5 days ago · 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

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 …

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

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

The torch package contains data structures for multi-dimensional tensors and defines mathematical operations over these tensors. Additionally, it provides many utilities for efficient …

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

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 …

Oct 17, 2024 · Enhanced Intel GPU backend of torch.compile to improve inference and training performance for a wide range of deep learning workloads. These features are available …

class torch.nn.Linear(in_features, out_features, bias=True, device=None, dtype=None)[source] # Applies an affine linear transformation to the incoming data: y = x A T + b