def run(run_or_experiment,
name=None,
stop=None,
config=None,
resources_per_trial=None,
num_samples=1,
local_dir=None,
upload_dir=None,
trial_name_creator=None,
loggers=None,
sync_to_cloud=None,
sync_to_driver=None,
checkpoint_freq=0,
checkpoint_at_end=False,
sync_on_checkpoint=True,
keep_checkpoints_num=None,
checkpoint_score_attr=None,
global_checkpoint_period=10,
export_formats=None,
max_failures=0,
fail_fast=False,
restore=None,
search_alg=None,
scheduler=None,
with_server=False,
server_port=TuneServer.DEFAULT_PORT,
verbose=2,
progress_reporter=None,
resume=False,
queue_trials=False,
reuse_actors=False,
trial_executor=None,
raise_on_failed_trial=True,
return_trials=False,
ray_auto_init=True):
"""Executes training.
Args:
run_or_experiment (function | class | str | :class:`Experiment`): If
function|class|str, this is the algorithm or model to train.
This may refer to the name of a built-on algorithm
(e.g. RLLib's DQN or PPO), a user-defined trainable
function or class, or the string identifier of a
trainable function or class registered in the tune registry.
If Experiment, then Tune will execute training based on
Experiment.spec.
name (str): Name of experiment.
stop (dict | callable | :class:`Stopper`): Stopping criteria. If dict,
the keys may be any field in the return result of 'train()',
whichever is reached first. If function, it must take (trial_id,
result) as arguments and return a boolean (True if trial should be
stopped, False otherwise). This can also be a subclass of
``ray.tune.Stopper``, which allows users to implement
custom experiment-wide stopping (i.e., stopping an entire Tune
run based on some time constraint).
config (dict): Algorithm-specific configuration for Tune variant
generation (e.g. env, hyperparams). Defaults to empty dict.
Custom search algorithms may ignore this.
resources_per_trial (dict): Machine resources to allocate per trial,
e.g. ``{"cpu": 64, "gpu": 8}``. Note that GPUs will not be
assigned unless you specify them here. Defaults to 1 CPU and 0
GPUs in ``Trainable.default_resource_request()``.
num_samples (int): Number of times to sample from the
hyperparameter space. Defaults to 1. If `grid_search` is
provided as an argument, the grid will be repeated
`num_samples` of times.
local_dir (str): Local dir to save training results to.
Defaults to ``~/ray_results``.
upload_dir (str): Optional URI to sync training results and checkpoints
to (e.g. ``s3://bucket`` or ``gs://bucket``).
trial_name_creator (func): Optional function for generating
the trial string representation.
loggers (list): List of logger creators to be used with
each Trial. If None, defaults to ray.tune.logger.DEFAULT_LOGGERS.
See `ray/tune/logger.py`.
sync_to_cloud (func|str): Function for syncing the local_dir to and
from upload_dir. If string, then it must be a string template that
includes `{source}` and `{target}` for the syncer to run. If not
provided, the sync command defaults to standard S3 or gsutil sync
commands.
sync_to_driver (func|str|bool): Function for syncing trial logdir from
remote node to local. If string, then it must be a string template
that includes `{source}` and `{target}` for the syncer to run.
If True or not provided, it defaults to using rsync. If False,
syncing to driver is disabled.
checkpoint_freq (int): How many training iterations between
checkpoints. A value of 0 (default) disables checkpointing.
checkpoint_at_end (bool): Whether to checkpoint at the end of the
experiment regardless of the checkpoint_freq. Default is False.
sync_on_checkpoint (bool): Force sync-down of trial checkpoint to
driver. If set to False, checkpoint syncing from worker to driver
is asynchronous and best-effort. This does not affect persistent
storage syncing. Defaults to True.
keep_checkpoints_num (int): Number of checkpoints to keep. A value of
`None` keeps all checkpoints. Defaults to `None`. If set, need
to provide `checkpoint_score_attr`.
checkpoint_score_attr (str): Specifies by which attribute to rank the
best checkpoint. Default is increasing order. If attribute starts
with `min-` it will rank attribute in decreasing order, i.e.
`min-validation_loss`.
global_checkpoint_period (int): Seconds between global checkpointing.
