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  2. PyTorch - Wikipedia

    en.wikipedia.org/wiki/PyTorch

    In September 2022, Meta announced that PyTorch would be governed by the independent PyTorch Foundation, a newly created subsidiary of the Linux Foundation. [ 24 ] PyTorch 2.0 was released on 15 March 2023, introducing TorchDynamo , a Python-level compiler that makes code run up to 2x faster, along with significant improvements in training and ...

  3. CUDA - Wikipedia

    en.wikipedia.org/wiki/CUDA

    CUDA is a software layer that gives direct access to the GPU's virtual instruction set and parallel computational elements for the execution of compute kernels. [5] In addition to drivers and runtime kernels, the CUDA platform includes compilers, libraries and developer tools to help programmers accelerate their applications.

  4. Torch (machine learning) - Wikipedia

    en.wikipedia.org/wiki/Torch_(machine_learning)

    The torch package also simplifies object-oriented programming and serialization by providing various convenience functions which are used throughout its packages. The torch.class(classname, parentclass) function can be used to create object factories ().

  5. 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]

  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. Comparison of deep learning software - Wikipedia

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

    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 Toolkit (CNTK) Microsoft Research: 2016 MIT license [28] Yes Windows, Linux [29] (macOS via Docker on roadmap) C++

  8. FDNY unions say congestion toll will hurt response times ...

    www.aol.com/news/fdny-unions-congestion-toll...

    The city’s hated new congestion toll could dangerously delay FDNY response times — meaning the “difference between life and death,” unions repping thousands of Bravest warned Sunday. The ...

  9. Caffe (software) - Wikipedia

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

    Caffe supports GPU- and CPU-based acceleration ... (RNN). At the end of March 2018, Caffe2 was merged into PyTorch. [13 ... Text is available under the ...