def main():
args = parse_cli()
with open(args.configure, 'r') as f:
config = yaml.load(f)
for clf_name, db_collection in config['experiment_name'].items():
clf = user.clfs[clf_name]
w = Worker(config['project_name'], db_collection,
objective_func, host=config['MongoDB']['host'], port=config['MongoDB']['port'],
loop_forever=args.loop)
w.start_worker(clf=clf, X=user.X, y=user.y)
def main():
args = parse_cli()
config = yread(args.conf)
w = Worker(config['project_name'],
config['experiment_name'],
objective_random_sleep,
host=config['MongoDB']['host'],
port=config['MongoDB']['port'],
loop_forever=args.loop)
# do the tasks
w.start_worker()
def main():
args = parse_cli()
with open(args.conf, 'r') as f:
config = yaml.load(f)
for clf_name, db_collection in config['experiment_name'].items():
clf = user.clfs[clf_name]
w = Worker(config['project_name'],
db_collection,
user.objective_func,
host=config['MongoDB']['host'],
port=config['MongoDB']['port'],
loop_forever=False)
w.start_worker(clf=clf, X=user.X, y=user.y)
def main():
args = parse_cli()
with open(args.conf, 'r') as f:
config = yaml.load(f)
for clf_name, db_collection in config['experiment_name'].items():
clf = user.clfs[clf_name]
# call worker with loop_forever = False, for multi-experiment mode.
# In this mode this worker will exit after all jobs in the current
# experiment have been completed.
w = Worker(config['project_name'], db_collection,
user.objective, host=config['MongoDB']['host'],
port=config['MongoDB']['port'],
loop_forever=False)
w.start_worker(clf=clf, X=user.X, y=user.y)
def main():
args = parse_cli()
with open(args.conf, 'r') as f:
config = yaml.load(f)
for clf_name, db_collection in config['experiment_name'].items():
clf = user.clfs[clf_name]
# call worker with loop_forever = True, for single-experiment mode.
# In this mode this worker will not exit, and will continually check
# for new jobs in the current experiment (db_collection).
# Single-experiment mode is therefore not (yet) appropriate for
# running jobs from multiple different experiments.
w = Worker(config['project_name'], db_collection,
user.objective, host=config['MongoDB']['host'],
port=config['MongoDB']['port'],
loop_forever=True)
w.start_worker(clf=clf, X=user.X, y=user.y)
def main():
args = parse_cli()
with open(args.configure, "r") as f:
config = yaml.load(f)
for clf_name, db_collection in config["experiment_name"].items():
# clf = user.clfs[clf_name]
# w = Worker(config['project_name'], db_collection,
# objective_func, host=config['MongoDB']['host'], port=config['MongoDB']['port'],
# loop_forever=args.loop)
w = Worker(
config["project_name"],
db_collection,
objective_func,
host=config["MongoDB"]["host"],
port=config["MongoDB"]["port"],
loop_forever=False,
)
w.start_worker()
logger.info('Running in multi-experiment mode (reads multiple experiment names from *.experiment files)...')
loop = False
# Loop over experiment files (generated by coordinator.py)
# read each one: extract the project and experiment names,
# then launch a Worker instance on the experiment.
for expt_file in expt_files:
logger.info('reading {} experiment definition file'.format(expt_file))
dat = load_experiment_file(expt_file)
project_name = dat['project']
experiment_name = dat['experiment']
# we stored the name of the classifier in the experiment name
# delimited by a colon: get that name and use it to decide which
# clf object to pass to Worker.
clf_name = experiment_name.split(':')[0]
clf = user.clfs[clf_name]
logger.info("starting worker on project {}, experiment {}".format(project_name, experiment_name))
# Note: we use loop_forever=False here since we want this worker to compute on the number
# of (currently) queued jobs in the experiment and then exit, to allow a new Worker object
# to do the next experiment (if there is one).
w = Worker(project_name, experiment_name, clf, user.X, user.y, user.objective,
host=args.host, port=args.port, loop_forever=loop)
w.start_worker()