examples/example_21_scoop_multiprocessing.py
def main():
    """Main function to protect the *entry point* of the program.

    If you want to use multiprocessing with SCOOP you need to wrap your
    main code creating an environment into a function. Otherwise
    the newly started child processes will re-execute the code and throw
    errors (also see http://scoop.readthedocs.org/en/latest/usage.html#pitfalls).

    """

    # Create an environment that handles running.
    # Let's enable multiprocessing with scoop:
    filename = os.path.join('hdf5', 'example_21.hdf5')
    env = Environment(trajectory='Example_21_SCOOP',
                      filename=filename,
                      file_title='Example_21_SCOOP',
                      log_stdout=True,
                      comment='Multiprocessing example using SCOOP!',
                      multiproc=True,
                      freeze_input=True, # We want to save overhead and freeze input
                      use_scoop=True, # Yes we want SCOOP!
                      wrap_mode=pypetconstants.WRAP_MODE_LOCAL,  # SCOOP only works with 'LOCAL'
                      # or 'NETLOCK' wrapping
                      overwrite_file=True)

    # Get the trajectory from the environment
    traj = env.trajectory

    # Add both parameters
    traj.f_add_parameter('x', 1.0, comment='I am the first dimension!')
    traj.f_add_parameter('y', 1.0, comment='I am the second dimension!')

    # Explore the parameters with a cartesian product, but we want to explore a bit more
    traj.f_explore(cartesian_product({'x':[float(x) for x in range(20)],
                                      'y':[float(y) for y in range(20)]}))
    # Run the simulation
    env.run(multiply)

    # Let's check that all runs are completed!
    assert traj.f_is_completed()

    # Finally disable logging and close all log-files
    env.disable_logging()
def main():
    """Main function to protect the *entry point* of the program.

    If you want to use multiprocessing with SCOOP you need to wrap your
    main code creating an environment into a function. Otherwise
    the newly started child processes will re-execute the code and throw
    errors (also see http://scoop.readthedocs.org/en/latest/usage.html#pitfalls).

    """

    # Create an environment that handles running.
    # Let's enable multiprocessing with scoop:
    filename = os.path.join('hdf5', 'example_21.hdf5')
    env = Environment(trajectory='Example_21_SCOOP',
                      filename=filename,
                      file_title='Example_21_SCOOP',
                      log_stdout=True,
                      comment='Multiprocessing example using SCOOP!',
                      multiproc=True,
                      freeze_input=True, # We want to save overhead and freeze input
                      use_scoop=True, # Yes we want SCOOP!
                      wrap_mode=pypetconstants.WRAP_MODE_LOCAL,  # SCOOP only works with 'LOCAL'
                      # or 'NETLOCK' wrapping
                      overwrite_file=True)

    # Get the trajectory from the environment
    traj = env.trajectory

    # Add both parameters
    traj.f_add_parameter('x', 1.0, comment='I am the first dimension!')
    traj.f_add_parameter('y', 1.0, comment='I am the second dimension!')

    # Explore the parameters with a cartesian product, but we want to explore a bit more
    traj.f_explore(cartesian_product({'x':[float(x) for x in range(20)],
                                      'y':[float(y) for y in range(20)]}))
    # Run the simulation
    env.run(multiply)

    # Let's check that all runs are completed!
    assert traj.f_is_completed()

    # Finally disable logging and close all log-files
    env.disable_logging()
def main ( ) : # Create an environment that handles running. # Let's enable multiprocessing with scoop: filename = os . path . join ( 'hdf5' , 'example_21.hdf5' ) env = Environment ( trajectory = 'Example_21_SCOOP' , filename = filename , file_title = 'Example_21_SCOOP' , log_stdout = True , comment = 'Multiprocessing example using SCOOP!' , multiproc = True , freeze_input = True , # We want to save overhead and freeze input use_scoop = True , # Yes we want SCOOP! wrap_mode = pypetconstants . WRAP_MODE_LOCAL , # SCOOP only works with 'LOCAL' # or 'NETLOCK' wrapping overwrite_file = True ) # Get the trajectory from the environment traj = env . trajectory # Add both parameters traj . f_add_parameter ( 'x' , 1.0 , comment = 'I am the first dimension!' ) traj . f_add_parameter ( 'y' , 1.0 , comment = 'I am the second dimension!' ) # Explore the parameters with a cartesian product, but we want to explore a bit more traj . f_explore ( cartesian_product ( { 'x' : [ float ( x ) for x in range ( 20 ) ] , 'y' : [ float ( y ) for y in range ( 20 ) ] } ) ) # Run the simulation env . run ( multiply ) # Let's check that all runs are completed! assert traj . f_is_completed ( ) # Finally disable logging and close all log-files env . disable_logging ( )
Main function to protect the *entry point* of the program.

    If you want to use multiprocessing with SCOOP you need to wrap your
    main code creating an environment into a function. Otherwise
    the newly started child processes will re-execute the code and throw
    errors (also see http://scoop.readthedocs.org/en/latest/usage.html#pitfalls).
Main function to protect the * entry point * of the program .