def run_experiments(experiments,
scheduler=None,
with_server=False,
server_port=TuneServer.DEFAULT_PORT,
verbose=True):
"""Tunes experiments.
Args:
experiments (Experiment | list | dict): Experiments to run.
scheduler (TrialScheduler): Scheduler for executing
the experiment. Choose among FIFO (default), MedianStopping,
AsyncHyperBand, HyperBand, or HyperOpt.
with_server (bool): Starts a background Tune server. Needed for
using the Client API.
server_port (int): Port number for launching TuneServer.
verbose (bool): How much output should be printed for each trial.
"""
if scheduler is None:
scheduler = FIFOScheduler()
runner = TrialRunner(
scheduler,
launch_web_server=with_server,
server_port=server_port,
verbose=verbose)
exp_list = experiments
if isinstance(experiments, Experiment):
exp_list = [experiments]
elif type(experiments) is dict:
exp_list = [
Experiment.from_json(name, spec)
for name, spec in experiments.items()
]
if (type(exp_list) is list
and all(isinstance(exp, Experiment) for exp in exp_list)):
for experiment in exp_list:
scheduler.add_experiment(experiment, runner)
else:
raise TuneError("Invalid argument: {}".format(experiments))
print(runner.debug_string(max_debug=99999))
last_debug = 0
while not runner.is_finished():
runner.step()
if time.time() - last_debug > DEBUG_PRINT_INTERVAL:
print(runner.debug_string())
last_debug = time.time()
print(runner.debug_string(max_debug=99999))
for trial in runner.get_trials():
# TODO(rliaw): What about errored?
if trial.status != Trial.TERMINATED:
raise TuneError("Trial did not complete", trial)
wait_for_log_sync()
return runner.get_trials()
def run_experiments(experiments,
scheduler=None,
with_server=False,
server_port=TuneServer.DEFAULT_PORT,
verbose=True,
queue_trials=False):
"""Tunes experiments.
Args:
experiments (Experiment | list | dict): Experiments to run.
scheduler (TrialScheduler): Scheduler for executing
the experiment. Choose among FIFO (default), MedianStopping,
AsyncHyperBand, HyperBand, or HyperOpt.
with_server (bool): Starts a background Tune server. Needed for
using the Client API.
server_port (int): Port number for launching TuneServer.
verbose (bool): How much output should be printed for each trial.
queue_trials (bool): Whether to queue trials when the cluster does
not currently have enough resources to launch one. This should
be set to True when running on an autoscaling cluster to enable
automatic scale-up.
Returns:
List of Trial objects, holding data for each executed trial.
"""
if scheduler is None:
scheduler = FIFOScheduler()
runner = TrialRunner(
scheduler,
launch_web_server=with_server,
server_port=server_port,
verbose=verbose,
queue_trials=queue_trials)
exp_list = experiments
if isinstance(experiments, Experiment):
exp_list = [experiments]
elif type(experiments) is dict:
exp_list = [
Experiment.from_json(name, spec)
for name, spec in experiments.items()
]
if (type(exp_list) is list
and all(isinstance(exp, Experiment) for exp in exp_list)):
for experiment in exp_list:
scheduler.add_experiment(experiment, runner)
else:
raise TuneError("Invalid argument: {}".format(experiments))
print(runner.debug_string(max_debug=99999))
last_debug = 0
while not runner.is_finished():
runner.step()
if time.time() - last_debug > DEBUG_PRINT_INTERVAL:
print(runner.debug_string())
last_debug = time.time()
print(runner.debug_string(max_debug=99999))
errored_trials = []
for trial in runner.get_trials():
if trial.status != Trial.TERMINATED:
errored_trials += [trial]
if errored_trials:
raise TuneError("Trials did not complete", errored_trials)
wait_for_log_sync()
return runner.get_trials()
