def __init__(self, snapshot_config, max_cpus=1):
self._snapshotter = Snapshotter(snapshot_config.snapshot_dir,
snapshot_config.snapshot_mode,
snapshot_config.snapshot_gap)
parallel_sampler.initialize(max_cpus)
seed = get_seed()
if seed is not None:
parallel_sampler.set_seed(seed)
self._has_setup = False
self._plot = False
self._setup_args = None
self._train_args = None
self._stats = ExperimentStats(total_itr=0,
total_env_steps=0,
total_epoch=0,
last_path=None)
self._algo = None
self._env = None
self._policy = None
self._sampler = None
self._plotter = None
self._start_time = None
self._itr_start_time = None
self.step_itr = None
self.step_path = None
def populate_task(self, env, policy, scope=None):
logger.log("Populating workers...")
if singleton_pool.n_parallel > 1:
singleton_pool.run_each(
_worker_populate_task,
[(pickle.dumps(env), pickle.dumps(policy), scope)] *
singleton_pool.n_parallel)
else:
# avoid unnecessary copying
g = parallel_sampler._get_scoped_g(singleton_pool.G, scope)
g.env = env
g.policy = policy
parallel_sampler.set_seed(ext.get_seed())
logger.log("Populated")
def __init__(self, snapshot_config, max_cpus=1):
self._snapshotter = Snapshotter(
snapshot_config.snapshot_dir,
snapshot_config.snapshot_mode,
snapshot_config.snapshot_gap,
)
parallel_sampler.initialize(max_cpus)
seed = get_seed()
if seed is not None:
parallel_sampler.set_seed(seed)
self._has_setup = False
self._plot = False
self._setup_args = None
self._train_args = None
self._stats = ExperimentStats(
total_itr=0, total_env_steps=0, total_epoch=0, last_path=None
)
self._algo = None
self._env = None
self._sampler = None
self._plotter = None
self._start_time = None
self._itr_start_time = None
self.step_itr = None
self.step_path = None
# only used for off-policy algorithms
self.enable_logging = True
self._n_workers = None
self._worker_class = None
self._worker_args = None
def run_experiment(argv):
"""Run experiment.
Args:
argv (list[str]): Command line arguments.
Raises:
BaseException: Propagate any exception in the experiment.
"""
now = datetime.datetime.now(dateutil.tz.tzlocal())
# avoid name clashes when running distributed jobs
rand_id = str(uuid.uuid4())[:5]
timestamp = now.strftime('%Y_%m_%d_%H_%M_%S_%f_%Z')
default_exp_name = 'experiment_%s_%s' % (timestamp, rand_id)
parser = argparse.ArgumentParser()
parser.add_argument(
'--n_parallel',
type=int,
default=1,
help=('Number of parallel workers to perform rollouts. '
"0 => don't start any workers"))
parser.add_argument('--exp_name',
type=str,
default=default_exp_name,
help='Name of the experiment.')
parser.add_argument('--log_dir',
type=str,
default=None,
help='Path to save the log and iteration snapshot.')
parser.add_argument('--snapshot_mode',
type=str,
default='last',
help='Mode to save the snapshot. Can be either "all" '
'(all iterations will be saved), "last" (only '
'the last iteration will be saved), "gap" (every'
'`snapshot_gap` iterations are saved), or "none" '
'(do not save snapshots)')
parser.add_argument('--snapshot_gap',
type=int,
default=1,
help='Gap between snapshot iterations.')
parser.add_argument(
'--resume_from_dir',
type=str,
default=None,
help='Directory of the pickle file to resume experiment from.')
parser.add_argument('--resume_from_epoch',
type=str,
default=None,
help='Index of iteration to restore from. '
'Can be "first", "last" or a number. '
'Not applicable when snapshot_mode="last"')
parser.add_argument('--tabular_log_file',
type=str,
default='progress.csv',
help='Name of the tabular log file (in csv).')
parser.add_argument('--text_log_file',
type=str,
default='debug.log',
help='Name of the text log file (in pure text).')
parser.add_argument('--tensorboard_step_key',
type=str,
default=None,
help='Name of the step key in tensorboard_summary.')
parser.add_argument('--params_log_file',
type=str,
default='params.json',
help='Name of the parameter log file (in json).')
parser.add_argument('--variant_log_file',
type=str,
default='variant.json',
help='Name of the variant log file (in json).')
