def test_main_worker(trials):
options = FakeOptions(
max_jobs=1,
# XXX: sync this with TempMongo
mongo=as_mongo_str('localhost:22334/foodb'),
reserve_timeout=1,
poll_interval=.5,
workdir=None,
exp_key='foo')
# -- check that it runs
# and that the reserve timeout is respected
main_worker_helper(options, ())
def test_main_worker(trials):
options = FakeOptions(
max_jobs=1,
# XXX: sync this with TempMongo
mongo=as_mongo_str('localhost:22334/foodb'),
reserve_timeout=1,
poll_interval=.5,
workdir=None,
exp_key='foo'
)
# -- check that it runs
# and that the reserve timeout is respected
main_worker_helper(options, ())
#'rsenses_imp_loss': "categorical_crossentropy",
}
if args.action == 'worker':
# run distributed worker
class Options(object):
mongo = args.mongo
exp_key = args.exp_key
last_job_timeout = None
max_consecutive_failures = 2
max_jobs = args.evals #= inf
poll_interval = 300 #= 5 min
reserve_timeout = 3600 #= 1 hour
workdir = None
sys.argv[0] = args.worker_helper
sys.exit(main_worker_helper(Options(), None))
elif args.action == 'optimizer':
# run distributed optimizer
trials = MongoTrials(args.mongo, exp_key=args.exp_key)
best = fmin(optimize_exec.objective, space, trials=trials, algo=tpe.suggest, max_evals=args.evals)
# summary
print
print "evals: {}".format(args.evals)
print "best: {}".format(best)
print "space: {}".format(space_eval(space, best))
elif args.action == 'list_best':
# list distributed evaluation results
best = list_best(args.mongo, exp_key=args.exp_key, space=space)