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decentralizepy
==============

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Setting up decentralizepy
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* Fork the repository.
* Clone and enter your local repository.
* Check if you have ``python>=3.8``.
* (Optional) Create and activate a virtual environment.
* Update pip. ::

    pip3 install --upgrade pip
    pip install --upgrade pip

* On Mac M1, installing ``pyzmq`` fails with `pip`. Use ``conda``.
* Install decentralizepy for development. ::

    pip3 install --editable .\[dev\]
    
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Running the code
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* 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 ``first_machine`` (used to calculate machine_id of all machines), ``original_config``, and other 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.

Node
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* The Manager. Optimizations at process level.

Dataset
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* Static

Training
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* Heterogeneity. How much do I want to work?

Graph
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* Static. Who are my neighbours? Topologies.

Mapping
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* Naming. The globally unique ids of the ``processes <-> machine_id, local_rank``

Sharing
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* Leverage Redundancy. Privacy. Optimizations in model and data sharing.

Communication
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* IPC/Network level. Compression. Privacy. Reliability

Model
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* Learning Model