def pp_map(function, sequence, *args, **kwds):
'''extend python's parallel map function to parallel python
Inputs:
function -- target function
sequence -- sequence to process in parallel
Additional Inputs:
ncpus -- number of 'local' processors to use [defaut = 'autodetect']
servers -- available distributed parallel python servers [default = ()]
'''
procs = None
servers = ()
if kwds.has_key('ncpus'):
procs = kwds['ncpus']
kwds.pop('ncpus')
if kwds.has_key('servers'):
servers = kwds['servers']
kwds.pop('servers')
# remove all the junk kwds that are added due to poor design!
if kwds.has_key('nnodes'): kwds.pop('nnodes')
if kwds.has_key('nodes'): kwds.pop('nodes')
if kwds.has_key('launcher'): kwds.pop('launcher')
if kwds.has_key('mapper'): kwds.pop('mapper')
if kwds.has_key('queue'): kwds.pop('queue')
if kwds.has_key('timelimit'): kwds.pop('timelimit')
if kwds.has_key('scheduler'): kwds.pop('scheduler')
# return Pool(procs, servers=servers).map(function, sequence, *args, **kwds)
if not __STATE['server']:
__STATE['server'] = job_server = ppServer(ppservers=servers)
return list(ppmap(procs,function,sequence,*args))
def pp_map(function, sequence, *args, **kwds):
'''extend python's parallel map function to parallel python
Inputs:
function -- target function
sequence -- sequence to process in parallel
Additional Inputs:
ncpus -- number of 'local' processors to use [defaut = 'autodetect']
servers -- available distributed parallel python servers [default = ()]
'''
procs = None
servers = ()
if 'ncpus' in kwds:
procs = kwds['ncpus']
kwds.pop('ncpus')
if 'servers' in kwds:
servers = kwds['servers']
kwds.pop('servers')
# remove all the junk kwds that are added due to poor design!
if 'nnodes' in kwds: kwds.pop('nnodes')
if 'nodes' in kwds: kwds.pop('nodes')
if 'launcher' in kwds: kwds.pop('launcher')
if 'mapper' in kwds: kwds.pop('mapper')
if 'queue' in kwds: kwds.pop('queue')
if 'timelimit' in kwds: kwds.pop('timelimit')
if 'scheduler' in kwds: kwds.pop('scheduler')
# return Pool(procs, servers=servers).map(function, sequence, *args, **kwds)
if not __STATE.get('server', None):
__STATE['server'] = job_server = ppServer(ppservers=servers)
return list(ppmap(procs, function, sequence, *args))
def pp_map(function, sequence, *args, **kwds):
"""extend python's parallel map function to parallel python
Inputs:
function -- target function
sequence -- sequence to process in parallel
Additional Inputs:
ncpus -- number of 'local' processors to use [defaut = 'autodetect']
servers -- available distributed parallel python servers [default = ()]
"""
procs = None
servers = ()
if kwds.has_key("ncpus"):
procs = kwds["ncpus"]
kwds.pop("ncpus")
if kwds.has_key("servers"):
servers = kwds["servers"]
kwds.pop("servers")
# remove all the junk kwds that are added due to poor design!
if kwds.has_key("nnodes"):
kwds.pop("nnodes")
if kwds.has_key("nodes"):
kwds.pop("nodes")
if kwds.has_key("launcher"):
kwds.pop("launcher")
if kwds.has_key("mapper"):
kwds.pop("mapper")
if kwds.has_key("queue"):
kwds.pop("queue")
if kwds.has_key("timelimit"):
kwds.pop("timelimit")
if kwds.has_key("scheduler"):
kwds.pop("scheduler")
# return Pool(procs, servers=servers).map(function, sequence, *args, **kwds)
if not __STATE["server"]:
__STATE["server"] = job_server = ppServer(ppservers=servers)
return list(ppmap(procs, function, sequence, *args))
def ppmap(processes, function, sequence, *sequences):
"""Split the work of 'function' across the given number of
processes. Set 'processes' to None to let Parallel Python
autodetect the number of children to use.
Although the calling semantics should be identical to
__builtin__.map (even using __builtin__.map to process
arguments), it differs in that it returns a generator instead of a
list. This enables lazy evaluation of the results so that other
work can be done while the subprocesses are still running.
>>> def rangetotal(n): return n, sum(range(n))
>>> list(map(rangetotal, range(1, 6)))
[(1, 0), (2, 1), (3, 3), (4, 6), (5, 10)]
>>> list(ppmap(1, rangetotal, range(1, 6)))
[(1, 0), (2, 1), (3, 3), (4, 6), (5, 10)]
"""
ppservers = ("*",) # autodetect
#from _ppserver_config import ppservers # read from a config file
# Create a new server if one isn't already initialized
if not __STATE['server']:
__STATE['server'] = ppServer(ppservers=ppservers)
#class dill_wrapper(object):
# """handle non-picklable functions by wrapping with dill"""
# def __init__(self, function):
# from dill import dumps
# self.pickled_function = dumps(function)
# def __call__(self, *args):
# from dill import loads #XXX: server now requires dill
# f = loads(self.pickled_function)
# return f(*args)
# def dill_wrapper(function):
# """handle non-picklable functions by wrapping with dill"""
# from dill import dumps
# pickled_function = dumps(function)
# def unwrap(*args):
# from dill import loads #XXX: server now requires dill
# f = loads(pickled_function)
# return f(*args)
# return unwrap
def submit(*args): #XXX: needs **kwds to allow "depfuncs, modules, ...?
