# PyTorch Release

PyTorch is an open-source deep learning library originally developed by Meta Platforms, now under the Linux Foundation. It provides a high-level API for building and training neural networks, with automatic differentiation and GPU acceleration, and remains one of the most popular frameworks as of 2025.

PyTorch is an open-source [deep-learning](https://www.wikiprompt.org/wiki/deep-learning) library originally developed by Meta Platforms and currently supported by the Linux Foundation. It is the successor to Torch, providing a high-level API built on optimized, low-level implementations of algorithms and architectures such as the [transformer](https://www.wikiprompt.org/wiki/transformer) and [SGD variants](https://www.wikiprompt.org/wiki/sgd-variants). The library simplifies model training and inference to a few lines of code, allows automatic parallelization, and implements CUDA bindings to leverage GPU resources. As of 2025, it remains one of the most popular deep learning libraries, alongside TensorFlow and Keras, and underpins commercial systems including ChatGPT, Tesla Autopilot, Uber's Pyro, and Hugging Face's Transformers.

PyTorch uses the tensor as a fundamental data type, similar to NumPy arrays, but with support for GPU operations. Training is facilitated by Autograd, a reversed automatic differentiation system that constructs a directed acyclic graph of operations during the forward pass, enabling backpropagation with a loss function. The library can be installed via Anaconda package managers and is widely adopted in both research and industry.

## History and Development

The original Torch library was released in 2001 by the Idiap Research Institute under a GPL license, written in C++ and CUDA, supporting methods like [neural networks](https://www.wikiprompt.org/wiki/neural-network) and support vector machines. Around 2010, it was rewritten by Ronan Collobert, Clement Farabet, and Koray Kavukcuoglu as Torch7 or LuaTorch, with a C backend and Lua frontend. In mid-2016, developers refactored it to decouple frontend and backend, influenced by torch-autograd and Chainer, which in turn drew from HIPS/autograd. Development on Torch7 ceased in 2018, subsumed by the PyTorch project.

Meta also operated Caffe2, but models from the two frameworks were incompatible. In September 2017, Meta and Microsoft created the Open Neural Network Exchange (ONNX) project to decouple frameworks from hardware-specific runtimes, enabling model conversion and optimization for execution providers like NVIDIA's TensorRT. Caffe2 was merged into PyTorch at the end of March 2018. In September 2022, Meta announced that PyTorch would be governed by the independent PyTorch Foundation, a subsidiary of the Linux Foundation. PyTorch 2.0 was released on 15 March 2023, introducing TorchDynamo, a Python-level compiler that speeds up code by up to two times, with improvements across major cloud platforms.

## Tensor Operations

PyTorch defines the Tensor class (torch.Tensor) to store and operate on homogeneous multidimensional rectangular arrays of numbers. Tensors are similar to NumPy arrays but can be operated on by CUDA-capable NVIDIA GPUs. The library has also developed support for other GPU platforms, including AMD's ROCm and Apple's Metal Framework. This flexibility allows for efficient computation across diverse hardware environments.

## Neural Network Module

The torch.nn module provides a comprehensive collection of building blocks for neural networks, including layers and activation functions. Networks are constructed by inheriting from this module and defining the sequence of operations in the forward() function. This design supports complex model architectures, from simple linear layers to advanced structures like [residual networks](https://www.wikiprompt.org/wiki/residual-network) and [U-Nets](https://www.wikiprompt.org/wiki/u-net), facilitating research and deployment.

## Serialization and File Format

PyTorch can save and load models using its own file format, a ZIP64 archive containing model weights in a Python pickle file, along with metadata such as byte order. Common file extensions are .pt and .pth. This serialization enables model persistence and sharing, essential for production deployment and collaborative development.

## Example Usage

The following program demonstrates the low-level functionality of the library. A simple neural network with linear layers is defined using the nn module, showcasing the ease of building and training models. This example highlights the core workflow: defining a network, specifying a loss function, and performing backpropagation with an optimizer like [Adam](https://www.wikiprompt.org/wiki/adam-optimizer).

## See Also

Related topics include comparison of deep learning software, differentiable programming, DeepSpeed, open-source artificial intelligence, and PyTorch Lightning. These resources provide broader context on the ecosystem and alternatives.

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Source: https://www.wikiprompt.org/wiki/pytorch-release-2016
License: CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0/)
Last updated: 2026-09-09T02:01:32.31758+00:00