Deep Dive Pytorch Tutorials 2 9 0 Cu128 Documentation

Focused on enhancing model performance, this section includes tutorials on profiling, hyperparameter tuning, quantization, and other techniques to optimize PyTorch models for better efficiency and speed. Learn how to profile a PyTorch application Learn how to use torch.nn.utils.parametrize to put constraints on your parameters (e.g. make them orthogonal, symmetric positive definite, low-rank...) 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. Learn the usage, debugging and performance profiling for ``torch.compile`` with Inductor CPU backend.

Integrating Custom Operators with SYCL for Intel GPU Supporting Custom C++ Classes in torch.compile/torch.export Accelerating torch.save and torch.load with GPUDirect Storage Getting Started with Fully Sharded Data Parallel (FSDP2) Interactive Distributed Applications with Monarch Go to the end to download the full example code.

Created On: Apr 08, 2017 | Last Updated: Apr 24, 2018 | Last Verified: Nov 05, 2024 Deep learning consists of composing linearities with non-linearities in clever ways. The introduction of non-linearities allows for powerful models. In this section, we will play with these core components, make up an objective function, and see how the model is trained. One of the core workhorses of deep learning is the affine map, which is a function \(f(x)\) where for a matrix \(A\) and vectors \(x, b\).

The parameters to be learned here are \(A\) and \(b\). Often, \(b\) is refered to as the bias term. This is a collection of beginner-friendly resources to help you get started with PyTorch. These tutorials cover fundamental concepts, basic operations, and essential workflows to build a solid foundation for your deep learning journey. Perfect for newcomers looking to understand PyTorch’s core functionality through step-by-step guidance. A step-by-step interactive series for those just starting out with PyTorch.

Learn the fundamentals of PyTorch through our video series, perfect for those who prefer learning from videos. Quickly grasp the basics of PyTorch with these bite-sized foundational recipes. Go to the end to download the full example code. Learn the Basics || Quickstart || Tensors || Datasets & DataLoaders || Transforms || Build Model || Autograd || Optimization || Save & Load Model Created On: Feb 09, 2021 | Last Updated: Jan 24, 2025 | Last Verified: Not Verified This section runs through the API for common tasks in machine learning.

Refer to the links in each section to dive deeper. PyTorch has two primitives to work with data: torch.utils.data.DataLoader and torch.utils.data.Dataset. Dataset stores the samples and their corresponding labels, and DataLoader wraps an iterable around the Dataset. Go to the end to download the full example code. Created On: Aug 22, 2023 | Last Updated: Jan 24, 2025 | Last Verified: Nov 05, 2024 Knowledge distillation is a technique that enables knowledge transfer from large, computationally expensive models to smaller ones without losing validity.

This allows for deployment on less powerful hardware, making evaluation faster and more efficient. In this tutorial, we will run a number of experiments focused at improving the accuracy of a lightweight neural network, using a more powerful network as a teacher. The computational cost and the speed of the lightweight network will remain unaffected, our intervention only focuses on its weights, not on its forward pass. Applications of this technology can be found in devices such as drones or mobile phones. In this tutorial, we do not use any external packages as everything we need is available in torch and torchvision. How to modify model classes to extract hidden representations and use them for further calculations

Go to the end to download the full example code. Learn the Basics || Quickstart || Tensors || Datasets & DataLoaders || Transforms || Build Model || Autograd || Optimization || Save & Load Model Created On: Feb 09, 2021 | Last Updated: Jul 07, 2025 | Last Verified: Nov 05, 2024 Authors: Suraj Subramanian, Seth Juarez, Cassie Breviu, Dmitry Soshnikov, Ari Bornstein Most machine learning workflows involve working with data, creating models, optimizing model parameters, and saving the trained models. This tutorial introduces you to a complete ML workflow implemented in PyTorch, with links to learn more about each of these concepts.

Created On: Mar 24, 2017 | Last Updated: May 31, 2023 | Last Verified: Nov 05, 2024 PyTorch is a Python-based scientific computing package serving two broad purposes: A replacement for NumPy to use the power of GPUs and other accelerators. An automatic differentiation library that is useful to implement neural networks. Understand PyTorch’s Tensor library and neural networks at a high level. All the tutorials are now presented as sphinx style documentation at:

If you have a question about a tutorial, post in https://dev-discuss.pytorch.org/ rather than creating an issue in this repo. Your question will be answered much faster on the dev-discuss forum. You can submit the following types of issues: We use sphinx-gallery's notebook styled examples to create the tutorials. Syntax is very simple. In essence, you write a slightly well formatted Python file and it shows up as an HTML page.

In addition, a Jupyter notebook is autogenerated and available to run in Google Colab. Here is how you can create a new tutorial (for a detailed description, see CONTRIBUTING.md): Hi folks, I spent most of today trying to get vLLM running with PyTorch 2.9.0 and it looks like the most recent build takes care of a lot of errors. There are so many ways to get this wrong and I’m amazed it worked at all. I think I hit every issue on this forum to get to this point. I hope it helps anyone else working on the same issue to get things running.

The successful installation used PyTorch 2.9 nightly + vLLM source build: This is a September 1, 2025 development build compiled from source with PyTorch 2.9.0.dev20250831+cu128. The successful build required specific Blackwell-targeting variables:

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