Audience
Developers looking for a scientific computing framework for their neural networks and energy-based models
About Torch
Torch is a scientific computing framework with wide support for machine learning algorithms that puts GPUs first. It is easy to use and efficient, thanks to an easy and fast scripting language, LuaJIT, and an underlying C/CUDA implementation. The goal of Torch is to have maximum flexibility and speed in building your scientific algorithms while making the process extremely simple. Torch comes with a large ecosystem of community-driven packages in machine learning, computer vision, signal processing, parallel processing, image, video, audio and networking among others, and builds on top of the Lua community. At the heart of Torch are the popular neural network and optimization libraries which are simple to use, while having maximum flexibility in implementing complex neural network topologies. You can build arbitrary graphs of neural networks, and parallelize them over CPUs and GPUs in an efficient manner.
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PyTorch
Transition seamlessly between eager and graph modes with TorchScript, and accelerate the path to production with TorchServe. Scalable distributed training and performance optimization in research and production is enabled by the torch-distributed backend. A rich ecosystem of tools and libraries extends PyTorch and supports development in computer vision, NLP and more. PyTorch is well supported on major cloud platforms, providing frictionless development and easy scaling. Select your preferences and run the install command. Stable represents the most currently tested and supported version of PyTorch. This should be suitable for many users. Preview is available if you want the latest, not fully tested and supported, 1.10 builds that are generated nightly. Please ensure that you have met the prerequisites (e.g., numpy), depending on your package manager. Anaconda is our recommended package manager since it installs all dependencies.
Learn more about PyTorch
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SHARK
SHARK is a fast, modular, feature-rich open-source C++ machine learning library. It provides methods for linear and nonlinear optimization, kernel-based learning algorithms, neural networks, and various other machine learning techniques. It serves as a powerful toolbox for real-world applications as well as research. Shark depends on Boost and CMake. It is compatible with Windows, Solaris, MacOS X, and Linux. Shark is licensed under the permissive GNU Lesser General Public License. Shark provides an excellent trade-off between flexibility and ease-of-use on the one hand, and computational efficiency on the other. Shark offers numerous algorithms from various machine learning and computational intelligence domains in a way that they can be easily combined and extended. Shark comes with a lot of powerful algorithms that are to our best knowledge not implemented in any other library.
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Company Information
Torch
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Product Details
Platforms Supported
Cloud
iPhone
iPad
Android
Training
Documentation
Videos
Support
Online
Torch Frequently Asked Questions
Q: What kinds of users and organization types does Torch work with?
Torch works with these users and organization types: Mid Size Business, Small Business, Enterprise, Freelance, Nonprofit, Government, and Startup.
Q: What languages does Torch support in their product?
Torch supports these languages: English.
Q: What kind of support options does Torch offer?
Torch offers support via online.
Q: What other applications or services does Torch integrate with?
Torch integrates with: Hetman Internet Spy and LeaderGPU.
Q: Does Torch have a mobile app?
Yes, Torch has a mobile app for iPhone, Android, and iPad.
Q: What type of training does Torch provide?
Torch provides training in the form of documentation and videos.