parser.add_argument('--plot',
type=ast.literal_eval,
default=False,
help='Whether to plot the iteration results')
parser.add_argument(
'--log_tabular_only',
type=ast.literal_eval,
default=False,
help='Print only the tabular log information (in a horizontal format)')
parser.add_argument('--seed',
type=int,
default=None,
help='Random seed for numpy')
parser.add_argument('--args_data',
type=str,
help='Pickled data for objects')
parser.add_argument('--variant_data',
type=str,
help='Pickled data for variant configuration')
args = parser.parse_args(argv[1:])
if args.seed is not None:
deterministic.set_seed(args.seed)
if args.n_parallel > 0:
parallel_sampler.initialize(n_parallel=args.n_parallel)
if args.seed is not None:
parallel_sampler.set_seed(args.seed)
if not args.plot:
garage.plotter.Plotter.disable()
garage.tf.plotter.Plotter.disable()
if args.log_dir is None:
log_dir = os.path.join(os.path.join(os.getcwd(), 'data'),
args.exp_name)
else:
log_dir = args.log_dir
tabular_log_file = os.path.join(log_dir, args.tabular_log_file)
text_log_file = os.path.join(log_dir, args.text_log_file)
params_log_file = os.path.join(log_dir, args.params_log_file)
if args.variant_data is not None:
variant_data = pickle.loads(base64.b64decode(args.variant_data))
variant_log_file = os.path.join(log_dir, args.variant_log_file)
dump_variant(variant_log_file, variant_data)
else:
variant_data = None
log_parameters(params_log_file, args)
logger.add_output(dowel.TextOutput(text_log_file))
logger.add_output(dowel.CsvOutput(tabular_log_file))
logger.add_output(dowel.TensorBoardOutput(log_dir, x_axis='TotalEnvSteps'))
logger.add_output(dowel.StdOutput())
logger.push_prefix('[%s] ' % args.exp_name)
snapshot_config = SnapshotConfig(snapshot_dir=log_dir,
snapshot_mode=args.snapshot_mode,
snapshot_gap=args.snapshot_gap)
method_call = cloudpickle.loads(base64.b64decode(args.args_data))
try:
method_call(snapshot_config, variant_data, args.resume_from_dir,
args.resume_from_epoch)
except BaseException:
children = garage.plotter.Plotter.get_plotters()
children += garage.tf.plotter.Plotter.get_plotters()
if args.n_parallel > 0:
children += [parallel_sampler]
child_proc_shutdown(children)
raise
logger.remove_all()
logger.pop_prefix()
def run_experiment(argv):
default_log_dir = config.LOG_DIR
now = datetime.datetime.now(dateutil.tz.tzlocal())
# avoid name clashes when running distributed jobs
rand_id = str(uuid.uuid4())[:5]
timestamp = now.strftime('%Y_%m_%d_%H_%M_%S_%f_%Z')
default_exp_name = 'experiment_%s_%s' % (timestamp, rand_id)
parser = argparse.ArgumentParser()
parser.add_argument(
'--n_parallel',
type=int,
default=1,
help=("Number of parallel workers to perform rollouts. "
"0 => don't start any workers"))
parser.add_argument(
'--exp_name',
type=str,
default=default_exp_name,
help='Name of the experiment.')
parser.add_argument(
'--log_dir',
type=str,
default=None,
help='Path to save the log and iteration snapshot.')
parser.add_argument(
'--snapshot_mode',
type=str,
default='all',
help='Mode to save the snapshot. Can be either "all" '
'(all iterations will be saved), "last" (only '
'the last iteration will be saved), "gap" (every'
'`snapshot_gap` iterations are saved), or "none" '
'(do not save snapshots)')
parser.add_argument(
'--snapshot_gap',
type=int,
default=1,
help='Gap between snapshot iterations.')
parser.add_argument(
'--tabular_log_file',
type=str,
default='progress.csv',
help='Name of the tabular log file (in csv).')
parser.add_argument(
'--text_log_file',
type=str,
default='debug.log',
help='Name of the text log file (in pure text).')
parser.add_argument(
'--tensorboard_step_key',
type=str,
default=None,
help=("Name of the step key in tensorboard_summary."))
parser.add_argument(
'--params_log_file',
type=str,
default='params.json',
help='Name of the parameter log file (in json).')
parser.add_argument(
'--variant_log_file',
type=str,
default='variant.json',
help='Name of the variant log file (in json).')
parser.add_argument(
'--resume_from',
type=str,
default=None,
help='Name of the pickle file to resume experiment from.')
