.. currentmodule:: brian2

.. 02_using_standalone:

Example: 02_using_standalone
============================


        .. only:: html

            .. |launchbinder| image:: http://mybinder.org/badge.svg
            .. _launchbinder: https://mybinder.org/v2/gh/brian-team/brian2-binder/master?filepath=examples/multiprocessing/02_using_standalone.ipynb

            .. note::
               You can launch an interactive, editable version of this
               example without installing any local files
               using the Binder service (although note that at some times this
               may be slow or fail to open): |launchbinder|_

        

Parallel processes using standalone mode

This example use multiprocessing to run several simulations in parallel.
The code is using the C++ standalone mode to compile and execute the code.

The generated code is stored in a ``standalone{pid}`` directory, with ``pid``
being the id of each process.

Note that the `set_device` call should be in the ``run_sim`` function.

By moving the `set_device` line into the parallelised function, it creates one
C++ standalone device per process.
The ``device.reinit()`` needs to be called` if you are running multiple
simulations per process (there are 10 tau values and num_proc = 4).

Each simulation uses it's own code folder to generate the code for the
simulation, controlled by the directory keyword to the set_device
call. By setting ``directory=None``, a temporary folder with random name is
created. This way, each simulation uses a different folder for code generation
and there is nothing shared between the parallel processes.

If you don't set the directory argument, it defaults to ``directory="output"``.
In that case each process would use the same files to try to generate and
compile your simulation, which would lead to compile/execution errors.

Setting ``directory=f"standalone{pid}"`` is even better than using
``directory=None`` in this case. That is, giving each parallel process
*it's own directory to work on*. This way you avoid the problem of multiple
processes working on the same code directories. But you also don't need to
recompile the entire project at each simulation. What happens is that in the
generated code in two consecutive simulations in a single process will only
differ slightly (in this case only the tau parameter). The compiler will
therefore only recompile the file that has changed and not the entire project.

The ``numb_proc`` sets the number of processes. ``run_sim`` is just a toy
example that creates a single neuron and connects a `StateMonitor` to record
the voltage.

For more details see the `discussion in the Brian forum <https://brian.discourse.group/t/multiprocessing-in-standalone-mode/142/2>`_.

Note that Python's `multiprocessing` module cannot deal with user-defined functions (including `TimedArray`) and other
complex code structures. If you run into `PicklingError` or `AttributeError` exceptions, you might
have to use the `pathos` (https://pypi.org/project/pathos) package instead, which can handle more complex
code structures.

::

    import os
    import multiprocessing
    from time import time as wall_time
    from os import system
    from brian2 import *
    
    def run_sim(tau):
        pid = os.getpid()
        directory = f"standalone{pid}"
        set_device('cpp_standalone', directory=directory)
        print(f'RUNNING {pid}')
    
        G = NeuronGroup(1, 'dv/dt = -v/tau : 1', method='euler')
        G.v = 1
    
        mon = StateMonitor(G, 'v', record=0)
        net = Network()
        net.add(G, mon)
        net.run(100 * ms)
        res = (mon.t/ms, mon.v[0])
    
        device.reinit()
    
        print(f'FINISHED {pid}')
        return res
    
    
    if __name__ == "__main__":
        start_time = wall_time()
        
        num_proc = 4
        tau_values = np.arange(10)*ms + 5*ms
        with multiprocessing.Pool(num_proc) as p:
            results = p.map(run_sim, tau_values)
    
        print(f"Done in {wall_time() - start_time:10.3f}")
    
        for tau_value, (t, v) in zip(tau_values, results):
            plt.plot(t, v, label=str(tau_value))
        plt.legend()
        plt.show()
    

.. image:: ../resources/examples_images/multiprocessing.02_using_standalone.1.png