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In this case the formula to calculate the theoretical performance in floating point operations per second becomes: FLOPS sp = 2 × n × f. The theoretical double-precision processing power of a Tesla GPU is 1/8 of the single precision performance on GT200; there is no double precision support on G8x and G9x. [9]
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]
PyTorch 2.0 was released on 15 March 2023, introducing TorchDynamo, a Python-level compiler that makes code run up to 2x faster, along with significant improvements in training and inference performance across major cloud platforms. [25] [26]
Nvidia Tesla C2075. Offering computational power much greater than traditional microprocessors, the Tesla products targeted the high-performance computing market. [4] As of 2012, Nvidia Teslas power some of the world's fastest supercomputers, including Summit at Oak Ridge National Laboratory and Tianhe-1A, in Tianjin, China.
Overall, Tesla claims HW3 has 2.5× improved performance over HW2.5, with 1.25× higher power and 0.2× lower cost. [34] HW3 is based on a custom Tesla-designed system on a chip called "FSD Chip", [35] fabricated using a 14 nm process by Samsung. [36] Jim Keller and Pete Bannon, among other architects, have led the project since February 2016. [37]
Tesla Dojo is a supercomputer designed and built by Tesla for computer vision video processing and recognition. [1] It is used for training Tesla's machine learning models to improve its Full Self-Driving (FSD) advanced driver-assistance system. According to Tesla, it went into production in July 2023. [2]
The Open Neural Network Exchange (ONNX) [ˈɒnɪks] [2] is an open-source artificial intelligence ecosystem [3] of technology companies and research organizations that establish open standards for representing machine learning algorithms and software tools to promote innovation and collaboration in the AI sector.
PyTorch Lightning is an open-source Python library that provides a high-level interface for PyTorch, a popular deep learning framework. [1] It is a lightweight and high-performance framework that organizes PyTorch code to decouple research from engineering, thus making deep learning experiments easier to read and reproduce.