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PyTorch has also been developing support for other GPU platforms, for example, AMD's ROCm [27] and Apple's Metal Framework. [28] PyTorch supports various sub-types of Tensors. [29] Note that the term "tensor" here does not carry the same meaning as tensor in mathematics or physics.
ROCm [3] is an Advanced Micro Devices (AMD) software stack for graphics processing unit (GPU) programming. ROCm spans several domains: general-purpose computing on graphics processing units (GPGPU), high performance computing (HPC), heterogeneous computing.
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.
Advanced Micro Devices (AMD) joins the newly-launched PyTorch foundation to create a sustainable ecosystem of open-source projects with PyTorch and drive the growing adoption of AI.
In computing, CUDA is a proprietary [1] parallel computing platform and application programming interface (API) that allows software to use certain types of graphics processing units (GPUs) for accelerated general-purpose processing, an approach called general-purpose computing on GPUs.
AMD support Year introduced Introduced with Rendering Computing / ROCm; Vulkan [1] OpenGL [2] Direct3D HSA OpenCL; Wonder: Fixed-pipeline [a] 1000 nm 800 nm — — — — — Ended 1986 Graphics Solutions Mach: 800 nm 600 nm 1991 Mach8 3D Rage: 500 nm 5.0 1996 3D Rage Rage Pro: 350 nm 1.1 6.0 1997 Rage Pro Rage 128: 250 nm 1.2 1998 Rage 128 ...
OpenMP support OpenCL support CUDA support ROCm support [1] Automatic differentiation [2] Has pretrained models Recurrent nets Convolutional nets RBM/DBNs Parallel execution (multi node) Actively developed BigDL: Jason Dai (Intel) 2016 Apache 2.0: Yes Apache Spark Scala Scala, Python No No Yes Yes Yes Yes Caffe: Berkeley Vision and Learning ...
ONNX was originally named Toffee [4] and was developed by the PyTorch team at Facebook. [5] In September 2017 it was renamed to ONNX and announced by Facebook and Microsoft. [6] Later, IBM, Huawei, Intel, AMD, Arm and Qualcomm announced support for the initiative. [3]