def __init__(self,
time_attr: str = "time_total_s",
metric: Optional[str] = None,
mode: Optional[str] = None,
perturbation_interval: float = 60.0,
hyperparam_bounds: Dict = None,
quantile_fraction: float = 0.25,
log_config: bool = True,
require_attrs: bool = True,
synch: bool = False):
gpy_available, sklearn_available = import_pb2_dependencies()
if not gpy_available:
raise RuntimeError("Please install GPy to use PB2.")
if not sklearn_available:
raise RuntimeError("Please install scikit-learn to use PB2.")
hyperparam_bounds = hyperparam_bounds or {}
for value in hyperparam_bounds.values():
if not isinstance(value, (list, tuple)) or len(value) != 2:
raise ValueError("`hyperparam_bounds` values must either be "
"a list or tuple of size 2, but got {} "
"instead".format(value))
if not hyperparam_bounds:
raise TuneError("`hyperparam_bounds` must be specified to use "
"PB2 scheduler.")
super(PB2, self).__init__(time_attr=time_attr,
metric=metric,
mode=mode,
perturbation_interval=perturbation_interval,
hyperparam_mutations=hyperparam_bounds,
quantile_fraction=quantile_fraction,
resample_probability=0,
custom_explore_fn=explore,
log_config=log_config,
require_attrs=require_attrs,
synch=synch)
self.last_exploration_time = 0 # when we last explored
self.data = pd.DataFrame()
self._hyperparam_bounds = hyperparam_bounds
# Current = trials running that have already re-started after reaching
# the checkpoint. When exploring we care if these trials
# are already in or scheduled to be in the next round.
self.current = None
def step(self):
"""Runs one step of the trial event loop.
Callers should typically run this method repeatedly in a loop. They
may inspect or modify the runner's state in between calls to step().
"""
if self._can_launch_more():
self._launch_trial()
elif self._running:
self._process_events()
else:
for trial in self._trials:
if trial.status == Trial.PENDING:
if not self.has_resources(trial.resources):
raise TuneError(
"Insufficient cluster resources to launch trial",
(trial.resources, self._avail_resources))
elif trial.status == Trial.PAUSED:
raise TuneError(
"There are paused trials, but no more pending "
"trials with sufficient resources.")
raise TuneError("Called step when all trials finished?")
def _sync_trial_checkpoint(self, trial: "Trial",
checkpoint: _TrackedCheckpoint):
if checkpoint.storage_mode == CheckpointStorage.MEMORY:
return
trial_syncer = self._get_trial_syncer(trial)
# If the sync_function is False, syncing to driver is disabled.
# In every other case (valid values include None, True Callable,
# NodeSyncer) syncing to driver is enabled.
if trial.sync_on_checkpoint and self._sync_function is not False:
try:
# Wait for any other syncs to finish. We need to sync again
# after this to handle checkpoints taken mid-sync.
trial_syncer.wait()
except TuneError as e:
# Errors occurring during this wait are not fatal for this
# checkpoint, so it should just be logged.
logger.error(f"Trial {trial}: An error occurred during the "
f"checkpoint pre-sync wait: {e}")
# Force sync down and wait before tracking the new checkpoint.
try:
if trial_syncer.sync_down():
trial_syncer.wait()
else:
logger.error(f"Trial {trial}: Checkpoint sync skipped. "
f"This should not happen.")
except TuneError as e:
if trial.uses_cloud_checkpointing:
# Even though rsync failed the trainable can restore
# from remote durable storage.
logger.error(f"Trial {trial}: Sync error: {e}")
else:
# If the trainable didn't have remote storage to upload
# to then this checkpoint may have been lost, so we
# shouldn't track it with the checkpoint_manager.
raise e
if not trial.uses_cloud_checkpointing:
if not os.path.exists(checkpoint.dir_or_data):
raise TuneError("Trial {}: Checkpoint path {} not "
"found after successful sync down. "
"Are you running on a Kubernetes or "
"managed cluster? rsync will not function "
"due to a lack of SSH functionality. "
"You'll need to use cloud-checkpointing "
"if that's the case, see instructions "
"here: {} .".format(
trial,
checkpoint.dir_or_data,
CLOUD_CHECKPOINTING_URL,
))
def validate(formats):
"""Validates formats.
