Domain error
PyTorch is an open-source machine learning library based on the Torch library, used for applications such as computer vision and natural language processing, primarily developed by Facebook's AI Research lab. It is known for its flexibility, ease of use, and dynamic computational graph that allows for easy modification of the network architecture on the fly.
1.2M
40000 / day
1.5M
50000 / day
5.5 min
3.5 pages per visit
40%
-
Domain Rating
-
Domain Authority
-
Citation Level
2016
English, etc
Website Key Features
Dynamic Computational Graphs
Allows for the modification of the network architecture on the fly, making it highly flexible for research and development.
Tensor Computing
Provides strong acceleration via GPUs with a simple to use API for tensor computations.
Deep Neural Networks
Built-in support for building and training deep neural networks with a rich library of layers and loss functions.
Autograd System
Automatically differentiates native Python and NumPy code, simplifying the process of computing gradients.
TorchScript
Enables the creation of serializable and optimizable models from PyTorch code, allowing for easy deployment.
Extensive Libraries and Tools
Comes with a wide range of libraries and tools for tasks such as computer vision, natural language processing, and more.
Community and Ecosystem
Boasts a large and active community, contributing to a rich ecosystem of tools, libraries, and resources.
Cross-Platform
Supports running on multiple platforms, including Linux, macOS, and Windows.
Cloud Support
Integrates with major cloud platforms to provide scalable machine learning solutions.
Research to Production
Facilitates a seamless transition from research prototyping to production deployment.
Similar Sites and Competitors
Additional information
License
PyTorch is released under the modified BSD license.
Developers
Primarily developed by Facebook's AI Research lab (FAIR), with contributions from a large community.
Documentation
Extensive documentation and tutorials are available to help users get started and master PyTorch.
Community Support
A vibrant community provides support through forums, GitHub, and social media platforms.
Educational Resources
Offers a variety of educational resources, including courses, workshops, and conferences.
Integration with Other Libraries
Easily integrates with other Python libraries and frameworks, such as NumPy, SciPy, and Pandas.
Performance
Optimized for performance, with support for CUDA and other acceleration technologies.
HTTP headers
Security headers report is a very important part of user data protection. Learn more about http headers for pytorch.org