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#

distributed-data-parallel

Here are 19 public repositories matching this topic...

TTNet-Real-time-Analysis-System-for-Table-Tennis-Pytorch

Unofficial implementation of "TTNet: Real-time temporal and spatial video analysis of table tennis" (CVPR 2020)

  • UpdatedAug 2, 2024
  • Python
awesome-ai-dev-platform-opensource

Code for Active Learning at The ImageNet Scale. This repository implements many popular active learning algorithms and allows training with torch's DDP.

  • UpdatedNov 29, 2021
  • Python

A simple API to launch Python functions to run on multiple ranked processes, mpify is designed to enable interactive multiprocessing experiments in Jupyter/IPython, such as distributed data parallel training over multiple GPUs.

  • UpdatedSep 29, 2020
  • Python

Different template codes for Deep Learning with PyTorch.

  • UpdatedMay 11, 2022
  • Python

A Tiny Version of the Original ultralytics/yolov5

  • UpdatedMay 5, 2025
  • Python

This is a simulator for access strategies for distributed caching. The simulator considers a user who is equipped by several caches, and receives from them periodical updates about the cached content. The problem and algorithms implemented here are detailed in the paper: I. Cohen, G. Einziger, R. Friedman, and G. Scalosub, “Access Strategies for…

  • UpdatedOct 7, 2024
  • Python

[CVPR 2016]You Only Look Once: Unified, Real-Time Object Detection

  • UpdatedJul 9, 2023
  • Python
PyTorch_Template_Project

This repository is intended to be a template for starting new projects with PyTorch, in which deep learning models are trained and evaluated on medical imaging data.

  • UpdatedJul 7, 2022
  • Jupyter Notebook

Final project of the course "Large Scale AI Engineering" at ETH Zürich, FS2025. Implementation and benchmarking of pretokenization and Distributed Data Parallel (DDP) for efficient LLM training on the CSCS Alps supercomputer.

  • UpdatedMay 19, 2025
  • Python

Classification of Thoracic Diseases based on Chest X-ray Images.

  • UpdatedMar 27, 2025
  • Python

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