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  2. TensorFlow - Wikipedia

    en.wikipedia.org/wiki/TensorFlow

    In January 2019, the TensorFlow team released a developer preview of the mobile GPU inference engine with OpenGL ES 3.1 Compute Shaders on Android devices and Metal Compute Shaders on iOS devices. [30] In May 2019, Google announced that their TensorFlow Lite Micro (also known as TensorFlow Lite for Microcontrollers) and ARM's uTensor would be ...

  3. CUDA - Wikipedia

    en.wikipedia.org/wiki/CUDA

    In computing, CUDA is a proprietary [2] parallel computing platform and application programming interface (API) that allows software to use certain types of graphics processing units (GPUs) for accelerated general-purpose processing, an approach called general-purpose computing on GPUs.

  4. Pop!_OS - Wikipedia

    en.wikipedia.org/wiki/Pop!_OS

    Pop OS (stylized as Pop!_OS) is a free and open-source Linux distribution, based on Ubuntu, and featuring a customized GNOME desktop environment known as COSMIC.The distribution is developed by American Linux computer manufacturer System76.

  5. General-purpose computing on graphics processing units

    en.wikipedia.org/wiki/General-purpose_computing...

    This representation does have certain limitations. Given sufficient graphics processing power even graphics programmers would like to use better formats, such as floating point data formats, to obtain effects such as high-dynamic-range imaging. Many GPGPU applications require floating point accuracy, which came with video cards conforming to ...

  6. CuPy - Wikipedia

    en.wikipedia.org/wiki/CuPy

    CuPy is an open source library for GPU-accelerated computing with Python programming language, providing support for multi-dimensional arrays, sparse matrices, and a variety of numerical algorithms implemented on top of them. [3] CuPy shares the same API set as NumPy and SciPy, allowing it to be a drop-in replacement to run NumPy/SciPy code on GPU.

  7. DeepSpeed - Wikipedia

    en.wikipedia.org/wiki/DeepSpeed

    Features include mixed precision training, single-GPU, multi-GPU, and multi-node training as well as custom model parallelism. The DeepSpeed source code is licensed under MIT License and available on GitHub. [5] The team claimed to achieve up to a 6.2x throughput improvement, 2.8x faster convergence, and 4.6x less communication. [6]

  8. Windows Subsystem for Linux - Wikipedia

    en.wikipedia.org/wiki/Windows_Subsystem_for_Linux

    However, with the introduction of Windows 11, WSLg is finally shipping with a production build of Windows, bringing support for both graphics and audio in WSL apps. [34] FreeRDP is used to encode all communications going from the Remote Desktop Protocol (RDP) Server (in Weston) to the RDP Client ( msrdc on Windows [ 35 ] ) according to the RDP ...

  9. Comparison of deep learning software - Wikipedia

    en.wikipedia.org/wiki/Comparison_of_deep...

    Can use Theano, Tensorflow or PlaidML as backends Yes No Yes Yes [20] Yes Yes No [21] Yes [22] Yes MATLAB + Deep Learning Toolbox (formally Neural Network Toolbox) MathWorks: 1992 Proprietary: No Linux, macOS, Windows: C, C++, Java, MATLAB: MATLAB: No No Train with Parallel Computing Toolbox and generate CUDA code with GPU Coder [23] No Yes [24 ...