parser.add_argument(
'--plot',
type=ast.literal_eval,
default=False,
help='Whether to plot the iteration results')
parser.add_argument(
'--log_tabular_only',
type=ast.literal_eval,
default=False,
help='Print only the tabular log information (in a horizontal format)')
parser.add_argument('--seed', type=int, help='Random seed for numpy')
parser.add_argument(
'--args_data', type=str, help='Pickled data for objects')
parser.add_argument(
'--variant_data',
type=str,
help='Pickled data for variant configuration')
parser.add_argument(
'--use_cloudpickle', type=ast.literal_eval, default=False)
args = parser.parse_args(argv[1:])
if args.seed is not None:
set_seed(args.seed)
# SIGINT is blocked for all processes created in parallel_sampler to avoid
# the creation of sleeping and zombie processes.
#
# If the user interrupts run_experiment, there's a chance some processes
# won't die due to a dead lock condition where one of the children in the
# parallel sampler exits without releasing a lock once after it catches
# SIGINT.
#
# Later the parent tries to acquire the same lock to proceed with his
# cleanup, but it remains sleeping waiting for the lock to be released.
# In the meantime, all the process in parallel sampler remain in the zombie
# state since the parent cannot proceed with their clean up.
with mask_signals([signal.SIGINT]):
if args.n_parallel > 0:
parallel_sampler.initialize(n_parallel=args.n_parallel)
if args.seed is not None:
parallel_sampler.set_seed(args.seed)
if not args.plot:
garage.plotter.Plotter.disable()
garage.tf.plotter.Plotter.disable()
if args.log_dir is None:
log_dir = osp.join(default_log_dir, args.exp_name)
else:
log_dir = args.log_dir
tabular_log_file = osp.join(log_dir, args.tabular_log_file)
text_log_file = osp.join(log_dir, args.text_log_file)
params_log_file = osp.join(log_dir, args.params_log_file)
if args.variant_data is not None:
variant_data = pickle.loads(base64.b64decode(args.variant_data))
variant_log_file = osp.join(log_dir, args.variant_log_file)
logger.log_variant(variant_log_file, variant_data)
else:
variant_data = None
if not args.use_cloudpickle:
logger.log_parameters_lite(params_log_file, args)
logger.add_text_output(text_log_file)
logger.add_tabular_output(tabular_log_file)
logger.set_tensorboard_dir(log_dir)
prev_snapshot_dir = logger.get_snapshot_dir()
prev_mode = logger.get_snapshot_mode()
logger.set_snapshot_dir(log_dir)
logger.set_snapshot_mode(args.snapshot_mode)
logger.set_snapshot_gap(args.snapshot_gap)
logger.set_log_tabular_only(args.log_tabular_only)
logger.set_tensorboard_step_key(args.tensorboard_step_key)
logger.push_prefix("[%s] " % args.exp_name)
if args.resume_from is not None:
data = joblib.load(args.resume_from)
assert 'algo' in data
algo = data['algo']
algo.train()
else:
# read from stdin
if args.use_cloudpickle:
import cloudpickle
method_call = cloudpickle.loads(base64.b64decode(args.args_data))
try:
method_call(variant_data)
except BaseException:
children = garage.plotter.Plotter.get_plotters()
children += garage.tf.plotter.Plotter.get_plotters()
if args.n_parallel > 0:
children += [parallel_sampler]
child_proc_shutdown(children)
raise
else:
data = pickle.loads(base64.b64decode(args.args_data))
maybe_iter = concretize(data)
if is_iterable(maybe_iter):
for _ in maybe_iter:
pass
logger.set_snapshot_mode(prev_mode)
logger.set_snapshot_dir(prev_snapshot_dir)
logger.remove_tabular_output(tabular_log_file)
logger.remove_text_output(text_log_file)
logger.pop_prefix()
def run_experiment(argv):
now = datetime.datetime.now(dateutil.tz.tzlocal())
# avoid name clashes when running distributed jobs
rand_id = str(uuid.uuid4())[:5]
timestamp = now.strftime('%Y_%m_%d_%H_%M_%S_%f_%Z')
default_exp_name = 'experiment_%s_%s' % (timestamp, rand_id)
parser = argparse.ArgumentParser()
parser.add_argument(
'--n_parallel',
type=int,
default=1,
help=('Number of parallel workers to perform rollouts. '
"0 => don't start any workers"))
parser.add_argument(
'--exp_name',
type=str,
default=default_exp_name,
help='Name of the experiment.')
parser.add_argument(
'--log_dir',
type=str,
default=None,
help='Path to save the log and iteration snapshot.')
parser.add_argument(
'--snapshot_mode',
type=str,
default='last',
help='Mode to save the snapshot. Can be either "all" '
'(all iterations will be saved), "last" (only '
'the last iteration will be saved), "gap" (every'
'`snapshot_gap` iterations are saved), or "none" '
'(do not save snapshots)')
parser.add_argument(
'--snapshot_gap',
type=int,
default=1,
help='Gap between snapshot iterations.')
