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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
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.
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 ...
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]
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]
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.
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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.