Raises:
ValueError if the format is unknown.
"""
for i in range(len(formats)):
formats[i] = formats[i].strip().lower()
if formats[i] not in [
ExportFormat.CHECKPOINT, ExportFormat.MODEL,
ExportFormat.ONNX, ExportFormat.H5
]:
raise TuneError("Unsupported import/export format: " +
formats[i])
def step(self):
"""Runs one step of the trial event loop.
Callers should typically run this method repeatedly in a loop. They
may inspect or modify the runner's state in between calls to step().
"""
next_trial = self._get_next_trial()
if next_trial is not None:
self._launch_trial(next_trial)
elif self._running:
self._process_events()
else:
for trial in self._trials:
if trial.status == Trial.PENDING:
if not self.has_resources(trial.resources):
raise TuneError(
("Insufficient cluster resources to launch trial: "
"trial requested {} but the cluster only has {} "
"available. Pass `queue_trials=True` in "
"ray.tune.run_experiments() or on the command "
"line to queue trials until the cluster scales "
"up. {}").format(
trial.resources.summary_string(),
self._avail_resources.summary_string(),
trial._get_trainable_cls().resource_help(
trial.config)))
elif trial.status == Trial.PAUSED:
raise TuneError(
"There are paused trials, but no more pending "
"trials with sufficient resources.")
raise TuneError("Called step when all trials finished?")
if self._server:
self._process_requests()
if self.is_finished():
self._server.shutdown()
def create_trial_from_spec(spec, output_path, parser, **trial_kwargs):
"""Creates a Trial object from parsing the spec.
Arguments:
spec (dict): A resolved experiment specification. Arguments should
The args here should correspond to the command line flags
in ray.tune.config_parser.
output_path (str); A specific output path within the local_dir.
Typically the name of the experiment.
parser (ArgumentParser): An argument parser object from
make_parser.
trial_kwargs: Extra keyword arguments used in instantiating the Trial.
Returns:
A trial object with corresponding parameters to the specification.
"""
try:
args, _ = parser.parse_known_args(to_argv(spec))
except SystemExit:
raise TuneError("Error parsing args, see above message", spec)
if "resources_per_trial" in spec:
trial_kwargs["resources"] = json_to_resources(
spec["resources_per_trial"])
return Trial(
# Submitting trial via server in py2.7 creates Unicode, which does not
# convert to string in a straightforward manner.
trainable_name=spec["run"],
# json.load leads to str -> unicode in py2.7
config=spec.get("config", {}),
local_dir=os.path.join(spec["local_dir"], output_path),
# json.load leads to str -> unicode in py2.7
stopping_criterion=spec.get("stop", {}),
remote_checkpoint_dir=spec.get("remote_checkpoint_dir"),
checkpoint_freq=args.checkpoint_freq,
checkpoint_at_end=args.checkpoint_at_end,
sync_on_checkpoint=args.sync_on_checkpoint,
keep_checkpoints_num=args.keep_checkpoints_num,
checkpoint_score_attr=args.checkpoint_score_attr,
export_formats=spec.get("export_formats", []),
# str(None) doesn't create None
restore_path=spec.get("restore"),
trial_name_creator=spec.get("trial_name_creator"),
trial_dirname_creator=spec.get("trial_dirname_creator"),
loggers=spec.get("loggers"),
log_to_file=spec.get("log_to_file"),
# str(None) doesn't create None
sync_to_driver_fn=spec.get("sync_to_driver"),
max_failures=args.max_failures,
**trial_kwargs)
def should_stop(self, result):
"""Whether the given result meets this trial's stopping criteria."""
