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- Add pytorch_overview.md - Add pytorch_main_components.md - Reorganize top nav to have Get Started, User Guide, Reference API, Community, Tutorials - Move notes under user guide Pull Request resolved: https://github.com/pytorch/pytorch/pull/159379 Approved by: https://github.com/albanD Co-authored-by: sekyondaMeta <127536312+sekyondaMeta@users.noreply.github.com> Co-authored-by: Nikita Shulga <2453524+malfet@users.noreply.github.com>
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PyTorch Main Components
PyTorch is a flexible and powerful library for deep learning that provides a comprehensive set of tools for building, training, and deploying machine learning models.
PyTorch Components for Basic Deep Learning
Some of the basic PyTorch components include:
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Tensors - N-dimensional arrays that serve as PyTorch's fundamental data structure. They support automatic differentiation, hardware acceleration, and provide a comprehensive API for mathematical operations.
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Autograd - PyTorch's automatic differentiation engine that tracks operations performed on tensors and builds a computational graph dynamically to be able to compute gradients.
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Neural Network API - A modular framework for building neural networks with pre-defined layers, activation functions, and loss functions. The {mod}
nn.Modulebase class provides a clean interface for creating custom network architectures with parameter management. -
DataLoaders - Tools for efficient data handling that provide features like batching, shuffling, and parallel data loading. They abstract away the complexities of data preprocessing and iteration, allowing for optimized training loops.
PyTorch Compiler
The PyTorch compiler is a suite of tools that optimize model execution and reduce resource requirements. You can learn more about the PyTorch compiler here.