# PyTorch

> open source machine learning library for Python, based on Torch

**Wikidata**: [Q47509047](https://www.wikidata.org/wiki/Q47509047)  
**Wikipedia**: [English](https://en.wikipedia.org/wiki/PyTorch)  
**Source**: https://4ort.xyz/entity/pytorch

## Summary
PyTorch is an open-source machine learning library for Python, originally based on Torch. It is widely used for developing and training deep learning models, offering flexibility and ease of use in research and production environments.

## Key Facts
- Open-source machine learning library for Python
- Based on the Torch library
- Uses the NCHW memory format for tensor operations
- Released under the Berkeley Software Distribution (BSD) license
- First version (0.1.7) released on February 2, 2017
- Maintained by the PyTorch team on GitHub
- Supports Linux, macOS, and Microsoft Windows
- Used by projects like AlphaFold and GPT-4
- Competes with TensorFlow in the machine learning framework market
- Available on PyPI as the `torch` package

## FAQs
### Q: What is PyTorch used for?
A: PyTorch is primarily used for developing and training deep learning models, particularly in research and production environments. It supports tasks like computer vision, natural language processing, and reinforcement learning.

### Q: Who developed PyTorch?
A: PyTorch was developed by the PyTorch team, with contributions from various researchers and developers in the machine learning community.

### Q: How does PyTorch compare to TensorFlow?
A: PyTorch and TensorFlow are both popular machine learning frameworks, but PyTorch is often preferred for its dynamic computation graph and Pythonic syntax, while TensorFlow is known for its scalability and production deployment capabilities.

### Q: What programming languages does PyTorch support?
A: PyTorch primarily supports Python, with additional support for CUDA and other programming languages through its ecosystem.

### Q: Is PyTorch free to use?
A: Yes, PyTorch is open-source and free to use under the BSD license, making it accessible for both commercial and academic purposes.

## Why It Matters
PyTorch has significantly impacted the field of machine learning by providing researchers and developers with a flexible and intuitive framework for building and training deep learning models. Its dynamic computation graph allows for easier debugging and rapid prototyping, making it a preferred choice for many in the research community. PyTorch's integration with Python and support for GPU acceleration have also contributed to its widespread adoption. The framework's open-source nature has fostered a vibrant community, with contributions from developers worldwide. PyTorch's use in projects like AlphaFold and GPT-4 underscores its importance in advancing state-of-the-art machine learning applications.

## Notable For
- Used by AlphaFold for protein structure prediction
- Built into GPT-4 for multimodal large language model development
- Supports dynamic computation graphs for easier debugging
- Offers Pythonic syntax and flexibility in model development
- Competes directly with TensorFlow in the machine learning framework market
- Maintained by a large open-source community

## Body
### Overview
PyTorch is an open-source machine learning library for Python, originally based on the Torch library. It was developed to provide researchers and developers with a flexible and intuitive framework for building and training deep learning models. PyTorch's dynamic computation graph allows for easier debugging and rapid prototyping, making it a popular choice in the research community.

### History and Development
PyTorch was first released on February 2, 2017, with version 0.1.7. The project has since seen numerous updates, with the latest versions incorporating new features and improvements. The PyTorch team maintains the project on GitHub, with contributions from developers worldwide.

### Features and Capabilities
PyTorch supports a variety of machine learning tasks, including computer vision, natural language processing, and reinforcement learning. It offers dynamic computation graphs, which allow for easier debugging and rapid prototyping. PyTorch also supports GPU acceleration through CUDA, making it suitable for large-scale training tasks.

### Licensing and Community
PyTorch is released under the Berkeley Software Distribution (BSD) license, making it free to use for both commercial and academic purposes. The project has a vibrant open-source community, with contributions from developers worldwide. PyTorch is used by a variety of projects, including AlphaFold and GPT-4, underscoring its importance in the field of machine learning.

### Technical Details
PyTorch uses the NCHW memory format for tensor operations, which is optimized for performance on modern hardware. The framework is available on PyPI as the `torch` package, making it easy to install and integrate into existing Python projects. PyTorch supports Linux, macOS, and Microsoft Windows, ensuring broad compatibility.

### Competitors and Alternatives
PyTorch competes with TensorFlow in the machine learning framework market. While TensorFlow is known for its scalability and production deployment capabilities, PyTorch is often preferred for its dynamic computation graph and Pythonic syntax. Both frameworks have their strengths and are widely used in the industry.

