Learn how to profile a PyTorch application

Profiling

Learn how to use torch.nn.utils.parametrize to put constraints on your parameters (e.g. make them orthogonal, symmetric positive definite, low-rank...)

Model-Optimization,Best-Practice

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

Learn the usage, debugging and performance profiling for ``torch.compile`` with Inductor CPU backend.

Model-Optimization,inductor

This tutorial explores the new torch.nn.functional.scaled_dot_product_attention and how it can be used to construct Transformer components.

Model-Optimization,Attention,Transformer

Learn how to improve the accuracy of lightweight models using more powerful models as teachers.

Model-Optimization,Image/Video

Get an overview of Channels Last memory format and understand how it is used to order NCHW tensors in memory preserving dimensions.

Memory-Format,Best-Practice,Frontend-APIs

Learn how to use forward-mode automatic differentiation.

Frontend-APIs

Learn how to compute advanced autodiff quantities using torch.func

Frontend-APIs

Learn how to ensemble models using torch.vmap

Frontend-APIs

Learn how to compute per-sample-gradients using torch.func

Frontend-APIs

Learn how to compute neural tangent kernels using torch.func

Frontend-APIs

Walk through an end-to-end example of training a model with the C++ frontend by training a DCGAN – a kind of generative model – to generate images of MNIST digits.

Frontend-APIs,C++

The autograd package helps build flexible and dynamic nerural netorks. In this tutorial, exploreseveral examples of doing autograd in PyTorch C++ frontend

Frontend-APIs,C++