if result.get(DONE):
return True
for criteria, stop_value in self.stopping_criterion.items():
if criteria not in result:
raise TuneError(
"Stopping criteria {} not provided in result {}.".format(
criteria, result))
if result[criteria] >= stop_value:
return True
return False
def wait_for_all(self):
failed_syncs = {}
for trial, sync_process in self._sync_processes.items():
try:
sync_process.wait()
except Exception as e:
failed_syncs[trial] = e
if failed_syncs:
sync_str = "\n".join(
[f" {trial}: {e}" for trial, e in failed_syncs.items()]
)
raise TuneError(
f"At least one trial failed to sync down when waiting for all "
f"trials to sync: \n{sync_str}"
)
def _train(self):
time.sleep(
self.config.get("script_min_iter_time_s",
self._default_config["script_min_iter_time_s"]))
result = self._status_reporter._get_and_clear_status()
while result is None:
time.sleep(1)
result = self._status_reporter._get_and_clear_status()
if result.timesteps_total is None:
raise TuneError("Must specify timesteps_total in result", result)
result = result._replace(
timesteps_this_iter=(result.timesteps_total -
self._last_reported_timestep))
self._last_reported_timestep = result.timesteps_total
return result
def wait_or_retry(self, max_retries: int = 3, backoff_s: int = 5):
assert max_retries > 0
last_error = None
for _ in range(max_retries - 1):
try:
self.wait()
except Exception as e:
logger.error(
f"Caught sync error: {e}. "
f"Retrying after sleeping for {backoff_s} seconds...")
last_error = e
time.sleep(backoff_s)
self.retry()
continue
return
raise TuneError(
f"Failed sync even after {max_retries} retries.") from last_error
def _try_resolve(v):
if isinstance(v, sample_from):
# Function to sample from
return False, v.func
elif isinstance(v, dict) and len(v) == 1 and "eval" in v:
# Lambda function in eval syntax
return False, lambda spec: eval(
v["eval"], _STANDARD_IMPORTS, {"spec": spec})
elif isinstance(v, dict) and len(v) == 1 and "grid_search" in v:
# Grid search values
grid_values = v["grid_search"]
if not isinstance(grid_values, list):
raise TuneError(
"Grid search expected list of values, got: {}".format(
grid_values))
return False, grid_values
return True, v
def step(self):
"""Runs one step of the trial event loop.
Callers should typically run this method repeatedly in a loop. They
may inspect or modify the runner's state in between calls to step().
"""
if self.is_finished():
raise TuneError("Called step when all trials finished?")
with warn_if_slow("on_step_begin"):
self.trial_executor.on_step_begin(self)
with warn_if_slow("callbacks.on_step_begin"):
self._callbacks.on_step_begin(
iteration=self._iteration, trials=self._trials)
next_trial = self._get_next_trial() # blocking
if next_trial is not None:
with warn_if_slow("start_trial"):
self.trial_executor.start_trial(next_trial)
self._callbacks.on_trial_start(
iteration=self._iteration,
trials=self._trials,
trial=next_trial)
elif self.trial_executor.get_running_trials():
self._process_events() # blocking
else:
self.trial_executor.on_no_available_trials(self)
self._stop_experiment_if_needed()
try:
with warn_if_slow("experiment_checkpoint"):
self.checkpoint()
except Exception as e:
logger.warning(f"Trial Runner checkpointing failed: {str(e)}")
self._iteration += 1
if self._server:
with warn_if_slow("server"):
self._process_stop_requests()
if self.is_finished():
self._server.shutdown()
with warn_if_slow("on_step_end"):
self.trial_executor.on_step_end(self)
with warn_if_slow("callbacks.on_step_end"):
self._callbacks.on_step_end(
iteration=self._iteration, trials=self._trials)
def should_stop(self, result):
"""Whether the given result meets this trial's stopping criteria."""
if result.get(DONE):
return True
for criteria, stop_value in self.stopping_criterion.items():
if criteria not in result:
raise TuneError(
"Stopping criteria {} not provided in result {}.".format(
criteria, result))
elif isinstance(criteria, dict):
raise ValueError(
"Stopping criteria is now flattened by default. "
"Use forward slashes to nest values `key1/key2/key3`.")
elif result[criteria] >= stop_value:
return True
return False
def on_checkpoint(
self,
iteration: int,
trials: List["Trial"],
trial: "Trial",
checkpoint: _TrackedCheckpoint,
**info,
):
if checkpoint.storage_mode == CheckpointStorage.MEMORY:
return
if self._sync_trial_dir(
trial, force=trial.sync_on_checkpoint,
wait=True) and not os.path.exists(checkpoint.dir_or_data):
raise TuneError(
f"Trial {trial}: Checkpoint path {checkpoint.dir_or_data} not "
"found after successful sync down.")