parser.add_argument(
'--resume_from_dir',
type=str,
default=None,
help='Directory of the pickle file to resume experiment from.')
parser.add_argument(
'--resume_from_epoch',
type=str,
default=None,
help='Index of iteration to restore from. '
'Can be "first", "last" or a number. '
'Not applicable when snapshot_mode="last"')
parser.add_argument(
'--tabular_log_file',
type=str,
default='progress.csv',
help='Name of the tabular log file (in csv).')
parser.add_argument(
'--text_log_file',
type=str,
default='debug.log',
help='Name of the text log file (in pure text).')
parser.add_argument(
'--tensorboard_step_key',
type=str,
default=None,
help='Name of the step key in tensorboard_summary.')
parser.add_argument(
'--params_log_file',
type=str,
default='params.json',
help='Name of the parameter log file (in json).')
parser.add_argument(
'--variant_log_file',
type=str,
default='variant.json',
help='Name of the variant log file (in json).')
parser.add_argument(
'--plot',
type=ast.literal_eval,
default=False,
help='Whether to plot the iteration results')
parser.add_argument(
'--log_tabular_only',
type=ast.literal_eval,
default=False,
help='Print only the tabular log information (in a horizontal format)')
parser.add_argument(
'--seed', type=int, default=None, help='Random seed for numpy')
parser.add_argument(
'--args_data', type=str, help='Pickled data for objects')
parser.add_argument(
'--variant_data',
type=str,
help='Pickled data for variant configuration')
args = parser.parse_args(argv[1:])
if args.seed is not None:
deterministic.set_seed(args.seed)
# SIGINT is blocked for all processes created in parallel_sampler to avoid
# the creation of sleeping and zombie processes.
#
# If the user interrupts run_experiment, there's a chance some processes
# won't die due to a dead lock condition where one of the children in the
# parallel sampler exits without releasing a lock once after it catches
# SIGINT.
#
# Later the parent tries to acquire the same lock to proceed with his
# cleanup, but it remains sleeping waiting for the lock to be released.
# In the meantime, all the process in parallel sampler remain in the zombie
# state since the parent cannot proceed with their clean up.
with mask_signals([signal.SIGINT]):
if args.n_parallel > 0:
parallel_sampler.initialize(n_parallel=args.n_parallel)
if args.seed is not None:
parallel_sampler.set_seed(args.seed)
if not args.plot:
garage.plotter.Plotter.disable()
garage.tf.plotter.Plotter.disable()
if args.log_dir is None:
log_dir = os.path.join(
os.path.join(os.getcwd(), 'data'), args.exp_name)
else:
log_dir = args.log_dir
tabular_log_file = os.path.join(log_dir, args.tabular_log_file)
text_log_file = os.path.join(log_dir, args.text_log_file)
params_log_file = os.path.join(log_dir, args.params_log_file)
if args.variant_data is not None:
variant_data = pickle.loads(base64.b64decode(args.variant_data))
variant_log_file = os.path.join(log_dir, args.variant_log_file)
dump_variant(variant_log_file, variant_data)
else:
variant_data = None
log_parameters(params_log_file, args)
logger.add_output(dowel.TextOutput(text_log_file))
logger.add_output(dowel.CsvOutput(tabular_log_file))
logger.add_output(dowel.TensorBoardOutput(log_dir))
logger.add_output(dowel.StdOutput())
logger.push_prefix('[%s] ' % args.exp_name)
snapshot_config = SnapshotConfig(
snapshot_dir=log_dir,
snapshot_mode=args.snapshot_mode,
snapshot_gap=args.snapshot_gap)
method_call = pickle.loads(base64.b64decode(args.args_data))
try:
method_call(snapshot_config, variant_data, args.resume_from_dir,
args.resume_from_epoch)
except BaseException:
children = garage.plotter.Plotter.get_plotters()
children += garage.tf.plotter.Plotter.get_plotters()
if args.n_parallel > 0:
children += [parallel_sampler]
child_proc_shutdown(children)
raise
logger.remove_all()
logger.pop_prefix()
def run_experiment(argv):
default_log_dir = config.LOG_DIR
now = datetime.datetime.now(dateutil.tz.tzlocal())
# avoid name clashes when running distributed jobs
rand_id = str(uuid.uuid4())[:5]
timestamp = now.strftime('%Y_%m_%d_%H_%M_%S_%f_%Z')
default_exp_name = 'experiment_%s_%s' % (timestamp, rand_id)
parser = argparse.ArgumentParser()
parser.add_argument(
'--n_parallel',
type=int,
default=1,
help=("Number of parallel workers to perform rollouts. "
"0 => don't start any workers"))
parser.add_argument(
'--exp_name',
type=str,
default=default_exp_name,
help='Name of the experiment.')
