Nov 13, 2025 · Distributed Training Scalable distributed training and performance optimization in research and production is enabled by the torch.distributed backend.

CUDA 13.0 ROCm 6.4 CPU pip3 install torch torchvision --index-url https://download.pytorch.org/whl/cu126

Speed up your models with minimal code changes using torch.compile, the latest PyTorch compiler solution.

Extending PyTorch Extending torch.func with autograd.Function Frequently Asked Questions Getting Started on Intel GPU Gradcheck mechanics HIP (ROCm) semantics Features for large …

Apr 23, 2025 · Enable torch.compile on Windows 11 for Intel GPUs, delivering the performance advantages over eager mode as on Linux. Optimize the performance of PyTorch 2 Export Post …

PyTorch is an open source machine learning framework that accelerates the path from research prototyping to production deployment. Built to offer maximum flexibility and speed, PyTorch …

OSX macOS is currently not supported in LTS. Linux and Windows # CUDA 10.2 pip3 install torch==1.8.2 torchvision==0.9.2 torchaudio==0.8.2 --extra-index-url …

PyTorch on XLA Devices PyTorch runs on XLA devices, like TPUs, with the torch_xla package. This document describes how to run your models on these devices.

In PyTorch we can easily define our own autograd operator by defining a subclass of torch.autograd.Function and implementing the forward and backward functions. We can then …

Learn about PyTorch 2.x: faster performance, dynamic shapes, distributed training, and torch.compile.