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  2. 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]

  3. Caffe (software) - Wikipedia

    en.wikipedia.org/wiki/Caffe_(software)

    Caffe supports many different types of deep learning architectures geared towards image classification and image segmentation. It supports CNN, RCNN, LSTM and fully-connected neural network designs. [8] Caffe supports GPU- and CPU-based acceleration computational kernel libraries such as Nvidia cuDNN and Intel MKL. [9] [10]

  4. TensorFlow - Wikipedia

    en.wikipedia.org/wiki/TensorFlow

    While the reference implementation runs on single devices, TensorFlow can run on multiple CPUs and GPUs (with optional CUDA and SYCL extensions for general-purpose computing on graphics processing units). [18] TensorFlow is available on 64-bit Linux, macOS, Windows, and mobile computing platforms including Android and iOS. [citation needed]

  5. mlpack - Wikipedia

    en.wikipedia.org/wiki/Mlpack

    ensmallen [7] is a high quality C++ library for non linear numerical optimizer, it uses Armadillo or bandicoot for linear algebra and it is used by mlpack to provide optimizer for training machine learning algorithms. Similar to mlpack, ensmallen is a header-only library and supports custom behavior using callbacks functions allowing the users ...

  6. SYCL - Wikipedia

    en.wikipedia.org/wiki/SYCL

    Unlike C++ AMP and CUDA, SYCL is a pure C++ eDSL without any C++ extension. This allows for a basic CPU implementation that relies on pure runtime without any specific compiler. [40] Both DPC++ [45] and AdaptiveCpp [46] compilers provide a backend to NVIDIA GPUs, similar to how CUDA does. This allows SYCL code to be compiled and run on NVIDIA ...

  7. Comparison of deep learning software - Wikipedia

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

    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] Yes [25] [26] Yes [25] Yes [25] Yes With Parallel Computing Toolbox [27] Yes Microsoft Cognitive ...

  8. LightGBM - Wikipedia

    en.wikipedia.org/wiki/LightGBM

    LightGBM, short for Light Gradient-Boosting Machine, is a free and open-source distributed gradient-boosting framework for machine learning, originally developed by Microsoft. [4] [5] It is based on decision tree algorithms and used for ranking, classification and other machine learning tasks. The development focus is on performance and ...

  9. OpenCL - Wikipedia

    en.wikipedia.org/wiki/OpenCL

    OpenCL (Open Computing Language) is a framework for writing programs that execute across heterogeneous platforms consisting of central processing units (CPUs), graphics processing units (GPUs), digital signal processors (DSPs), field-programmable gate arrays (FPGAs) and other processors or hardware accelerators.

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