parser.add_argument(
'--log_dir',
type=str,
default=None,
help='Path to save the log and iteration snapshot.')
parser.add_argument(
'--snapshot_mode',
type=str,
default='all',
help='Mode to save the snapshot. Can be either "all" '
'(all iterations will be saved), "last" (only '
'the last iteration will be saved), "gap" (every'
'`snapshot_gap` iterations are saved), or "none" '
'(do not save snapshots)')
parser.add_argument(
'--snapshot_gap',
type=int,
default=1,
help='Gap between snapshot iterations.')
parser.add_argument(
'--tabular_log_file',
type=str,
default='progress.csv',
help='Name of the tabular log file (in csv).')
parser.add_argument(
'--text_log_file',
type=str,
default='debug.log',
help='Name of the text log file (in pure text).')
parser.add_argument(
'--tensorboard_step_key',
type=str,
default=None,
help=("Name of the step key in tensorboard_summary."))
parser.add_argument(
'--params_log_file',
type=str,
default='params.json',
help='Name of the parameter log file (in json).')
parser.add_argument(
'--variant_log_file',
type=str,
default='variant.json',
help='Name of the variant log file (in json).')
parser.add_argument(
'--resume_from',
type=str,
default=None,
help='Name of the pickle file to resume experiment from.')
parser.add_argument(
'--plot',
type=ast.literal_eval,
default=False,
help='Whether to plot the iteration results')
parser.add_argument(
'--log_tabular_only',
type=ast.literal_eval,
default=False,
help='Print only the tabular log information (in a horizontal format)')
parser.add_argument('--seed', type=int, help='Random seed for numpy')
parser.add_argument(
'--args_data', type=str, help='Pickled data for stub objects')
parser.add_argument(
'--variant_data',
type=str,
help='Pickled data for variant configuration')
parser.add_argument(
'--use_cloudpickle', type=ast.literal_eval, default=False)
args = parser.parse_args(argv[1:])
assert (os.environ.get("JOBLIB_START_METHOD", None) == "forkserver")
if args.seed is not None:
set_seed(args.seed)
if args.n_parallel > 0:
from garage.sampler import parallel_sampler
parallel_sampler.initialize(n_parallel=args.n_parallel)
if args.seed is not None:
parallel_sampler.set_seed(args.seed)
if not args.plot:
garage.plotter.Plotter.disable()
garage.tf.plotter.Plotter.disable()
if args.log_dir is None:
log_dir = osp.join(default_log_dir, args.exp_name)
else:
log_dir = args.log_dir
tabular_log_file = osp.join(log_dir, args.tabular_log_file)
text_log_file = osp.join(log_dir, args.text_log_file)
params_log_file = osp.join(log_dir, args.params_log_file)
if args.variant_data is not None:
variant_data = pickle.loads(base64.b64decode(args.variant_data))
variant_log_file = osp.join(log_dir, args.variant_log_file)
logger.log_variant(variant_log_file, variant_data)
else:
variant_data = None
if not args.use_cloudpickle:
logger.log_parameters_lite(params_log_file, args)
logger.add_text_output(text_log_file)
logger.add_tabular_output(tabular_log_file)
logger.set_tensorboard_dir(log_dir)
prev_snapshot_dir = logger.get_snapshot_dir()
prev_mode = logger.get_snapshot_mode()
logger.set_snapshot_dir(log_dir)
logger.set_snapshot_mode(args.snapshot_mode)
logger.set_snapshot_gap(args.snapshot_gap)
logger.set_log_tabular_only(args.log_tabular_only)
logger.set_tensorboard_step_key(args.tensorboard_step_key)
logger.push_prefix("[%s] " % args.exp_name)
if args.resume_from is not None:
data = joblib.load(args.resume_from)
assert 'algo' in data
algo = data['algo']
algo.train()
else:
# read from stdin
if args.use_cloudpickle:
import cloudpickle
method_call = cloudpickle.loads(base64.b64decode(args.args_data))
try:
method_call(variant_data)
except BaseException:
if args.n_parallel > 0:
parallel_sampler.terminate()
raise
else:
data = pickle.loads(base64.b64decode(args.args_data))
maybe_iter = concretize(data)
if is_iterable(maybe_iter):
for _ in maybe_iter:
pass
logger.set_snapshot_mode(prev_mode)
logger.set_snapshot_dir(prev_snapshot_dir)
logger.remove_tabular_output(tabular_log_file)
logger.remove_text_output(text_log_file)
logger.pop_prefix()