def _try_resolve(v) -> Tuple[bool, Any]:
if isinstance(v, Domain):
# Domain to sample from
return False, v
elif isinstance(v, dict) and len(v) == 1 and "eval" in v:
# Lambda function in eval syntax
return False, Function(
lambda spec: eval(v["eval"], _STANDARD_IMPORTS, {"spec": spec}))
elif isinstance(v, dict) and len(v) == 1 and "grid_search" in v:
# Grid search values
grid_values = v["grid_search"]
if not isinstance(grid_values, list):
raise TuneError(
"Grid search expected list of values, got: {}".format(
grid_values))
return False, Categorical(grid_values).grid()
return True, v
def _sync_trial_checkpoint(self, trial: "Trial", checkpoint: Checkpoint):
if checkpoint.storage == Checkpoint.MEMORY:
return
# Local import to avoid circular dependencies between syncer and
# trainable
from ray.tune.durable_trainable import DurableTrainable
trial_syncer = self._get_trial_syncer(trial)
# If the sync_function is False, syncing to driver is disabled.
# In every other case (valid values include None, True Callable,
# NodeSyncer) syncing to driver is enabled.
if trial.sync_on_checkpoint and self._sync_function is not False:
try:
# Wait for any other syncs to finish. We need to sync again
# after this to handle checkpoints taken mid-sync.
trial_syncer.wait()
except TuneError as e:
# Errors occurring during this wait are not fatal for this
# checkpoint, so it should just be logged.
logger.error(
"Trial %s: An error occurred during the "
"checkpoint pre-sync wait - %s", trial, str(e))
# Force sync down and wait before tracking the new checkpoint.
try:
if trial_syncer.sync_down():
trial_syncer.wait()
else:
logger.error(
"Trial %s: Checkpoint sync skipped. "
"This should not happen.", trial)
except TuneError as e:
if issubclass(trial.get_trainable_cls(), DurableTrainable):
# Even though rsync failed the trainable can restore
# from remote durable storage.
logger.error("Trial %s: Sync error - %s", trial, str(e))
else:
# If the trainable didn't have remote storage to upload
# to then this checkpoint may have been lost, so we
# shouldn't track it with the checkpoint_manager.
raise e
if not issubclass(trial.get_trainable_cls(), DurableTrainable):
if not os.path.exists(checkpoint.value):
raise TuneError("Trial {}: Checkpoint path {} not "
"found after successful sync down.".format(
trial, checkpoint.value))
def on_checkpoint(self, checkpoint):
"""Hook for handling checkpoints taken by the Trainable.
Args:
checkpoint (Checkpoint): Checkpoint taken.
"""
if checkpoint.storage == Checkpoint.MEMORY:
# TODO(ujvl): Handle this separately to avoid restoration failure.
self.checkpoint_manager.on_checkpoint(checkpoint)
return
if self.sync_on_checkpoint:
try:
# Wait for any other syncs to finish. We need to sync again
# after this to handle checkpoints taken mid-sync.
self.result_logger.wait()
except TuneError as e:
# Errors occurring during this wait are not fatal for this
# checkpoint, so it should just be logged.
logger.error(
"Trial %s: An error occurred during the "
"checkpoint pre-sync wait.", str(e))
# Force sync down and wait before tracking the new checkpoint.
try:
if self.result_logger.sync_down():
self.result_logger.wait()
else:
logger.error(
"Trial %s: Checkpoint sync skipped. "
"This should not happen.", self)
except TuneError as e:
if issubclass(self.get_trainable_cls(), DurableTrainable):
# Even though rsync failed the trainable can restore
# from remote durable storage.
logger.error("Trial %s: Sync error - %s", self, str(e))
else:
