Welcome to PyTorch Tutorials — PyTorch Tutorials 2.15.0+cu132 documentation

What’s new in PyTorch tutorials?
Data Loading Optimization in PyTorch
Distributed Training with Ray Train
Serve PyTorch models at scale with Ray Serve
Hyperparameter tuning using Ray Tune
Memory Profiling with Mosaic
Using Variable Length Attention in PyTorch
DebugMode: Recording Dispatched Operations and Numerical Debugging
Familiarize yourself with PyTorch concepts and modules. Learn how to load data, build deep neural networks, train and save your models in this quickstart guide.
Bite-size, ready-to-deploy PyTorch code examples.
A step-by-step guide to building a complete ML workflow with PyTorch.
An introduction to building a complete ML workflow with PyTorch. Follows the PyTorch Beginner Series on YouTube.
This tutorial introduces the fundamental concepts of PyTorch through self-contained examples.
Use torch.nn to create and train a neural network.
Learn to use TensorBoard to visualize data and model training.
Interpretability,Getting-Started,TensorBoard
A guide on best practices to copy data from CPU to GPU.
Optimize DataLoader configuration with num_workers, pin_memory, persistent_workers for maximum training throughput.
Getting-Started,Best-Practice
Learn the subtleties of requires_grad, retain_grad, leaf, and non-leaf tensors
Visualize the gradient flow of a network.
Finetune a pre-trained Mask R-CNN model.
Train a convolutional neural network for image classification using transfer learning.
Train a convolutional neural network for image classification using transfer learning.
Train a generative adversarial network (GAN) to generate new celebrities.
Learn how to augment your network using a visual attention mechanism.
Learn how to train semi-supervised learning algorithms (on custom data) using USB and PyTorch.
Pre-train a transformer language model across multiple GPUs using PyTorch and Ray Train.
Text,Best-Practice,Ray-Distributed,Parallel-and-Distributed-Training
Learn to load data with torchaudio.
Learn to resample audio waveforms using torchaudio.
Learn to apply data augmentations using torchaudio.
Learn to extract features using torchaudio.
Learn to augment features using torchaudio.
Learn to use torchaudio datasets.
Learn how to use torchaudio's pretrained models for building a speech recognition application.
Learn how to correctly format an audio dataset and then train/test an audio classifier network on the dataset.
Learn how to use torchaudio's pretrained models for building a text-to-speech application.
Learn how to use torchaudio's Wav2Vec2 pretrained models for aligning text to speech
Build and train a basic character-level RNN to classify word from scratch without the use of torchtext. First in a series of three tutorials.