def __init__(self,
trainable_name,
config=None,
trial_id=None,
local_dir=DEFAULT_RESULTS_DIR,
experiment_tag="",
resources=None,
stopping_criterion=None,
checkpoint_freq=0,
checkpoint_at_end=False,
export_formats=None,
restore_path=None,
upload_dir=None,
trial_name_creator=None,
custom_loggers=None,
sync_function=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.
"""
Trial._registration_check(trainable_name)
# Trial config
self.trainable_name = trainable_name
self.config = config or {}
self.local_dir = os.path.expanduser(local_dir)
self.experiment_tag = experiment_tag
self.resources = (
resources
or self._get_trainable_cls().default_resource_request(self.config))
self.stopping_criterion = stopping_criterion or {}
self.upload_dir = upload_dir
self.custom_loggers = custom_loggers
self.sync_function = sync_function
validate_sync_function(sync_function)
self.verbose = True
self.max_failures = max_failures
# Local trial state that is updated during the run
self.last_result = None
self.last_update_time = -float("inf")
self.checkpoint_freq = checkpoint_freq
self.checkpoint_at_end = checkpoint_at_end
self._checkpoint = Checkpoint(storage=Checkpoint.DISK,
value=restore_path)
self.export_formats = export_formats
self.status = Trial.PENDING
self.logdir = None
self.runner = None
self.result_logger = None
self.last_debug = 0
self.trial_id = Trial.generate_id() if trial_id is None else trial_id
self.error_file = None
self.num_failures = 0
self.trial_name = None
if trial_name_creator:
self.trial_name = trial_name_creator(self)
def __init__(self,
trainable_name,
config=None,
trial_id=None,
local_dir=DEFAULT_RESULTS_DIR,
experiment_tag="",
resources=None,
stopping_criterion=None,
checkpoint_freq=0,
checkpoint_at_end=False,
restore_path=None,
upload_dir=None,
trial_name_creator=None,
custom_loggers=None,
sync_function=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.
"""
Trial._registration_check(trainable_name)
# Trial config
self.trainable_name = trainable_name
self.config = config or {}
self.local_dir = os.path.expanduser(local_dir)
self.experiment_tag = experiment_tag
self.resources = (
resources
or self._get_trainable_cls().default_resource_request(self.config))
self.stopping_criterion = stopping_criterion or {}
self.upload_dir = upload_dir
self.custom_loggers = custom_loggers
self.sync_function = sync_function
validate_sync_function(sync_function)
self.verbose = True
self.max_failures = max_failures
# Local trial state that is updated during the run
self.last_result = None
self.last_update_time = -float("inf")
self.checkpoint_freq = checkpoint_freq
self.checkpoint_at_end = checkpoint_at_end
self._checkpoint = Checkpoint(
storage=Checkpoint.DISK, value=restore_path)
self.status = Trial.PENDING
self.location = None
self.logdir = None
self.result_logger = None
self.last_debug = 0
self.trial_id = Trial.generate_id() if trial_id is None else trial_id
self.error_file = None
self.num_failures = 0
self.trial_name = None
if trial_name_creator:
self.trial_name = trial_name_creator(self)
def __init__(self,
name,
run,
stop=None,
config=None,
resources_per_trial=None,
num_samples=1,
local_dir=None,
upload_dir=None,
trial_name_creator=None,
custom_loggers=None,
sync_function=None,
checkpoint_freq=0,
checkpoint_at_end=False,
max_failures=3,
restore=None,
repeat=None,
trial_resources=None):
validate_sync_function(sync_function)
if sync_function:
assert upload_dir, "Need `upload_dir` if sync_function given."
if repeat:
_raise_deprecation_note("repeat", "num_samples", soft=False)
if trial_resources:
_raise_deprecation_note("trial_resources",
"resources_per_trial",
soft=True)
resources_per_trial = trial_resources
spec = {
"run": self._register_if_needed(run),
"stop": stop or {},
"config": config or {},
"resources_per_trial": resources_per_trial,
"num_samples": num_samples,
"local_dir": local_dir or DEFAULT_RESULTS_DIR,
"upload_dir": upload_dir or "", # argparse converts None to "null"
"trial_name_creator": trial_name_creator,
"custom_loggers": custom_loggers,
"sync_function": sync_function or "", # See `upload_dir`.
"checkpoint_freq": checkpoint_freq,
"checkpoint_at_end": checkpoint_at_end,
"max_failures": max_failures,
"restore": restore
}
self.name = name
self.spec = spec
def __init__(self,
trainable_name,
config=None,
trial_id=None,
local_dir=DEFAULT_RESULTS_DIR,
experiment_tag="",
resources=None,
stopping_criterion=None,
checkpoint_freq=0,
checkpoint_at_end=False,
keep_checkpoints_num=None,
checkpoint_score_attr="",
export_formats=None,
restore_path=None,
upload_dir=None,
trial_name_creator=None,
loggers=None,
sync_function=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.
