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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. AlexNet - Wikipedia

    en.wikipedia.org/wiki/AlexNet

    AlexNet is highly influential, resulting in much subsequent work in using CNNs for computer vision and using GPUs to accelerate deep learning. As of mid 2024, the AlexNet paper has been cited over 157,000 times according to Google Scholar.

  4. 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]

  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. Convolutional neural network - Wikipedia

    en.wikipedia.org/wiki/Convolutional_neural_network

    Created by the Berkeley Vision and Learning Center (BVLC). It supports both CPU and GPU. Developed in C++, and has Python and MATLAB wrappers. Deeplearning4j: Deep learning in Java and Scala on multi-GPU-enabled Spark. A general-purpose deep learning library for the JVM production stack running on a C++ scientific computing engine.

  7. CUDA - Wikipedia

    en.wikipedia.org/wiki/CUDA

    [27] [1] Attempts to implement CUDA on other GPUs include: Project Coriander: Converts CUDA C++11 source to OpenCL 1.2 C. A fork of CUDA-on-CL intended to run TensorFlow. [28] [29] [30] CU2CL: Convert CUDA 3.2 C++ to OpenCL C. [31] GPUOpen HIP: A thin abstraction layer on top of CUDA and ROCm intended for AMD and Nvidia GPUs. Has a conversion ...

  8. Nvidia CUDA Compiler - Wikipedia

    en.wikipedia.org/wiki/Nvidia_CUDA_Compiler

    CUDA code runs on both the central processing unit (CPU) and graphics processing unit (GPU). NVCC separates these two parts and sends host code (the part of code which will be run on the CPU) to a C compiler like GNU Compiler Collection (GCC) or Intel C++ Compiler (ICC) or Microsoft Visual C++ Compiler, and sends the device code (the part which will run on the GPU) to the GPU.

  9. PyTorch - Wikipedia

    en.wikipedia.org/wiki/PyTorch

    Although the Python interface is more polished and the primary focus of development, PyTorch also has a C++ interface. [14] A number of pieces of deep learning software are built on top of PyTorch, including Tesla Autopilot, [15] Uber's Pyro, [16] Hugging Face's Transformers, [17] PyTorch Lightning, [18] [19] and Catalyst. [20] [21]