"Moshpit SGD: Communication-Efficient Decentralized Training on Heterogeneous Unreliable Devices", official implementation

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Deep Learning moshpit-sgd
Overview

Moshpit SGD: Communication-Efficient Decentralized Training on Heterogeneous Unreliable Devices

Illustration of Moshpit SGD

This repository contains the official PyTorch implementation of experiments for the paper.

Note (05.03.2021): as of now, this repository contains only the minimal (largely untested) version of the implementation. We intend to make the training code more robust and to add tested code for more experiments (including image classification) in the coming months. In the meantime, feel free to create an issue or contact us by email if you are having any troubles.

Setup

To launch the code in this repository, you will need Python 3.8+ and PyTorch 1.7. Also, install the dependencies by running pip install -r requirements.txt.

Experiments

The links below The first experiment is a self-contained Jupyter notebook; for the other two experiments, refer to README.md in their respective directories:

References

@misc{ryabinin2021moshpit,
      title={Moshpit SGD: Communication-Efficient Decentralized Training on Heterogeneous Unreliable Devices}, 
      author={Max Ryabinin and Eduard Gorbunov and Vsevolod Plokhotnyuk and Gennady Pekhimenko},
      year={2021},
      eprint={2103.03239},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}
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Comments
  • How to launch jobs on vast.ai instances

    How to launch jobs on vast.ai instances

    Hi @mryab ,

    Really cool work! I am trying to replicate the experiments you mentioned in the paper.

    I am wondering how do you set up the vast.ai workflow? I wonder if you have similar code like the notebook you showed for AWS https://github.com/yandex-research/moshpit-sgd/blob/main/language_modeling/run_training_aws.ipynb? It would be really helpful if you can share that.

    Thank you in advance!

    opened by Tiiiger 1
Owner
Yandex Research
Yandex Research
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