Hivemind: decentralized deep learning in PyTorch

Hivemind is a PyTorch library for decentralized deep learning across the Internet. Its intended usage is training one large model on hundreds of computers from different universities, companies, and volunteers.

Key Features

  • Distributed training without a master node: Distributed Hash Table allows connecting computers in a decentralized
  • Fault-tolerant backpropagation: forward and backward passes succeed even if some nodes are unresponsive or take too
    long to respond.
  • Decentralized parameter averaging: iteratively aggregate updates from multiple workers without the need to
    synchronize across the entire network (paper).
  • Train neural networks of arbitrary size: parts of their layers are distributed across the participants with the
    Decentralized Mixture-of-Experts (paper).

To learn more about the ideas behind this library, see or read
the NeurIPS 2020 paper.


Before installing, make sure that your environment has Python 3.7+
and PyTorch 1.6.0 or newer. They can be installed either
natively or with Anaconda.

You can get the latest release with pip or build hivemind from source.

With pip

If your versions of Python and PyTorch match the requirements, you can install hivemind from pip:

pip install hivemind

From source

To install hivemind from source, simply run the following:

git clone
cd hivemind
pip install .

If you would like to verify that your installation is working properly, you can install with pip install -e .[dev]
instead. Then, you can run the tests with pytest tests/.

By default, hivemind uses the precompiled binary of
the go-libp2p-daemon library. If you face compatibility issues
or want to build the binary yourself, you can recompile it by running pip install . --global-option="--buildgo".
Before running the compilation, please ensure that your machine has a recent version
of Go toolchain (1.15 or higher).

System requirements

  • Linux is the default OS for which hivemind is developed and tested. We recommend Ubuntu 18.04+ (64-bit), but
    other 64-bit distros should work as well. Legacy 32-bit is not recommended.
  • macOS 10.x mostly works but requires building hivemind from source, and some edge cases may fail. To ensure full
    compatibility, we recommend using our Docker image.
  • Windows 10+ (experimental) can run hivemind
    using WSL. You can configure WSL to use GPU by
    following sections 1–3 of this guide by NVIDIA. After
    that, you can simply follow the instructions above to install with pip or from source.


If you have any questions about installing and using hivemind, you can ask them in
our Discord chat or file an issue.


Hivemind is currently at the active development stage, and we welcome all contributions. Everything, from bug fixes and
documentation improvements to entirely new features, is equally appreciated.

If you want to contribute to hivemind but don't know where to start, take a look at the
unresolved issues. Open a new issue or
join our chat room in case you want to discuss new functionality or report a possible
bug. Bug fixes are always welcome, but new features should be preferably discussed with maintainers beforehand.

If you want to start contributing to the source code of hivemind, please see
the contributing guidelines first. To learn
more about other ways to contribute, read
our guide.


If you found hivemind or its underlying algorithms useful for your research, please cite the following source:

  author = {[email protected] team},
  title = {{H}ivemind: a {L}ibrary for {D}ecentralized {D}eep {L}earning},
  year = 2020,
  howpublished = {\url{}},

Also, you can cite the paper that inspired the creation of this library
(prototype implementation of hivemind available
at mryab/learning-at-home):

 author = {Ryabinin, Max and Gusev, Anton},
 booktitle = {Advances in Neural Information Processing Systems},
 editor = {H. Larochelle and M. Ranzato and R. Hadsell and M. F. Balcan and H. Lin},
 pages = {3659--3672},
 publisher = {Curran Associates, Inc.},
 title = {Towards Crowdsourced Training of Large Neural Networks using Decentralized Mixture-of-Experts},
 url = {},
 volume = {33},
 year = {2020}

Additional publications

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

      title={Moshpit SGD: Communication-Efficient Decentralized Training on Heterogeneous Unreliable Devices}, 
      author={Max Ryabinin and Eduard Gorbunov and Vsevolod Plokhotnyuk and Gennady Pekhimenko},

"Distributed Deep Learning in Open Collaborations"

      title={Distributed Deep Learning in Open Collaborations}, 
      author={Michael Diskin and Alexey Bukhtiyarov and Max Ryabinin and Lucile Saulnier and Quentin Lhoest and Anton Sinitsin and Dmitry Popov and Dmitry Pyrkin and Maxim Kashirin and Alexander Borzunov and Albert Villanova del Moral and Denis Mazur and Ilia Kobelev and Yacine Jernite and Thomas Wolf and Gennady Pekhimenko},

"Secure Distributed Training at Scale"

      title={Secure Distributed Training at Scale}, 
      author={Eduard Gorbunov and Alexander Borzunov and Michael Diskin and Max Ryabinin},

We also maintain a list
of related projects and acknowledgements.


GitHub - learning-at-home/hivemind: Decentralized deep learning in PyTorch. Built to train models on thousands of volunteers across the world.
Decentralized deep learning in PyTorch. Built to train models on thousands of volunteers across the world. - GitHub - learning-at-home/hivemind: Decentralized deep learning in PyTorch. Built to tra...