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PyTorch is a machine learning library based on the Torch library, [4] [5] [6] used for applications such as computer vision and natural language processing, [7] originally developed by Meta AI and now part of the Linux Foundation umbrella.
Format name Design goal 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
Moore Threads Technology Co. Ltd (Chinese: 摩尔线程) is a Chinese technology company specializing in graphics processing unit (GPU) design, established in October 2020 by Zhang Jianzhong (张建中), the former global vice-president of Nvidia and general manager of Nvidia China. [1]
Model – The marketing name for the processor, assigned by Nvidia. Launch – Date of release for the processor. Code name – The internal engineering codename for the processor (typically designated by an NVXY name and later GXY where X is the series number and Y is the schedule of the project for that generation). Fab – Fabrication ...
JAX is a Python library that provides a machine learning framework for transforming numerical functions developed by Google with some contributions from Nvidia. [2] [3] [4] It is described as bringing together a modified version of autograd (automatic obtaining of the gradient function through differentiation of a function) and OpenXLA's XLA (Accelerated Linear Algebra).
Lists of graphics cards follow. A graphics card, or graphics processing unit, is a specialized electronic circuit that rapidly manipulates and alters memory to build images in a frame buffer for output to a display. By manufacturer, they include: List of AMD graphics processing units; Intel Graphics Technology; List of Nvidia graphics ...
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]
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.