This does not affect `checkpoint_freq`, which specifies frequency
for individual trials.
export_formats (list): List of formats that exported at the end of
the experiment. Default is None.
max_failures (int): Try to recover a trial at least this many times.
Ray will recover from the latest checkpoint if present.
Setting to -1 will lead to infinite recovery retries.
Setting to 0 will disable retries. Defaults to 3.
fail_fast (bool): Whether to fail upon the first error.
restore (str): Path to checkpoint. Only makes sense to set if
running 1 trial. Defaults to None.
search_alg (SearchAlgorithm): Search Algorithm. Defaults to
BasicVariantGenerator.
scheduler (TrialScheduler): Scheduler for executing
the experiment. Choose among FIFO (default), MedianStopping,
AsyncHyperBand, HyperBand and PopulationBasedTraining. Refer to
ray.tune.schedulers for more options.
with_server (bool): Starts a background Tune server. Needed for
using the Client API.
server_port (int): Port number for launching TuneServer.
verbose (int): 0, 1, or 2. Verbosity mode. 0 = silent,
1 = only status updates, 2 = status and trial results.
progress_reporter (ProgressReporter): Progress reporter for reporting
intermediate experiment progress. Defaults to CLIReporter if
running in command-line, or JupyterNotebookReporter if running in
a Jupyter notebook.
resume (str|bool): One of "LOCAL", "REMOTE", "PROMPT", or bool.
LOCAL/True restores the checkpoint from the local_checkpoint_dir.
REMOTE restores the checkpoint from remote_checkpoint_dir.
PROMPT provides CLI feedback. False forces a new
experiment. If resume is set but checkpoint does not exist,
ValueError will be thrown.
queue_trials (bool): Whether to queue trials when the cluster does
not currently have enough resources to launch one. This should
be set to True when running on an autoscaling cluster to enable
automatic scale-up.
reuse_actors (bool): Whether to reuse actors between different trials
when possible. This can drastically speed up experiments that start
and stop actors often (e.g., PBT in time-multiplexing mode). This
requires trials to have the same resource requirements.
trial_executor (TrialExecutor): Manage the execution of trials.
raise_on_failed_trial (bool): Raise TuneError if there exists failed
trial (of ERROR state) when the experiments complete.
ray_auto_init (bool): Automatically starts a local Ray cluster
if using a RayTrialExecutor (which is the default) and
if Ray is not initialized. Defaults to True.
Returns:
ExperimentAnalysis: Object for experiment analysis.
Raises:
TuneError: Any trials failed and `raise_on_failed_trial` is True.
Examples:
>>> tune.run(mytrainable, scheduler=PopulationBasedTraining())
>>> tune.run(mytrainable, num_samples=5, reuse_actors=True)
>>> tune.run(
>>> "PG",
>>> num_samples=5,
>>> config={
>>> "env": "CartPole-v0",
>>> "lr": tune.sample_from(lambda _: np.random.rand())
>>> }
>>> )
"""
trial_executor = trial_executor or RayTrialExecutor(
queue_trials=queue_trials,
reuse_actors=reuse_actors,
ray_auto_init=ray_auto_init)
if isinstance(run_or_experiment, list):
experiments = run_or_experiment
else:
experiments = [run_or_experiment]
for i, exp in enumerate(experiments):
if not isinstance(exp, Experiment):
run_identifier = Experiment.register_if_needed(exp)
experiments[i] = Experiment(
name=name,
run=run_identifier,
stop=stop,
config=config,
resources_per_trial=resources_per_trial,
num_samples=num_samples,
local_dir=local_dir,
upload_dir=upload_dir,
sync_to_driver=sync_to_driver,
trial_name_creator=trial_name_creator,
loggers=loggers,
checkpoint_freq=checkpoint_freq,
checkpoint_at_end=checkpoint_at_end,
sync_on_checkpoint=sync_on_checkpoint,
keep_checkpoints_num=keep_checkpoints_num,
checkpoint_score_attr=checkpoint_score_attr,
export_formats=export_formats,
max_failures=max_failures,
restore=restore)
else:
logger.debug("Ignoring some parameters passed into tune.run.")
if sync_to_cloud:
for exp in experiments:
assert exp.remote_checkpoint_dir, (
"Need `upload_dir` if `sync_to_cloud` given.")