# If the trainable didn't have remote storage to upload
# to then this checkpoint may have been lost, so we
# shouldn't track it with the checkpoint_manager.
raise e
if not issubclass(self.get_trainable_cls(), DurableTrainable):
if not os.path.exists(checkpoint.value):
raise TuneError("Trial {}: Checkpoint path {} not "
"found after successful sync down.".format(
self, checkpoint.value))
self.checkpoint_manager.on_checkpoint(checkpoint)
def json_to_resources(data):
if data is None or data == "null":
return None
if isinstance(data, string_types):
data = json.loads(data)
for k in data:
if k in ["driver_cpu_limit", "driver_gpu_limit"]:
raise TuneError(
"The field `{}` is no longer supported. Use `extra_cpu` "
"or `extra_gpu` instead.".format(k))
if k not in Resources._fields:
raise ValueError(
"Unknown resource field {}, must be one of {}".format(
k, Resources._fields))
return Resources(data.get("cpu", 1), data.get("gpu", 0),
data.get("extra_cpu", 0), data.get("extra_gpu", 0),
data.get("custom_resources"),
data.get("extra_custom_resources"))
def start_trial(self, trial, checkpoint=None):
# Reserve node before starting trial
resources = trial.resources
# Check for required GPUs first
required = resources.gpu_total()
if required > 0:
resource_attr = "gpu"
else:
# No GPU, just use CPU
resource_attr = "cpu"
required = resources.cpu_total()
# Compute nodes required to fulfill trial resources request
custom_resources = {}
for node_id, node_resource in self._resources_by_node.items():
# Compute resource remaining on each node
node_capacity = node_resource.get_res_total(node_id)
committed_capacity = self._committed_resources.get_res_total(
node_id)
remaining_capacity = node_capacity - committed_capacity
node_procs = getattr(node_resource, resource_attr, 0)
available = node_procs * remaining_capacity
if available == 0:
continue
if required <= available:
custom_resources[node_id] = required / node_procs
required = 0
break
else:
custom_resources[node_id] = remaining_capacity
required -= available
if required > 0:
raise TuneError(f"Unable to start trial {trial.trial_id}. "
f"Not enough nodes")
# Update trial node affinity configuration and
# extra_custom_resources requirement
trial.config.update(__ray_node_affinity__=custom_resources)
trial.resources.extra_custom_resources.update(custom_resources)
super().start_trial(trial, checkpoint)
def create_trial_from_spec(spec, output_path, parser, **trial_kwargs):
"""Creates a Trial object from parsing the spec.
Arguments:
spec (dict): A resolved experiment specification. Arguments should
The args here should correspond to the command line flags
in ray.tune.config_parser.
output_path (str); A specific output path within the local_dir.
Typically the name of the experiment.
parser (ArgumentParser): An argument parser object from
make_parser.
trial_kwargs: Extra keyword arguments used in instantiating the Trial.
Returns:
A trial object with corresponding parameters to the specification.
"""
try:
# Special case the `env` param for RLlib by automatically
# moving it into the `config` section.
if "env" in spec:
spec["config"] = spec.get("config", {})
spec["config"]["env"] = spec["env"]
del spec["env"]
args = parser.parse_args(to_argv(spec))
except SystemExit:
raise TuneError("Error parsing args, see above message", spec)
if "trial_resources" in spec:
trial_kwargs["resources"] = json_to_resources(spec["trial_resources"])
return Trial(
# Submitting trial via server in py2.7 creates Unicode, which does not
# convert to string in a straightforward manner.
trainable_name=spec["run"],
# json.load leads to str -> unicode in py2.7
config=spec.get("config", {}),
local_dir=os.path.join(args.local_dir, output_path),
# json.load leads to str -> unicode in py2.7
stopping_criterion=spec.get("stop", {}),
checkpoint_freq=args.checkpoint_freq,
# str(None) doesn't create None
restore_path=spec.get("restore"),
upload_dir=args.upload_dir,
max_failures=args.max_failures,
**trial_kwargs)
def run_experiments(experiments, scheduler=None, **ray_args):
if scheduler is None:
scheduler = FIFOScheduler()
runner = TrialRunner(scheduler)
for name, spec in experiments.items():
for trial in generate_trials(spec, name):
runner.add_trial(trial)
print(runner.debug_string())
ray.init(**ray_args)
