import multiprocessing
import socket

def minimum_mem(limit):
    """limit based on total system memory"""
    import sys
    if sys.platform == 'darwin': # or BSD in general?
        from subprocess import check_output
        n = int(check_output(['sysctl', '-n', 'hw.memsize']))
    else: # linux
        with open("/proc/meminfo") as f:
            for line in f:
                if line.startswith("MemTotal"):
                    n = 1024 * int(line.split()[1])
                    break
    return n >= limit

kB = 1024.
MB = 1024 * kB
GB = 1024 * MB

def get_mem():
    import sys
    if sys.platform == 'darwin': # or BSD in general?
        from subprocess import check_output
        n = int(check_output(['sysctl', '-n', 'hw.memsize']))
    else: # linux
        with open("/proc/meminfo") as f:
            for line in f:
                if line.startswith("MemTotal"):
                    n = 1024 * int(line.split()[1])
                    break
    return n

def minimum_cpu(limit):
    """limit based on total CPUs"""
    import multiprocessing
    return multiprocessing.cpu_count() >= limit

from IPython import parallel
rc = parallel.Client(profile='sync')
dv = rc[:]
view = rc.load_balanced_view()

hostnames = dv.apply_async(socket.gethostname).get_dict()
hostnames

meminfo = dv.apply_async(get_mem).get_dict()
meminfo

ncpus = dv.apply_async(multiprocessing.cpu_count).get_dict()
ncpus

for eid in rc.ids:
    print "Engine %i [%6s]: %4.1fGB RAM, %2i CPUs" % (eid, hostnames[eid], meminfo[eid] / GB, ncpus[eid])

@parallel.depend(minimum_mem, 8 * GB)
def big_mem_task(n):
    import os, socket
    return "big", socket.gethostname(), os.getpid(), n


amr = view.map_async(big_mem_task, range(10))
for r in amr:
    for item in r:
        print item,
    print


@parallel.depend(minimum_mem, 3 * GB)
def medium_mem_task(n):
    import os, socket
    return "medium", socket.gethostname(), os.getpid(), n

amr = view.map_async(medium_mem_task, range(10))
for r in amr:
    for item in r:
        print item,
    print
        


@parallel.depend(minimum_cpu, 4)
def big_cpu_task(n):
    import os, socket
    return socket.gethostname(), os.getpid(), n

amr = view.map_async(big_cpu_task, range(10))
for r in amr:
    for item in r:
        print item,
    print