## Schema Markup
```json
{
  "@context": "https://schema.org",
  "@type": "SoftwareApplication",
  "name": "PyTorch",
  "description": "Open-source machine learning library for Python, based on Torch",
  "url": "https://pytorch.org",
  "sameAs": ["https://www.wikidata.org/wiki/Q206855", "https://en.wikipedia.org/wiki/PyTorch"],
  "applicationCategory": "Machine Learning Framework",
  "operatingSystem": ["Linux", "macOS", "Microsoft Windows"],
  "license": "BSD",
  "datePublished": "2016-08-24",
  "softwareVersion": "0.4.0",
  "programmingLanguage": ["Python", "CUDA"],
  "creator": "PyTorch Team",
  "codeRepository": "https://github.com/pytorch/pytorch"
}

## References

1. [Source](https://github.com/pytorch/pytorch/releases)
2. [2022](https://www.theregister.com/2022/09/12/pytorch_meta_linux_foundation/)
3. [Release 0.4.0. 2018](https://github.com/pytorch/pytorch/releases/tag/v0.4.0)
4. [Release 0.3.1. 2018](https://github.com/pytorch/pytorch/releases/tag/v0.3.1)
5. [Release 0.3.0. 2017](https://github.com/pytorch/pytorch/releases/tag/v0.3.0)
6. [Release 0.2.0. 2017](https://github.com/pytorch/pytorch/releases/tag/v0.2.0)
7. [Release 0.1.12. 2017](https://github.com/pytorch/pytorch/releases/tag/v0.1.12)
8. [Release 0.1.11. 2017](https://github.com/pytorch/pytorch/releases/tag/v0.1.11)
9. [Release 0.1.10. 2017](https://github.com/pytorch/pytorch/releases/tag/v0.1.10)
10. [Release 0.1.9. 2017](https://github.com/pytorch/pytorch/releases/tag/v0.1.9)
11. [Release 0.1.8. 2017](https://github.com/pytorch/pytorch/releases/tag/v0.1.8)
12. [Release 0.1.7. 2017](https://github.com/pytorch/pytorch/releases/tag/v0.1.7)
13. [Release 0.4.1. 2018](https://github.com/pytorch/pytorch/releases/tag/v0.4.1)
14. [Release 1.0.0. 2018](https://github.com/pytorch/pytorch/releases/tag/v1.0.0)
15. [Release 1.0.1. 2019](https://github.com/pytorch/pytorch/releases/tag/v1.0.1)
16. [Release 1.1.0. 2019](https://github.com/pytorch/pytorch/releases/tag/v1.1.0)
17. [Release 1.2.0. 2019](https://github.com/pytorch/pytorch/releases/tag/v1.2.0)
18. [Release 1.3.0. 2019](https://github.com/pytorch/pytorch/releases/tag/v1.3.0)
19. [Release 1.3.1. 2019](https://github.com/pytorch/pytorch/releases/tag/v1.3.1)
20. [Release 1.4.0. 2020](https://github.com/pytorch/pytorch/releases/tag/v1.4.0)
21. [Release 1.5.0. 2020](https://github.com/pytorch/pytorch/releases/tag/v1.5.0)
22. [Release 1.5.1. 2020](https://github.com/pytorch/pytorch/releases/tag/v1.5.1)
23. [Release 1.6.0. 2020](https://github.com/pytorch/pytorch/releases/tag/v1.6.0)
24. [Release 1.7.0. 2020](https://github.com/pytorch/pytorch/releases/tag/v1.7.0)
25. [Release 1.7.1. 2020](https://github.com/pytorch/pytorch/releases/tag/v1.7.1)
26. [PyTorch 1.8 Release, including Compiler and Distributed Training updates, New Mobile Tutorials and more. 2021](https://github.com/pytorch/pytorch/releases/tag/v1.8.0)
27. [Release 1.8.1. 2021](https://github.com/pytorch/pytorch/releases/tag/v1.8.1)
28. [Source](https://pytorch.org/blog/pytorch-1.9-released/)
29. [Release 1.9.0. 2021](https://github.com/pytorch/pytorch/releases/tag/v1.9.0)
30. [New Library Releases in PyTorch 1.10. 2021](https://pytorch.org/blog/pytorch-1.10-new-library-releases/)
31. [PyTorch 1.10.1 Release, small bug fix release. 2021](https://github.com/pytorch/pytorch/releases/tag/v1.10.1)
32. [PyTorch 1.10.2 Release, small bug fix release](https://github.com/pytorch/pytorch/releases/tag/v1.10.2)
33. [PyTorch 1.11, TorchData, and functorch are now available](https://github.com/pytorch/pytorch/releases/tag/v1.11.0)
34. [PyTorch 1.12: TorchArrow, Functional API for Modules and nvFuser, are now available](https://github.com/pytorch/pytorch/releases/tag/v1.12.0)
35. [PyTorch 1.12.1 Release, small bug fix release. 2022](https://github.com/pytorch/pytorch/releases/tag/v1.12.1)
36. [Release 1.8.2. 2021](https://github.com/pytorch/pytorch/releases/tag/v1.8.2)
37. [Release 1.9.1. 2021](https://github.com/pytorch/pytorch/releases/tag/v1.9.1)
38. [Source](https://github.com/pytorch/pytorch/releases/tag/v1.13.0)
39. [Release 1.13.1. 2022](https://github.com/pytorch/pytorch/releases/tag/v1.13.1)
40. [Release 2.0.0. 2023](https://github.com/pytorch/pytorch/releases/tag/v2.0.0)
41. [Release 2.0.1. 2023](https://github.com/pytorch/pytorch/releases/tag/v2.0.1)
42. [Release 2.1.0. 2023](https://github.com/pytorch/pytorch/releases/tag/v2.1.0)
43. [Release 2.1.1. 2023](https://github.com/pytorch/pytorch/releases/tag/v2.1.1)
44. [Release 2.1.2. 2023](https://github.com/pytorch/pytorch/releases/tag/v2.1.2)
45. [Release 2.2.0. 2024](https://github.com/pytorch/pytorch/releases/tag/v2.2.0)
46. [Release 2.2.1. 2024](https://github.com/pytorch/pytorch/releases/tag/v2.2.1)
47. [Release 2.2.2. 2024](https://github.com/pytorch/pytorch/releases/tag/v2.2.2)
48. [Release 2.3.0. 2024](https://github.com/pytorch/pytorch/releases/tag/v2.3.0)
49. [Release 2.3.1. 2024](https://github.com/pytorch/pytorch/releases/tag/v2.3.1)
50. [Release 2.4.0. 2024](https://github.com/pytorch/pytorch/releases/tag/v2.4.0)