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Learn how to use PyTorch, an optimized tensor library for deep learning using GPUs and CPUs. Browse the features, modules, language bindings, and tutorials for PyTorch 2.4.
- PyTorch Governance | Build + CI
PyTorch Governance | Build + CI - PyTorch documentation —...
- PyTorch Contribution Guide
The PyTorch organization is governed by PyTorch Governance...
- PyTorch Design Philosophy
One thing PyTorch has needed to deal with over the years is...
- PyTorch Governance | Mechanics
Summary¶. PyTorch adopts a technical governance structure...
- PyTorch Governance | Maintainers
PyTorch Governance | Maintainers - PyTorch documentation —...
- CUDA Automatic Mixed Precision examples
Automatic Mixed Precision examples¶. Ordinarily, “automatic...
- Autograd mechanics
Under the hood, to prevent reference cycles, PyTorch has...
- Broadcasting semantics
Broadcasting semantics - PyTorch documentation — PyTorch 2.5...
- PyTorch Governance | Build + CI
Run PyTorch locally or get started quickly with one of the supported cloud platforms. Tutorials. Whats new in PyTorch tutorials. Learn the Basics. Familiarize yourself with PyTorch concepts and modules. PyTorch Recipes. Bite-size, ready-to-deploy PyTorch code examples. Intro to PyTorch - YouTube Series
PyTorch Tutorials provides bite-size, ready-to-deploy PyTorch code examples and a step-by-step guide to building a complete ML workflow with PyTorch. Explore topics such as data loading, neural networks, parallel and distributed training, TensorBoard, and more.
To create a tensor with pre-existing data, use torch.tensor(). To create a tensor with specific size, use torch.* tensor creation ops (see Creation Ops). To create a tensor with the same size (and similar types) as another tensor, use torch.*_like tensor creation ops (see Creation Ops).
torch. Shortcuts. torch ¶. The torch package contains data structures for multi-dimensional tensors and defines mathematical operations over these tensors. Additionally, it provides many utilities for efficient serialization of Tensors and arbitrary types, and other useful utilities.
This tutorial introduces you to a complete ML workflow implemented in PyTorch, using the FashionMNIST dataset to train a neural network. You can run the tutorial in the cloud or locally, and learn about tensors, datasets, transforms, models, optimization, and more.
PyTorch Documentation . Pick a version. main (unstable) v2.5.0 (release candidate) v2.4.0 (stable) v2.3.0