PyTorch documentation¶
PyTorch is an optimized tensor library for deep learning using GPUs and CPUs.
Notes
- 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
- Reproducibility
- Serialization semantics
- Windows FAQ
Python API
- torch
- torch.nn
- torch.nn.functional
- torch.Tensor
- Tensor Attributes
- Tensor Views
- torch.autograd
- torch.cuda
- torch.cuda.amp
- torch.distributed
- torch.distributions
- torch.hub
- torch.jit
- torch.nn.init
- torch.onnx
- torch.optim
- Quantization
- Distributed RPC Framework
- torch.random
- torch.sparse
- torch.Storage
- torch.utils.bottleneck
- torch.utils.checkpoint
- torch.utils.cpp_extension
- torch.utils.data
- torch.utils.dlpack
- torch.utils.model_zoo
- torch.utils.tensorboard
- Type Info
- Named Tensors
- Named Tensors operator coverage
- torch.__config__
