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  2. Deeplearning4j - Wikipedia

    en.wikipedia.org/wiki/Deeplearning4j

    Deeplearning4j can be used via multiple API languages including Java, Scala, Python, Clojure and Kotlin. Its Scala API is called ScalNet. [31] Keras serves as its Python API. [32] And its Clojure wrapper is known as DL4CLJ. [33] The core languages performing the large-scale mathematical operations necessary for deep learning are C, C++ and CUDA C.

  3. CUDA - Wikipedia

    en.wikipedia.org/wiki/CUDA

    CUDA is designed to work with programming languages such as C, C++, Fortran and Python. This accessibility makes it easier for specialists in parallel programming to use GPU resources, in contrast to prior APIs like Direct3D and OpenGL , which require advanced skills in graphics programming. [ 6 ]

  4. 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.

  5. 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]

  6. Google JAX - Wikipedia

    en.wikipedia.org/wiki/Google_JAX

    Google JAX is a machine learning framework for transforming numerical functions. [1] [2] [3] It is described as bringing together a modified version of autograd (automatic obtaining of the gradient function through differentiation of a function) and TensorFlow's XLA (Accelerated Linear Algebra).

  7. General-purpose computing on graphics processing units

    en.wikipedia.org/wiki/General-purpose_computing...

    This cumbersome translation was obviated by the advent of general-purpose programming languages and APIs such as Sh/RapidMind, Brook and Accelerator. [9] [10] [11] These were followed by Nvidia's CUDA, which allowed programmers to ignore the underlying graphical concepts in favor of more common high-performance computing concepts. [12]

  8. OptiX - Wikipedia

    en.wikipedia.org/wiki/OptiX

    CUDA is only available for Nvidia's graphics products. Nvidia OptiX is part of Nvidia GameWorks . OptiX is a high-level, or "to-the-algorithm" API, meaning that it is designed to encapsulate the entire algorithm of which ray tracing is a part, not just the ray tracing itself.

  9. Python (programming language) - Wikipedia

    en.wikipedia.org/wiki/Python_(programming_language)

    IronPython allows running Python 2.7 programs (and an alpha, released in 2021, is also available for "Python 3.4, although features and behaviors from later versions may be included" [169]) on the .NET Common Language Runtime. [170] Jython compiles Python 2.7 to Java bytecode, allowing the use of the Java libraries from a Python program. [171]