while not runner.is_finished():
runner.step()
print(runner.debug_string())
for trial in runner.get_trials():
if trial.status != Trial.TERMINATED:
raise TuneError("Trial did not complete", trial)
return runner.get_trials()
def train(self):
if not self._initialize_ok:
raise ValueError(
"Agent initialization failed, see previous errors")
now = time.time()
time.sleep(self.config["script_min_iter_time_s"])
result = self._status_reporter._get_and_clear_status()
while result is None:
time.sleep(1)
result = self._status_reporter._get_and_clear_status()
if result.timesteps_total is None:
raise TuneError("Must specify timesteps_total in result", result)
# Include the negative loss to use as a stopping condition
if result.mean_loss is not None:
neg_loss = -result.mean_loss
else:
neg_loss = result.neg_mean_loss
result = result._replace(
experiment_id=self._experiment_id,
neg_mean_loss=neg_loss,
training_iteration=self.iteration,
time_this_iter_s=now - self._last_reported_time,
timesteps_this_iter=(result.timesteps_total -
self._last_reported_timestep),
time_total_s=now - self._start_time,
pid=os.getpid(),
hostname=os.uname()[1])
if result.timesteps_total:
self._last_reported_timestep = result.timesteps_total
self._last_reported_time = now
self._iteration += 1
self._result_logger.on_result(result)
return result
def import_function(file_path, function_name):
# strong assumption here that we're in a new process
file_path = os.path.expanduser(file_path)
sys.path.insert(0, os.path.dirname(file_path))
if hasattr(importlib, "util"):
# Python 3.4+
spec = importlib.util.spec_from_file_location("external_file",
file_path)
external_file = importlib.util.module_from_spec(spec)
spec.loader.exec_module(external_file)
elif hasattr(importlib, "machinery"):
# Python 3.3
from importlib.machinery import SourceFileLoader
external_file = SourceFileLoader("external_file",
file_path).load_module()
else:
# Python 2.x
import imp
external_file = imp.load_source("external_file", file_path)
if not external_file:
raise TuneError("Unable to import file at {}".format(file_path))
return getattr(external_file, function_name)
def run_experiments(experiments,
scheduler=None,
with_server=False,
server_port=TuneServer.DEFAULT_PORT,
verbose=True):
# Make sure rllib agents are registered
from ray import rllib # noqa # pylint: disable=unused-import
if scheduler is None:
scheduler = FIFOScheduler()
runner = TrialRunner(scheduler,
launch_web_server=with_server,
server_port=server_port)
for name, spec in experiments.items():
for trial in generate_trials(spec, name):
trial.set_verbose(verbose)
runner.add_trial(trial)
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 _generate_trials(self, num_samples, unresolved_spec, output_path=""):
"""Generates Trial objects with the variant generation process.
Uses a fixed point iteration to resolve variants. All trials
should be able to be generated at once.
See also: `ray.tune.suggest.variant_generator`.
Yields:
Trial object
"""
if "run" not in unresolved_spec:
raise TuneError("Must specify `run` in {}".format(unresolved_spec))
for _ in range(num_samples):
# Iterate over list of configs
for unresolved_cfg in unresolved_spec["config"]:
unresolved_spec_variant = deepcopy(unresolved_spec)
unresolved_spec_variant["config"] = unresolved_cfg
resolved_base_vars = CustomVariantGenerator._extract_resolved_base_vars(unresolved_cfg,
unresolved_spec["config"])
print("Resolved base cfg vars", resolved_base_vars)
for resolved_vars, spec in generate_variants(unresolved_spec_variant):
resolved_vars.update(resolved_base_vars)
print("Resolved vars", resolved_vars)
trial_id = "%05d" % self._counter
experiment_tag = str(self._counter)
if resolved_vars:
experiment_tag += "_{}".format(
format_vars({k: v for k, v in resolved_vars.items() if "tag" in k}))
self._counter += 1
yield create_trial_from_spec(
spec,
output_path,
self._parser,
evaluated_params=flatten_resolved_vars(resolved_vars),
trial_id=trial_id,
experiment_tag=experiment_tag)
def step(self):
"""Implements train() for a Function API.
If the RunnerThread finishes without reporting "done",
Tune will automatically provide a magic keyword __duplicate__
along with a result with "done=True". The TrialRunner will handle the
result accordingly (see tune/trial_runner.py).