if fail_fast and max_failures != 0:
raise ValueError("max_failures must be 0 if fail_fast=True.")
runner = TrialRunner(
search_alg=search_alg or BasicVariantGenerator(),
scheduler=scheduler or FIFOScheduler(),
local_checkpoint_dir=experiments[0].checkpoint_dir,
remote_checkpoint_dir=experiments[0].remote_checkpoint_dir,
sync_to_cloud=sync_to_cloud,
stopper=experiments[0].stopper,
checkpoint_period=global_checkpoint_period,
resume=resume,
launch_web_server=with_server,
server_port=server_port,
verbose=bool(verbose > 1),
fail_fast=fail_fast,
trial_executor=trial_executor)
for exp in experiments:
runner.add_experiment(exp)
if progress_reporter is None:
if IS_NOTEBOOK:
progress_reporter = JupyterNotebookReporter(overwrite=verbose < 2)
else:
progress_reporter = CLIReporter()
# User Warning for GPUs
if trial_executor.has_gpus():
if isinstance(resources_per_trial,
dict) and "gpu" in resources_per_trial:
# "gpu" is manually set.
pass
elif _check_default_resources_override(experiments[0].run_identifier):
# "default_resources" is manually overriden.
pass
else:
logger.warning("Tune detects GPUs, but no trials are using GPUs. "
"To enable trials to use GPUs, set "
"tune.run(resources_per_trial={'gpu': 1}...) "
"which allows Tune to expose 1 GPU to each trial. "
"You can also override "
"`Trainable.default_resource_request` if using the "
"Trainable API.")
while not runner.is_finished():
runner.step()
if verbose:
_report_progress(runner, progress_reporter)
try:
runner.checkpoint(force=True)
except Exception:
logger.exception("Trial Runner checkpointing failed.")
if verbose:
_report_progress(runner, progress_reporter, done=True)
wait_for_sync()
runner.cleanup_trials()
incomplete_trials = []
for trial in runner.get_trials():
if trial.status != Trial.TERMINATED:
incomplete_trials += [trial]
if incomplete_trials:
if raise_on_failed_trial:
raise TuneError("Trials did not complete", incomplete_trials)
else:
logger.error("Trials did not complete: %s", incomplete_trials)
trials = runner.get_trials()
if return_trials:
return trials
return ExperimentAnalysis(runner.checkpoint_file, trials=trials)
def test_cluster_interrupt(start_connected_cluster, tmpdir):
"""Tests run_experiment on cluster shutdown with actual interrupt.
This is an end-to-end test.
"""
cluster = start_connected_cluster
dirpath = str(tmpdir)
# Needs to be in scope for pytest
class _Mock(tune.Trainable):
"""Finishes on the 4th iteration."""
def _setup(self, config):
self.state = {"hi": 0}
def _train(self):
self.state["hi"] += 1
time.sleep(0.5)
return {"done": self.state["hi"] >= 4}
def _save(self, path):
return self.state
def _restore(self, state):
self.state = state
# Removes indent from class.
reformatted = "\n".join(line[4:] if len(line) else line
for line in inspect.getsource(_Mock).split("\n"))
script = """
import time
import ray
from ray import tune
ray.init(address="{address}")
{fail_class_code}
tune.run(
{fail_class},
name="experiment",
stop=dict(training_iteration=5),
local_dir="{checkpoint_dir}",
checkpoint_freq=1,
global_checkpoint_period=0,
max_failures=1,
raise_on_failed_trial=False)
""".format(address=cluster.address,
checkpoint_dir=dirpath,
fail_class_code=reformatted,
fail_class=_Mock.__name__)
run_string_as_driver_nonblocking(script)
# Wait until the right checkpoint is saved.
# The trainable returns every 0.5 seconds, so this should not miss
# the checkpoint.
local_checkpoint_dir = os.path.join(dirpath, "experiment")
for i in range(50):
if TrialRunner.checkpoint_exists(local_checkpoint_dir):
# Inspect the internal trialrunner
runner = TrialRunner(resume="LOCAL",
local_checkpoint_dir=local_checkpoint_dir)
trials = runner.get_trials()
last_res = trials[0].last_result
if last_res and last_res.get("training_iteration") == 3:
break
time.sleep(0.2)
if not TrialRunner.checkpoint_exists(local_checkpoint_dir):
raise RuntimeError("Checkpoint file didn't appear.")
