def __init__(self,
trainable_name,
config=None,
local_dir=DEFAULT_RESULTS_DIR,
experiment_tag=None,
resources=Resources(cpu=1, gpu=0),
stopping_criterion=None,
checkpoint_freq=0,
restore_path=None,
upload_dir=None,
max_failures=0):
"""Initialize a new trial.
The args here take the same meaning as the command line flags defined
in ray.tune.config_parser.
"""
if not _default_registry.contains(TRAINABLE_CLASS, trainable_name):
raise TuneError("Unknown trainable: " + trainable_name)
if stopping_criterion:
for k in stopping_criterion:
if k not in TrainingResult._fields:
raise TuneError(
"Stopping condition key `{}` must be one of {}".format(
k, TrainingResult._fields))
# Trial config
self.trainable_name = trainable_name
self.config = config or {}
self.local_dir = local_dir
self.experiment_tag = experiment_tag
self.resources = resources
self.stopping_criterion = stopping_criterion or {}
self.checkpoint_freq = checkpoint_freq
self.upload_dir = upload_dir
self.verbose = True
self.max_failures = max_failures
# Local trial state that is updated during the run
self.last_result = None
self._checkpoint_path = restore_path
self._checkpoint_obj = None
self.runner = None
self.status = Trial.PENDING
self.location = None
self.logdir = None
self.result_logger = None
self.last_debug = 0
self.trial_id = binary_to_hex(random_string())[:8]
self.error_file = None
self.num_failures = 0
def __init__(
self, trainable_name, config=None, local_dir=DEFAULT_RESULTS_DIR,
experiment_tag=None, resources=Resources(cpu=1, gpu=0),
stopping_criterion=None, checkpoint_freq=0,
restore_path=None, upload_dir=None, max_failures=0):
"""Initialize a new trial.
The args here take the same meaning as the command line flags defined
in ray.tune.config_parser.
"""
if not _default_registry.contains(
TRAINABLE_CLASS, trainable_name):
raise TuneError("Unknown trainable: " + trainable_name)
if stopping_criterion:
for k in stopping_criterion:
if k not in TrainingResult._fields:
raise TuneError(
"Stopping condition key `{}` must be one of {}".format(
k, TrainingResult._fields))
# Trial config
self.trainable_name = trainable_name
self.config = config or {}
self.local_dir = local_dir
self.experiment_tag = experiment_tag
self.resources = resources
self.stopping_criterion = stopping_criterion or {}
self.checkpoint_freq = checkpoint_freq
self.upload_dir = upload_dir
self.verbose = True
self.max_failures = max_failures
# Local trial state that is updated during the run
self.last_result = None
self._checkpoint_path = restore_path
self._checkpoint_obj = None
self.runner = None
self.status = Trial.PENDING
self.location = None
self.logdir = None
self.result_logger = None
self.last_debug = 0
self.trial_id = binary_to_hex(random_string())[:8]
self.error_file = None
self.num_failures = 0
def __init__(self,
trainable_name,
config={},
local_dir='/tmp/ray',
experiment_tag=None,
resources=Resources(cpu=1, gpu=0),
stopping_criterion={},
checkpoint_freq=0,
restore_path=None,
upload_dir=None):
"""Initialize a new trial.
The args here take the same meaning as the command line flags defined
in ray.tune.config_parser.
"""
if not _default_registry.contains(TRAINABLE_CLASS, trainable_name):
raise TuneError("Unknown trainable: " + trainable_name)
for k in stopping_criterion:
if k not in TrainingResult._fields:
raise TuneError(
"Stopping condition key `{}` must be one of {}".format(
k, TrainingResult._fields))
# Immutable config
self.trainable_name = trainable_name
self.config = config
self.local_dir = local_dir
self.experiment_tag = experiment_tag
self.resources = resources
self.stopping_criterion = stopping_criterion
self.checkpoint_freq = checkpoint_freq
self.upload_dir = upload_dir
# Local trial state that is updated during the run
self.last_result = None
self._checkpoint_path = restore_path
self._checkpoint_obj = None
self.runner = None
self.status = Trial.PENDING
self.location = None
self.logdir = None
self.result_logger = None
