decentralizepy is a framework for running distributed applications (particularly ML) on top of arbitrary topologies (decentralized, federated, parameter server). It was primarily conceived for assessing scientific ideas on several aspects of distributed learning (communication efficiency, privacy, data heterogeneity etc.).
Fork the repository.
Clone and enter your local repository.
Check if you have
python>=3.8
.python --version
(Optional) Create and activate a virtual environment.
python3 -m venv [venv-name] source [venv-name]/bin/activate
Update pip.
pip3 install --upgrade pip pip install --upgrade pip
On Mac M1, installing
pyzmq
fails with pip. Use conda.Install decentralizepy for development. (zsh)
pip3 install --editable .\[dev\]
Install decentralizepy for development. (bash)
pip3 install --editable .[dev]
Download CIFAR-10 using
download_dataset.py
.python download_dataset.py
(Optional) Download other datasets from LEAF <https://github.com/TalwalkarLab/leaf> and place them in
eval/data/
.
Follow the tutorial in
tutorial/
. OR,Generate a new graph file with the required topology using
generate_graph.py
.python generate_graph.py --help
Choose and modify one of the config files in
eval/{step,epoch}_configs
.Modify the dataset paths and
addresses_filepath
in the config file.In eval/run.sh, modify arguments as required.
Execute eval/run.sh on all the machines simultaneously. There is a synchronization barrier mechanism at the start so that all processes start training together.
Cite us as
@inproceedings{decentralizepy, author = {Dhasade, Akash and Kermarrec, Anne-Marie and Pires, Rafael and Sharma, Rishi and Vujasinovic, Milos}, title = {Decentralized Learning Made Easy with DecentralizePy}, year = {2023}, isbn = {9798400700842}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3578356.3592587}, doi = {10.1145/3578356.3592587}, booktitle = {Proceedings of the 3rd Workshop on Machine Learning and Systems}, pages = {34โ41}, numpages = {8}, keywords = {peer-to-peer, distributed systems, machine learning, middleware, decentralized learning, network topology}, location = {Rome, Italy}, series = {EuroMLSys '23} }
- Tutorial
tutorial/EpidemicLearning
- Source files
src/node/EpidemicLearning/
- Cite
Martijn de Vos, Sadegh Farhadkhani, Rachid Guerraoui, Anne-Marie Kermarrec, Rafael Pires, and Rishi Sharma. Epidemic Learning: Boosting Decentralized Learning with Randomized Communication. In Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS), 2023.
- Tutorial
tutorial/JWINS
- Source files
src/sharing/JWINS/
- Cite
Akash Dhasade, Anne-Marie Kermarrec, Rafael Pires, Rishi Sharma, Jeffrey Wigger, and Milos Vujasinovic. Get More for Less in Decentralized Learning Systems. In IEEE 43rd International Conference on Distributed Computing Systems (ICDCS), 2023.
isort
andblack
are installed along with the package for code linting.While in the root directory of the repository, before committing the changes, please run
black . isort .
Following are the modules of decentralizepy:
- The Manager. Optimizations at process level.
- Static
- Heterogeneity. How much do I want to work?
- Static. Who are my neighbours? Topologies.
- Naming. The globally unique ids of the
processes <-> machine_id, local_rank
- Leverage Redundancy. Privacy. Optimizations in model and data sharing.
- IPC/Network level. Compression. Privacy. Reliability
- Learning Model