# Using Multiple Processors

This section of the tutorial shows all the work that is needed to distribute operations in DEAP. Distribution relies on serialization of objects which is usually done by pickling, thus all objects that are distributed (functions and arguments, e.g. individuals and parameters) must be pickleable. This means that modifications made to an object on a distant processing unit will not be made available to the other processing units (including the master one) if it is not explicitly communicated through function arguments and return values.

## Scalable Concurrent Operations in Python (SCOOP)

SCOOP is a distributed task module allowing concurrent parallel programming on various environments, from heterogeneous grids to supercomputers. It has an interface similar to the `concurrent.futures` module introduced in Python 3.2. Its two simple functions `submit()` and `map()` allow to distribute computation efficiently and easily over a grid of computers.

In the second part, a complete algorithm was exposed with the `toolbox.map()` left to the default `map()`. In order to distribute the evaluations, we will replace this map by the one from SCOOP.

    from scoop import futures

    toolbox.register("map", futures.map)

Once this line is added, your program absolutely needs to be run from a `main()` function as mentioned in the scoop documentation. To run your program, use scoop as the main module.

    $ python -m scoop your_program.py

That is it, your program has been run in parallel on all available processors on your computer.

## Multiprocessing Module

Using the `multiprocessing` module is similar to using SCOOP. It can be done by replacing the appropriate function by the distributed one in the toolbox.

    import multiprocessing

    pool = multiprocessing.Pool()
    toolbox.register("map", pool.map)

    # Continue on with the evolutionary algorithm

Warning

As stated in the `multiprocessing` guidelines, under Windows, a process pool must be protected in a `if __name__ == "__main__"` section because of the way processes are initialized.

Note

While Python 2.6 is required for the multiprocessing module, the pickling of partial function is possible only since Python 2.7 (or 3.1), earlier version of Python may throw some strange errors when using partial function in the multiprocessing `multiprocessing.Pool.map()`. This may be avoided by creating local function outside of the toolbox (in Python version 2.6).

Note

The pickling of lambda function is not yet available in Python.