def main():
# If you wish to start a worker cluster just for this script, you
# can do so with a command like this. See the slurm module for
# more info on the available options.
#
# Note: If you are running multiple cluster-workers scripts, be
# careful with this because only one set of master/workers can be
# running at a time.
#
# This command will launch a cluster of workers using the Slurm
# job manager if it is available, but otherwise will launch workers
# locally to take advantage of multi-core parallelism.
#
# To bypass this auto-detect, use submodule versions: `cw.mp.start()`
# to launch workers locally, or `cw.slurm.start()`. Additional
# arguments (such as which Slurm partition to use) can be passed to
# `cw.start()` and they will be passed along if slurm is available.
cw.start(nworkers=2)
# Set up the client, connecting to the master host.
# If no host is specified (as here), it will will guess which host
# to use (e.g. if slurm is available, it will use `cw.slurm.master_host()`,
# otherwise it will default to `localhost` for MP mode.
client = cw.client.ClientThread(completion)
client.start()
# Submit a bunch of work.
for i in range(10):
# cw.randid() is a convenient utility function for generating
# unique job IDs in case you don't already have natural IDs.
jobid = cw.randid()
print(u'submitting job {} to square {}'.format(jobid, i))
client.submit(jobid, work, i)
# Wait for all of the jobs to finish. This helps ensure that we see
# the results of all of our work before shutting down the
# interpreter.
client.wait()
# If you started the worker cluster programmatically above, you
# can safely shut down the cluster workers here.
#
# This will use the same auto-detection mechanism to determine
# how to shutdown. To explicitly stop a slurm cluster, for example,
# use `cw.slurm.stop()`, which will free up the allocated nodes.
cw.stop()
def main():
# If you wish to start a worker cluster just for this script, you
# can do so with a command like this. See the slurm module for
# more info on the available options.
#
# Note: If you are running multiple cluster-workers scripts, be
# careful with this because only one set of master/workers can be
# running at a time.
#
# This command will launch a cluster of workers using the Slurm
# job manager if it is available, but otherwise will launch workers
# locally to take advantage of multi-core parallelism.
#
# To bypass this auto-detect, use submodule versions: `cw.mp.start()`
# to launch workers locally, or `cw.slurm.start()`. Additional
# arguments (such as which Slurm partition to use) can be passed to
# `cw.start()` and they will be passed along if slurm is available.
cw.start(nworkers=2)
# Set up the client, connecting to the master host.
# If no host is specified (as here), it will will guess which host
# to use (e.g. if slurm is available, it will use `cw.slurm.master_host()`,
# otherwise it will default to `localhost` for MP mode.
client = cw.client.ClientThread(completion)
client.start()
# Submit a bunch of work.
for i in range(10):
# cw.randid() is a convenient utility function for generating
# unique job IDs in case you don't already have natural IDs.
jobid = cw.randid()
print(u'submitting job {} to square {}'.format(jobid, i))
client.submit(jobid, work, i)
# Wait for all of the jobs to finish. This helps ensure that we see
# the results of all of our work before shutting down the
# interpreter.
client.wait()
# If you started the worker cluster programmatically above, you
# can safely shut down the cluster workers here.
#
# This will use the same auto-detection mechanism to determine
# how to shutdown. To explicitly stop a slurm cluster, for example,
# use `cw.slurm.stop()`, which will free up the allocated nodes.
cw.stop()
def nntune_cw(datafn):
import cw.client
import threading
# Map job IDs to topologies.
jobs = {}
jobs_lock = threading.Lock()
# Map topologies to errors.
topo_errors = collections.defaultdict(list)
def completion(jobid, output):
with jobs_lock:
topo = jobs.pop(jobid)
logging.info(u'got result for {}'.format('-'.join(map(str, topo))))
topo_errors[topo].append(output)
# Run jobs.
client = cw.client.ClientThread(completion)
client.start()
for topo in exhaustive_topos():
for i in range(REPS):
jobid = cw.randid()
with jobs_lock:
jobs[jobid] = topo
client.submit(jobid, evaluate, datafn, topo)
logging.info('all jobs submitted')
client.wait()
logging.info('all jobs finished')
# Find best.
min_error = None
min_topo = None
for topo, errors in topo_errors.items():
error = sum(errors) / len(errors)
if min_error is None or error < min_error:
min_error = error
min_topo = topo
print('best:', '-'.join(map(str, min_topo)))
print('error:', min_error)
