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  2. TensorFlow - Wikipedia

    en.wikipedia.org/wiki/TensorFlow

    TensorFlow is available on 64-bit Linux, macOS, Windows, and mobile computing platforms including Android and iOS. [ citation needed ] Its flexible architecture allows for easy deployment of computation across a variety of platforms (CPUs, GPUs, TPUs ), and from desktops to clusters of servers to mobile and edge devices .

  3. CUDA - Wikipedia

    en.wikipedia.org/wiki/CUDA

    Note: CUDA SDK 10.2 is the last official release for macOS, as support will not be available for macOS in newer releases. CUDA compute capability by version with associated GPU semiconductors and GPU card models (separated by their various application areas):

  4. Comparison of deep learning software - Wikipedia

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

    Linux, macOS, Windows: Python: Python, R: Only if using Theano as backend 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

  5. PlaidML - Wikipedia

    en.wikipedia.org/wiki/PlaidML

    PlaidML is a portable tensor compiler.Tensor compilers bridge the gap between the universal mathematical descriptions of deep learning operations, such as convolution, and the platform and chip-specific code needed to perform those operations with good performance.

  6. PyTorch - Wikipedia

    en.wikipedia.org/wiki/PyTorch

    PyTorch defines a class called Tensor (torch.Tensor) to store and operate on homogeneous multidimensional rectangular arrays of numbers.PyTorch Tensors are similar to NumPy Arrays, but can also be operated on a CUDA-capable NVIDIA GPU.

  7. Tensor Processing Unit - Wikipedia

    en.wikipedia.org/wiki/Tensor_Processing_Unit

    Tensor Processing Unit (TPU) is an AI accelerator application-specific integrated circuit (ASIC) developed by Google for neural network machine learning, using Google's own TensorFlow software. [2]

  8. 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]

  9. Pop!_OS - Wikipedia

    en.wikipedia.org/wiki/Pop!_OS

    The latest releases also have packages that allow for easy setup for TensorFlow and CUDA. [5] [6] Pop!_OS is maintained primarily by System76, with the release version source code hosted in a GitHub repository. Unlike many other Linux distributions, it is not community-driven, although outside programmers can contribute, view and modify the ...