Gabriel Coronado
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Explore a rich ecosystem of libraries, tools, and more to support development. Captum (“comprehension” in Latin) is an open source, extensible library for model interpretability built on PyTorch. PyTorch Geometric is a library for deep learning on irregular input data such as graphs, point clouds, and manifolds.
Jun 5, 2024 · PyTorch is a Python package that provides two high-level features: Tensor computation (like NumPy) with strong GPU acceleration. Deep neural networks built on a tape-based autograd system. You can reuse your favorite Python packages such as NumPy, SciPy, and Cython to extend PyTorch when needed.
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Installing on Windows. PyTorch can be installed and used on various Windows distributions. Depending on your system and compute requirements, your experience with PyTorch on Windows may vary in terms of processing time.
PyTorch is a Python package that provides two high-level features: Tensor computation (like NumPy) with strong GPU acceleration. Deep neural networks built on a tape-based autograd system. You can reuse your favorite Python packages such as NumPy, SciPy, and Cython to extend PyTorch when needed.
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.
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. Get started with PyTorch.
PyTorch is an optimized tensor library for deep learning using GPUs and CPUs. Features described in this documentation are classified by release status: Stable: These features will be maintained long-term and there should generally be no major performance limitations or gaps in documentation.
PyTorch is a software-based open source deep learning framework to build neural networks, pairing the Torch machine learning library with a Python-based API.
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.
