Featuring Python 3.13 support for torch.compile, several AOTInductor enhancements, FP16 support on X86 CPUs, and more. Learn more

import torch torch. cuda. is_available () Building from source For the majority of PyTorch users, installing from a pre-built binary via a package manager will provide the best experience.

The context managers torch.no_grad(), torch.enable_grad(), and torch.set_grad_enabled() are helpful for locally disabling and enabling gradient computation. See Locally disabling gradient computation for more details on their usage.

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 …

Learn how to use torch.nn.utils.prune to sparsify your neural networks, and how to extend it to implement your own custom pruning technique. Model-Optimization,Best-Practice

What is torch.nn really? NLP from Scratch; Visualizing Models, Data, and Training with TensorBoard; A guide on good usage of non_blocking and pin_memory() in PyTorch; Image and Video. TorchVision Object Detection Finetuning Tutorial; Transfer Learning for Computer Vision Tutorial; Adversarial Example Generation; DCGAN Tutorial; Spatial ...

Torch provides many in the torch.optim package, and they are all completely transparent. Using the simplest gradient update is the same as the more complicated algorithms. Trying different update algorithms and different parameters for the update algorithms (like different initial learning rates) is important in optimizing your network’s ...

2024年10月17日 · This API leverages torch.compile to generate a fused FlashAttention kernel, which eliminates extra memory allocation and achieves performance comparable to handwritten implementations. Additionally, we automatically generate the backwards pass using PyTorch’s autograd machinery.

torch.compile is a fully additive (and optional) feature and hence 2.0 is 100% backward compatible by definition. Underpinning torch.compile are new technologies – TorchDynamo, AOTAutograd, PrimTorch and TorchInductor.

2025年1月29日 · This release features multiple improvements for PT2: torch.compile can now be used with Python 3.13; new performance-related knob torch.compiler.set_stance; several AOTInductor enhancements. Besides the PT2 improvements, another highlight is …