"""Send a job to the server"""
#print globals()['ncalls'] #FIXME: ncalls not in globals()
#XXX: options for submit...
#XXX: func -- function to be executed
#XXX: depfuncs -- functions called from 'func'
#XXX: modules -- modules to import
#XXX: callback -- callback function to be called after 'func' completes
#XXX: callbackargs -- additional args for callback(result, *args)
#XXX: group -- allows naming of 'job group' to use in wait(group)
#XXX: globals -- dictionary from which everything imports
# from mystic.tools import wrap_function, wrap_bounds
# return __STATE['server'].submit(function, args, \
# depfuncs=(wrap_function,wrap_bounds), \
## modules=("mystic","numpy"), \
# globals=globals())
# p_function = dill_wrapper(function)
# return __STATE['server'].submit(p_function, args, globals=globals())
#print __STATE['server'].get_ncpus(), "local workers" #XXX: debug
return __STATE['server'].submit(function, args, globals=globals())
# Merge all the passed-in argument lists together. This is done
# that way because as with the map() function, at least one list
# is required but the rest are optional.
a = [sequence]
a.extend(sequences)
# Set the requested level of multi-processing
#__STATE['server'].set_ncpus(processes or 'autodetect') # never processes=0
if processes == None:
__STATE['server'].set_ncpus('autodetect')
else:
__STATE['server'].set_ncpus(processes) # allow processes=0
#print "running with", __STATE['server'].get_ncpus(), "local workers" #XXX: debug
# First, submit all the jobs. Then harvest the results as they
# come available.
return (subproc() for subproc in map(submit, *a))
def ppmap(processes, function, sequence, *sequences):
"""Split the work of 'function' across the given number of
processes. Set 'processes' to None to let Parallel Python
autodetect the number of children to use.
Although the calling semantics should be identical to
__builtin__.map (even using __builtin__.map to process
arguments), it differs in that it returns a generator instead of a
list. This enables lazy evaluation of the results so that other
work can be done while the subprocesses are still running.
>>> def rangetotal(n): return n, sum(range(n))
>>> list(map(rangetotal, range(1, 6)))
[(1, 0), (2, 1), (3, 3), (4, 6), (5, 10)]
>>> list(ppmap(1, rangetotal, range(1, 6)))
[(1, 0), (2, 1), (3, 3), (4, 6), (5, 10)]
"""
ppservers = ("*", ) # autodetect
#from _ppserver_config import ppservers # read from a config file
# Create a new server if one isn't already initialized
if not __STATE.get('server', None):
__STATE['server'] = ppServer(ppservers=ppservers)
#class dill_wrapper(object):
# """handle non-picklable functions by wrapping with dill"""
# def __init__(self, function):
# from dill import dumps
# self.pickled_function = dumps(function)
# def __call__(self, *args):
# from dill import loads #XXX: server now requires dill
# f = loads(self.pickled_function)
# return f(*args)
# def dill_wrapper(function):
# """handle non-picklable functions by wrapping with dill"""
# from dill import dumps
# pickled_function = dumps(function)
# def unwrap(*args):
# from dill import loads #XXX: server now requires dill
# f = loads(pickled_function)
# return f(*args)
# return unwrap
def submit(*args): #XXX: needs **kwds to allow "depfuncs, modules, ...?
"""Send a job to the server"""
#print globals()['ncalls'] #FIXME: ncalls not in globals()
#XXX: options for submit...
#XXX: func -- function to be executed
#XXX: depfuncs -- functions called from 'func'
#XXX: modules -- modules to import
#XXX: callback -- callback function to be called after 'func' completes
#XXX: callbackargs -- additional args for callback(result, *args)
#XXX: group -- allows naming of 'job group' to use in wait(group)
#XXX: globals -- dictionary from which everything imports
# from mystic.tools import wrap_function, wrap_bounds
# return __STATE['server'].submit(function, args, \
# depfuncs=(wrap_function,wrap_bounds), \
## modules=("mystic","numpy"), \
# globals=globals())
# p_function = dill_wrapper(function)
# return __STATE['server'].submit(p_function, args, globals=globals())
#print __STATE['server'].get_ncpus(), "local workers" #XXX: debug
return __STATE['server'].submit(function, args, globals=globals())
# Merge all the passed-in argument lists together. This is done
# that way because as with the map() function, at least one list
# is required but the rest are optional.
a = [sequence]
a.extend(sequences)
# Set the requested level of multi-processing
#__STATE['server'].set_ncpus(processes or 'autodetect') # never processes=0
if processes == None:
__STATE['server'].set_ncpus('autodetect')
else:
__STATE['server'].set_ncpus(processes) # allow processes=0
#print "running with", __STATE['server'].get_ncpus(), "local workers" #XXX: debug
# First, submit all the jobs. Then harvest the results as they
# come available.
return (subproc() for subproc in map(submit, *a))
