When.com Web Search

Search results

  1. Results From The WOW.Com Content Network
  2. DeepSpeed - Wikipedia

    en.wikipedia.org/wiki/DeepSpeed

    The library is designed to reduce computing power and memory use and to train large distributed models with better parallelism on existing computer hardware. [2] [3] DeepSpeed is optimized for low latency, high throughput training.

  3. Comparison of deep learning software - Wikipedia

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

    Compatible with other formats Self-contained DNN Model Pre-processing and Post-processing Run-time configuration for tuning & calibration DNN model interconnect Common platform TensorFlow, Keras, Caffe, Torch: Algorithm training No No / Separate files in most formats No No No Yes ONNX: Algorithm training Yes No / Separate files in most formats ...

  4. CUDA - Wikipedia

    en.wikipedia.org/wiki/CUDA

    CUDA works with all Nvidia GPUs from the G8x series onwards, including GeForce, Quadro and the Tesla line. CUDA is compatible with most standard operating systems. CUDA 8.0 comes with the following libraries (for compilation & runtime, in alphabetical order): cuBLAS – CUDA Basic Linear Algebra Subroutines library; CUDART – CUDA Runtime library

  5. CuPy - Wikipedia

    en.wikipedia.org/wiki/CuPy

    CuPy has been initially developed as a backend of Chainer deep learning framework, and later established as an independent project in 2017. [ 6 ] CuPy is a part of the NumPy ecosystem array libraries [ 7 ] and is widely adopted to utilize GPU with Python, [ 8 ] especially in high-performance computing environments such as Summit , [ 9 ...

  6. PyTorch - Wikipedia

    en.wikipedia.org/wiki/PyTorch

    The Open Neural Network Exchange project was created by Meta and Microsoft in September 2017 for converting models between frameworks. Caffe2 was merged into PyTorch at the end of March 2018. [ 23 ] In September 2022, Meta announced that PyTorch would be governed by the independent PyTorch Foundation, a newly created subsidiary of the Linux ...

  7. ROCm - Wikipedia

    en.wikipedia.org/wiki/ROCm

    Nvidia's CUDA is closed-source, whereas AMD ROCm is open source. There is open-source software built on top of the closed-source CUDA, for instance RAPIDS . CUDA is able run on consumer GPUs, whereas ROCm support is mostly offered for professional hardware such as AMD Instinct and AMD Radeon Pro .

  8. bfloat16 floating-point format - Wikipedia

    en.wikipedia.org/wiki/Bfloat16_floating-point_format

    Many libraries support bfloat16, such as CUDA, [13] Intel oneAPI Math Kernel Library, AMD ROCm, [14] AMD Optimizing CPU Libraries, PyTorch, and TensorFlow. [ 10 ] [ 15 ] On these platforms, bfloat16 may also be used in mixed-precision arithmetic , where bfloat16 numbers may be operated on and expanded to wider data types.

  9. Nvidia NVDEC - Wikipedia

    en.wikipedia.org/wiki/Nvidia_NVDEC

    Nvidia NVDEC (formerly known as NVCUVID [1]) is a feature in its graphics cards that performs video decoding, offloading this compute-intensive task from the CPU. [2] NVDEC is a successor of PureVideo and is available in Kepler and later Nvidia GPUs.