def testMedianStoppingOnCompleteOnly(self):
rule = MedianStoppingRule(grace_period=0, min_samples_required=1)
t1, t2 = self.basicSetup(rule)
self.assertEqual(
rule.on_trial_result(None, t2, result(100, 0)),
TrialScheduler.CONTINUE)
rule.on_trial_complete(None, t1, result(10, 1000))
self.assertEqual(
rule.on_trial_result(None, t2, result(101, 0)),
TrialScheduler.STOP)
def testMedianStoppingUsesMedian(self):
rule = MedianStoppingRule(grace_period=0, min_samples_required=1)
t1, t2 = self.basicSetup(rule)
rule.on_trial_complete(None, t1, result(10, 1000))
rule.on_trial_complete(None, t2, result(10, 1000))
t3 = Trial("PPO")
self.assertEqual(
rule.on_trial_result(None, t3, result(1, 260)),
TrialScheduler.CONTINUE)
self.assertEqual(
rule.on_trial_result(None, t3, result(2, 260)),
TrialScheduler.STOP)
def testMedianStoppingMinSamples(self):
rule = MedianStoppingRule(grace_period=0, min_samples_required=2)
t1, t2 = self.basicSetup(rule)
rule.on_trial_complete(None, t1, result(10, 1000))
t3 = Trial("PPO")
self.assertEqual(rule.on_trial_result(None, t3, result(3, 10)),
TrialScheduler.CONTINUE)
rule.on_trial_complete(None, t2, result(10, 1000))
self.assertEqual(rule.on_trial_result(None, t3, result(3, 10)),
TrialScheduler.STOP)
def testMedianStoppingConstantPerf(self):
rule = MedianStoppingRule(grace_period=0, min_samples_required=1)
t1, t2 = self.basicSetup(rule)
rule.on_trial_complete(None, t1, result(10, 1000))
self.assertEqual(rule.on_trial_result(None, t2, result(5, 450)),
TrialScheduler.CONTINUE)
self.assertEqual(rule.on_trial_result(None, t2, result(6, 0)),
TrialScheduler.CONTINUE)
self.assertEqual(rule.on_trial_result(None, t2, result(10, 450)),
TrialScheduler.STOP)
def testMedianStoppingSoftStop(self):
rule = MedianStoppingRule(grace_period=0,
min_samples_required=1,
hard_stop=False)
t1, t2 = self.basicSetup(rule)
rule.on_trial_complete(None, t1, result(10, 1000))
rule.on_trial_complete(None, t2, result(10, 1000))
t3 = Trial("PPO")
self.assertEqual(rule.on_trial_result(None, t3, result(1, 260)),
TrialScheduler.CONTINUE)
self.assertEqual(rule.on_trial_result(None, t3, result(2, 260)),
TrialScheduler.PAUSE)
def main():
args = parse_args()
save_path = args.save_path = os.path.join(args.save_folder, args.arch)
os.makedirs(save_path)
#os.makedirs(save_path, exist_ok=True)
# config
args.logger_file = os.path.join(save_path, 'log_{}.txt'.format(args.cmd))
handlers = [
logging.FileHandler(args.logger_file, mode='w'),
logging.StreamHandler()
]
logging.basicConfig(level=logging.INFO,
datefmt='%m-%d-%y %H:%M',
format='%(asctime)s:%(message)s',
handlers=handlers)
if args.cmd == 'train':
logging.info('start training {}'.format(args.arch))
run_training(args)
elif args.cmd == 'test':
logging.info('start evaluating {} with checkpoints from {}'.format(
args.arch, args.resume))
test_model(args)
elif args.cmd == 'tune':
import ray
import ray.tune as tune
from ray.tune import Experiment
from ray.tune.median_stopping_rule import MedianStoppingRule
ray.init()
sched = MedianStoppingRule(time_attr="timesteps_total",
reward_attr="neg_mean_loss")
tune.register_trainable(
"run_training",
lambda cfg, reporter: run_training(args, cfg, reporter))
experiment = Experiment(
"train_rl",
"run_training",
trial_resources={"gpu": 1},
config={"alpha": tune.grid_search([0.1, 0.01, 0.001])})
tune.run_experiments(experiment, scheduler=sched, verbose=False)
def testAlternateMetrics(self):
def result2(t, rew):
return TrainingResult(training_iteration=t, neg_mean_loss=rew)
rule = MedianStoppingRule(grace_period=0,
min_samples_required=1,
time_attr='training_iteration',
reward_attr='neg_mean_loss')
t1 = Trial("PPO") # mean is 450, max 900, t_max=10
t2 = Trial("PPO") # mean is 450, max 450, t_max=5
for i in range(10):
self.assertEqual(
rule.on_trial_result(None, t1, result2(i, i * 100)),
TrialScheduler.CONTINUE)
for i in range(5):
self.assertEqual(rule.on_trial_result(None, t2, result2(i, 450)),
TrialScheduler.CONTINUE)
rule.on_trial_complete(None, t1, result2(10, 1000))
self.assertEqual(rule.on_trial_result(None, t2, result2(5, 450)),
TrialScheduler.CONTINUE)
self.assertEqual(rule.on_trial_result(None, t2, result2(6, 0)),
TrialScheduler.CONTINUE)
def testAlternateMetrics(self):
def result2(t, rew):
return TrainingResult(training_iteration=t, neg_mean_loss=rew)
rule = MedianStoppingRule(
grace_period=0, min_samples_required=1,
time_attr='training_iteration', reward_attr='neg_mean_loss')
t1 = Trial("PPO") # mean is 450, max 900, t_max=10
t2 = Trial("PPO") # mean is 450, max 450, t_max=5
for i in range(10):
self.assertEqual(
rule.on_trial_result(None, t1, result2(i, i * 100)),
TrialScheduler.CONTINUE)
for i in range(5):
self.assertEqual(
rule.on_trial_result(None, t2, result2(i, 450)),
TrialScheduler.CONTINUE)
rule.on_trial_complete(None, t1, result2(10, 1000))
self.assertEqual(
rule.on_trial_result(None, t2, result2(5, 450)),
TrialScheduler.CONTINUE)
self.assertEqual(
rule.on_trial_result(None, t2, result2(6, 0)),
TrialScheduler.CONTINUE)