ray.shutdown()
cluster.shutdown()
cluster = _start_new_cluster()
Experiment.register_if_needed(_Mock)
# Inspect the internal trialrunner
runner = TrialRunner(resume="LOCAL",
local_checkpoint_dir=local_checkpoint_dir)
trials = runner.get_trials()
assert trials[0].last_result["training_iteration"] == 3
assert trials[0].status == Trial.PENDING
# Restore properly from checkpoint
trials2 = tune.run_experiments(
{
"experiment": {
"run": _Mock,
"local_dir": dirpath,
"checkpoint_freq": 1
}
},
resume=True,
raise_on_failed_trial=False)
assert all(t.status == Trial.TERMINATED for t in trials2)
assert {t.trial_id for t in trials2} == {t.trial_id for t in trials}
ray.shutdown()
cluster.shutdown()
def __init__(self,
run_or_experiment,
name=None,
stop=None,
config=None,
resources_per_trial=None,
num_samples=1,
local_dir=None,
upload_dir=None,
trial_name_creator=None,
loggers=None,
sync_to_cloud=None,
sync_to_driver=False,
checkpoint_freq=0,
checkpoint_at_end=False,
sync_on_checkpoint=True,
keep_checkpoints_num=None,
checkpoint_score_attr=None,
global_checkpoint_period=10,
export_formats=None,
max_failures=0,
fail_fast=True,
restore=None,
search_alg=None,
scheduler=None,
with_server=False,
server_port=TuneServer.DEFAULT_PORT,
verbose=0,
progress_reporter=None,
resume=False,
queue_trials=False,
reuse_actors=False,
trial_executor=None,
raise_on_failed_trial=True,
return_trials=False,
ray_auto_init=True,
shuffle=False):
if loggers is None:
loggers = [JsonLogger, CSVLogger]
config = _transform_config(config)
is_trainable = False
try:
if issubclass(run_or_experiment, Trainable):
is_trainable = True
except TypeError:
pass
if not is_trainable:
run_or_experiment = wrap_function(run_or_experiment)
self.trial_executor = trial_executor or RayTrialExecutor(
queue_trials=queue_trials,
reuse_actors=reuse_actors,
ray_auto_init=ray_auto_init)
experiments = [run_or_experiment]
self.logger = logging.getLogger(__name__)
for i, exp in enumerate(experiments):
if not isinstance(exp, Experiment):
run_identifier = Experiment.register_if_needed(exp)
experiments[i] = Experiment(
name=name,
run=run_identifier,
stop=stop,
config=config,
resources_per_trial=resources_per_trial,
num_samples=num_samples,
local_dir=local_dir,
upload_dir=upload_dir,
sync_to_driver=sync_to_driver,
trial_name_creator=trial_name_creator,
loggers=loggers,
checkpoint_freq=checkpoint_freq,
checkpoint_at_end=checkpoint_at_end,
sync_on_checkpoint=sync_on_checkpoint,
keep_checkpoints_num=keep_checkpoints_num,
checkpoint_score_attr=checkpoint_score_attr,
export_formats=export_formats,
max_failures=max_failures,
restore=restore)
if fail_fast and max_failures != 0:
raise ValueError("max_failures must be 0 if fail_fast=True.")
self.runner = TrialRunner(
search_alg=search_alg or BasicVariantGenerator(shuffle=shuffle),
scheduler=scheduler or FIFOScheduler(),
local_checkpoint_dir=experiments[0].checkpoint_dir,
remote_checkpoint_dir=experiments[0].remote_checkpoint_dir,
sync_to_cloud=sync_to_cloud,
stopper=experiments[0].stopper,
checkpoint_period=global_checkpoint_period,
resume=resume,
launch_web_server=with_server,
server_port=server_port,
verbose=bool(verbose > 1),
fail_fast=fail_fast,
trial_executor=self.trial_executor)
for exp in experiments:
self.runner.add_experiment(exp)
self._is_worker_stopped = threading.Event()
self._worker_exc = None
self._worker = threading.Thread(target=self.step_worker, daemon=True)
self._worker.start()
atexit.register(self.stop)