Search results
Results from the WOW.Com Content Network
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.
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]
Main page; Contents; Current events; Random article; About Wikipedia; Contact us; Donate
In September 2022, Meta announced that PyTorch would be governed by the independent PyTorch Foundation, a newly created subsidiary of the Linux Foundation. [ 24 ] 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 ...
File:Strategy Graphic - For collaborative editing.svg File:Strategy Graphic - Empty template.svg This work was created by María Cruz , Ed Bland, Nicole Ebber , Shannon Keith, Jaime Anstee , Suzie Nussel..
This image or file is a work of a U.S. Army Corps of Engineers soldier or employee, taken or made as part of that person's official duties. As a work of the U.S. federal government , the image is in the public domain .
First, a Combination Wave Generator is a standardized impulse generator (sometimes also referred to as a lightning surge generator), it's used for producing simulated, standard voltage and current surges under laboratory conditions. Subsequently, the surge is transmitted into a port of the Device-Under-Test (DUT) via a coupling network.
Teacher forcing is an algorithm for training the weights of recurrent neural networks (RNNs). [1] It involves feeding observed sequence values (i.e. ground-truth samples) back into the RNN after each step, thus forcing the RNN to stay close to the ground-truth sequence.