- Named Tensors
- Named Tensors operator coverage
Indices and tables
© 2019 Torch Contributors
Licensed under the 3-clause BSD License.