"""
Trial._registration_check(trainable_name)
# Trial config
self.trainable_name = trainable_name
self.config = config or {}
self.local_dir = os.path.expanduser(local_dir)
self.experiment_tag = experiment_tag
self.resources = (
resources
or self._get_trainable_cls().default_resource_request(self.config))
self.stopping_criterion = stopping_criterion or {}
self.upload_dir = upload_dir
self.loggers = loggers
self.sync_function = sync_function
validate_sync_function(sync_function)
self.verbose = True
self.max_failures = max_failures
# Local trial state that is updated during the run
self.last_result = {}
self.last_update_time = -float("inf")
self.checkpoint_freq = checkpoint_freq
self.checkpoint_at_end = checkpoint_at_end
self.history = []
self.keep_checkpoints_num = keep_checkpoints_num
self._cmp_greater = not checkpoint_score_attr.startswith("min-")
self.best_checkpoint_attr_value = -float("inf") \
if self._cmp_greater else float("inf")
# Strip off "min-" from checkpoint attribute
self.checkpoint_score_attr = checkpoint_score_attr \
if self._cmp_greater else checkpoint_score_attr[4:]
self._checkpoint = Checkpoint(storage=Checkpoint.DISK,
value=restore_path)
self.export_formats = export_formats
self.status = Trial.PENDING
self.logdir = None
self.runner = None
self.result_logger = None
self.last_debug = 0
self.trial_id = Trial.generate_id() if trial_id is None else trial_id
self.error_file = None
self.num_failures = 0
self.custom_trial_name = None
# AutoML fields
self.results = None
self.best_result = None
self.param_config = None
self.extra_arg = None
self._nonjson_fields = [
"_checkpoint",
"loggers",
"sync_function",
"results",
"best_result",
"param_config",
"extra_arg",
]
if trial_name_creator:
self.custom_trial_name = trial_name_creator(self)
def __init__(self,
trainable_name,
config=None,
trial_id=None,
local_dir=DEFAULT_RESULTS_DIR,
experiment_tag="",
resources=None,
stopping_criterion=None,
checkpoint_freq=0,
checkpoint_at_end=False,
keep_checkpoints_num=None,
checkpoint_score_attr="",
export_formats=None,
restore_path=None,
upload_dir=None,
trial_name_creator=None,
loggers=None,
sync_function=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.
"""
Trial._registration_check(trainable_name)
# Trial config
self.trainable_name = trainable_name
self.config = config or {}
self.local_dir = local_dir # This remains unexpanded for syncing.
self.experiment_tag = experiment_tag
trainable_cls = self._get_trainable_cls()
if trainable_cls and hasattr(trainable_cls,
"default_resource_request"):
default_resources = trainable_cls.default_resource_request(
self.config)
if default_resources:
if resources:
raise ValueError(
"Resources for {} have been automatically set to {} "
"by its `default_resource_request()` method. Please "
"clear the `resources_per_trial` option.".format(
trainable_cls, default_resources))
resources = default_resources
self.resources = resources or Resources(cpu=1, gpu=0)
self.stopping_criterion = stopping_criterion or {}
self.upload_dir = upload_dir
self.loggers = loggers
self.sync_function = sync_function
validate_sync_function(sync_function)
self.verbose = True
self.max_failures = max_failures
# Local trial state that is updated during the run
self.last_result = {}
self.last_update_time = -float("inf")
self.checkpoint_freq = checkpoint_freq
self.checkpoint_at_end = checkpoint_at_end
self.history = []
self.keep_checkpoints_num = keep_checkpoints_num
self._cmp_greater = not checkpoint_score_attr.startswith("min-")
self.best_checkpoint_attr_value = -float("inf") \
if self._cmp_greater else float("inf")
# Strip off "min-" from checkpoint attribute
self.checkpoint_score_attr = checkpoint_score_attr \
if self._cmp_greater else checkpoint_score_attr[4:]
self._checkpoint = Checkpoint(storage=Checkpoint.DISK,
value=_find_newest_ckpt(restore_path))
self.export_formats = export_formats
self.status = Trial.PENDING
self.logdir = None
self.runner = None
self.result_logger = None
self.last_debug = 0
self.trial_id = Trial.generate_id() if trial_id is None else trial_id
self.error_file = None
self.num_failures = 0
self.custom_trial_name = None
# AutoML fields
self.results = None
self.best_result = None
self.param_config = None
self.extra_arg = None
self._nonjson_fields = [
"_checkpoint",
"loggers",
"sync_function",
"results",
"best_result",
"param_config",
"extra_arg",
]
if trial_name_creator:
self.custom_trial_name = trial_name_creator(self)