==============
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``. ::

    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 <https://conda.io>`_.
* 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 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.

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Contributing
------------

* ``isort`` and ``black`` 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 .

Node
----
* The Manager. Optimizations at process level.

Dataset
-------
* Static

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

Graph
-----
* Static. Who are my neighbours? Topologies.

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

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

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

Model
-----
* Learning Model