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PyTorch Profiler With TensorBoard#Created On: Apr 20, 2021 | Last Updated: Oct 31, 2024 | Last Verified: Nov 05, 2024 This tutorial demonstrates how to use TensorBoard plugin with PyTorch Profiler to detect performance bottlenecks of the model. Warning The TensorBoard integration with the PyTorch profiler is now deprecated. Instead, use Perfetto or the Chrome trace to view trace.json files. After

Finetuning Torchvision Models#Created On: Jun 01, 2023 | Last Updated: Jun 01, 2023 | Last Verified: Nov 05, 2024 This tutorial has been moved to https://pytorch.org/tutorials/intermediate/torchvision_tutorial.html It will redirect in 3 seconds.

Writing Custom Datasets, DataLoaders and Transforms#Created On: Jun 10, 2017 | Last Updated: Mar 11, 2025 | Last Verified: Nov 05, 2024 Author: Sasank Chilamkurthy A lot of effort in solving any machine learning problem goes into preparing the data. PyTorch provides many tools to make data loading easy and hopefully, to make your code more readable. In this tutorial, we will see how to load and pr

DCGAN Tutorial#Created On: Jul 31, 2018 | Last Updated: Jan 19, 2024 | Last Verified: Nov 05, 2024 Author: Nathan Inkawhich Introduction# This tutorial will give an introduction to DCGANs through an example. We will train a generative adversarial network (GAN) to generate new celebrities after showing it pictures of many real celebrities. Most of the code here is from the DCGAN implementation in p

Reproducibility# Created On: Sep 11, 2018 | Last Updated On: Nov 26, 2024 Completely reproducible results are not guaranteed across PyTorch releases, individual commits, or different platforms. Furthermore, results may not be reproducible between CPU and GPU executions, even when using identical seeds. However, there are some steps you can take to limit the number of sources of nondeterministic be

What is torch.nn really?#Created On: Dec 26, 2018 | Last Updated: Jan 24, 2025 | Last Verified: Nov 05, 2024 Authors: Jeremy Howard, fast.ai. Thanks to Rachel Thomas and Francisco Ingham. We recommend running this tutorial as a notebook, not a script. To download the notebook (.ipynb) file, click the link at the top of the page. PyTorch provides the elegantly designed modules and classes torch.nn

The output of torchvision datasets are PILImage images of range [0, 1]. We transform them to Tensors of normalized range [-1, 1]. Note If you are running this tutorial on Windows or MacOS and encounter a BrokenPipeError or RuntimeError related to multiprocessing, try setting the num_worker of torch.utils.data.DataLoader() to 0. transform = transforms.Compose( [transforms.ToTensor(), transforms.Nor

Deep Learning with PyTorch: A 60 Minute Blitz#Created On: Mar 24, 2017 | Last Updated: May 31, 2023 | Last Verified: Nov 05, 2024 Author: Soumith Chintala What is PyTorch?# 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

Advanced: Making Dynamic Decisions and the Bi-LSTM CRF#Created On: Apr 08, 2017 | Last Updated: Dec 20, 2021 | Last Verified: Nov 05, 2024 Dynamic versus Static Deep Learning Toolkits# Pytorch is a dynamic neural network kit. Another example of a dynamic kit is Dynet (I mention this because working with Pytorch and Dynet is similar. If you see an example in Dynet, it will probably help you impleme

NLP From Scratch: Translation with a Sequence to Sequence Network and Attention#Created On: Mar 24, 2017 | Last Updated: Oct 21, 2024 | Last Verified: Nov 05, 2024 Author: Sean Robertson This tutorials is part of a three-part series: NLP From Scratch: Classifying Names with a Character-Level RNN NLP From Scratch: Generating Names with a Character-Level RNN NLP From Scratch: Translation with a Sequ

Deep Learning for NLP with Pytorch¶ Author: Robert Guthrie This tutorial will walk you through the key ideas of deep learning programming using Pytorch. Many of the concepts (such as the computation graph abstraction and autograd) are not unique to Pytorch and are relevant to any deep learning toolkit out there. I am writing this tutorial to focus specifically on NLP for people who have never writ

Learning PyTorch with Examples#Created On: Mar 24, 2017 | Last Updated: Sep 29, 2025 | Last Verified: Nov 05, 2024 Author: Justin Johnson Note This is one of our older PyTorch tutorials. You can view our latest beginner content in Learn the Basics. This tutorial introduces the fundamental concepts of PyTorch through self-contained examples. At its core, PyTorch provides two main features: An n-dim

torch.nn# Created On: Dec 23, 2016 | Last Updated On: Jul 25, 2025 These are the basic building blocks for graphs:

Welcome to PyTorch Tutorials# What’s new in PyTorch tutorials? 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) Learn the Basics Familiarize yourself with PyTorch concepts and modules. Learn how to load data, build de