"""
if self._runner and self._runner.is_alive():
# if started and alive, inform the reporter to continue and
# generate the next result
self._continue_semaphore.release()
else:
self._start()
result = None
while result is None and self._runner.is_alive():
# fetch the next produced result
try:
result = self._results_queue.get(block=True,
timeout=RESULT_FETCH_TIMEOUT)
except queue.Empty:
pass
# if no result were found, then the runner must no longer be alive
if result is None:
# Try one last time to fetch results in case results were reported
# in between the time of the last check and the termination of the
# thread runner.
try:
result = self._results_queue.get(block=False)
except queue.Empty:
pass
# check if error occurred inside the thread runner
if result is None:
# only raise an error from the runner if all results are consumed
self._report_thread_runner_error(block=True)
# Under normal conditions, this code should never be reached since
# this branch should only be visited if the runner thread raised
# an exception. If no exception were raised, it means that the
# runner thread never reported any results which should not be
# possible when wrapping functions with `wrap_function`.
raise TuneError(
("Wrapped function ran until completion without reporting "
"results or raising an exception."))
else:
if not self._error_queue.empty():
logger.warning(
("Runner error waiting to be raised in main thread. "
"Logging all available results first."))
# This keyword appears if the train_func using the Function API
# finishes without "done=True". This duplicates the last result, but
# the TrialRunner will not log this result again.
if RESULT_DUPLICATE in result:
new_result = self._last_result.copy()
new_result.update(result)
result = new_result
self._last_result = result
if self._status_reporter.has_new_checkpoint():
result[SHOULD_CHECKPOINT] = True
return result
def _tune_error(msg):
raise TuneError(msg)
def register(self, category, key, value):
if category not in KNOWN_CATEGORIES:
raise TuneError("Unknown category {} not among {}".format(
category, KNOWN_CATEGORIES))
self._all_objects[(category, key)] = value
def step(self):
"""Runs one step of the trial event loop.
Callers should typically run this method repeatedly in a loop. They
may inspect or modify the runner's state in between calls to step().
"""
self._updated_queue = False
if self.is_finished():
raise TuneError("Called step when all trials finished?")
with warn_if_slow("on_step_begin"):
self.trial_executor.on_step_begin(self)
with warn_if_slow("callbacks.on_step_begin"):
self._callbacks.on_step_begin(iteration=self._iteration,
trials=self._trials)
# This will contain the next trial to start
next_trial = self._get_next_trial() # blocking
# Create pending trials. If the queue was updated before, only
# continue updating if this was successful (next_trial is not None)
if not self._updated_queue or (self._updated_queue and next_trial):
num_pending_trials = len(
[t for t in self._trials if t.status == Trial.PENDING])
while num_pending_trials < self._max_pending_trials:
if not self._update_trial_queue(blocking=False):
break
num_pending_trials += 1
# Update status of staged placement groups
self.trial_executor.stage_and_update_status(self._trials)
def _start_trial(trial: Trial) -> bool:
"""Helper function to start trial and call callbacks"""
with warn_if_slow("start_trial"):
if self.trial_executor.start_trial(trial):
self._callbacks.on_trial_start(iteration=self._iteration,
trials=self._trials,
trial=trial)
return True
return False
may_handle_events = True
if next_trial is not None:
if _start_trial(next_trial):
may_handle_events = False
elif next_trial.status != Trial.ERROR:
# Only try to start another trial if previous trial startup
# did not error (e.g. it just didn't start because its
# placement group is not ready, yet).
next_trial = self.trial_executor.get_staged_trial()
if next_trial is not None:
if _start_trial(next_trial):
may_handle_events = False
if may_handle_events:
if self.trial_executor.get_running_trials():
timeout = None
if self.trial_executor.in_staging_grace_period():
timeout = 0.1
self._process_events(timeout=timeout) # blocking
else:
self.trial_executor.on_no_available_trials(self)
self._stop_experiment_if_needed()
try:
self.checkpoint()
except Exception as e:
logger.warning(f"Trial Runner checkpointing failed: {str(e)}")
self._iteration += 1
if self._server:
with warn_if_slow("server"):
self._process_stop_requests()
if self.is_finished():
self._server.shutdown()
with warn_if_slow("on_step_end"):
self.trial_executor.on_step_end(self)
with warn_if_slow("callbacks.on_step_end"):
self._callbacks.on_step_end(iteration=self._iteration,
trials=self._trials)
def _registration_check(cls, trainable_name):
if not has_trainable(trainable_name):
# Make sure rllib agents are registered
from ray import rllib # noqa: F401
if not has_trainable(trainable_name):
raise TuneError("Unknown trainable: " + trainable_name)
