def log_background_task(_, config, queue):
"""
Use background task to update logger files using ray.tune `UnifiedLogger`
"""
# Configure ray.tune loggers
from ray.tune.logger import UnifiedLogger
logger = UnifiedLogger(config=config,
logdir=config["logdir"],
loggers=config.get("loggers", None))
logger.last_timestamp = logger.start_timestamp = time.time()
# Wait for results
results = queue.get()
while results is not None:
# Update ray.tune fields
timestamp = results["timestamp"]
results["config"] = config
results["experiment_id"] = config["experiment_id"]
results["experiment_tag"] = config["experiment_tag"]
results["training_iteration"] = results.pop("epoch")
results["neg_mean_loss"] = results["mean_loss"]
results["timesteps_total"] = results.get("timestep", 0)
results["timestamp"] = int(timestamp)
results["date"] = datetime.fromtimestamp(timestamp).strftime(
"%Y-%m-%d_%H-%M-%S")
results["time_this_iter_s"] = timestamp - logger.last_timestamp
results["time_total_s"] = timestamp - logger.start_timestamp
logger.on_result(results)
logger.last_timestamp = timestamp
# Wait for next results
results = queue.get()
logger.flush()
logger.close()
def run_trial(config):
"""
Run a single trial configuration
"""
# Configure ray.tune loggers
from ray.tune.logger import UnifiedLogger
logger = UnifiedLogger(config=config,
logdir=config["logdir"],
loggers=config.get("loggers", None))
logger.last_timestamp = logger.start_timestamp = time.time()
result = imagenet_run.run(config=config,
logger=partial(log_results, logger, config),
on_checkpoint=partial(save_checkpoint, config))
logger.flush()
logger.close()
return result
class Trainable:
"""Abstract class for trainable models, functions, etc.
A call to ``train()`` on a trainable will execute one logical iteration of
training. As a rule of thumb, the execution time of one train call should
be large enough to avoid overheads (i.e. more than a few seconds), but
short enough to report progress periodically (i.e. at most a few minutes).
Calling ``save()`` should save the training state of a trainable to disk,
and ``restore(path)`` should restore a trainable to the given state.
Generally you only need to implement ``setup``, ``step``,
``save_checkpoint``, and ``load_checkpoint`` when subclassing Trainable.
Other implementation methods that may be helpful to override are
``log_result``, ``reset_config``, ``cleanup``, and ``_export_model``.
When using Tune, Tune will convert this class into a Ray actor, which
runs on a separate process. Tune will also change the current working
directory of this process to ``self.logdir``.
"""
def __init__(self, config=None, logger_creator=None):
"""Initialize an Trainable.
Sets up logging and points ``self.logdir`` to a directory in which
training outputs should be placed.
Subclasses should prefer defining ``build()`` instead of overriding
``__init__()`` directly.
Args:
config (dict): Trainable-specific configuration data. By default
will be saved as ``self.config``.
logger_creator (func): Function that creates a ray.tune.Logger
object. If unspecified, a default logger is created.
"""
self._experiment_id = uuid.uuid4().hex
self.config = config or {}
trial_info = self.config.pop(TRIAL_INFO, None)
self._result_logger = self._logdir = None
self._create_logger(self.config, logger_creator)
self._stdout_context = self._stdout_fp = self._stdout_stream = None
self._stderr_context = self._stderr_fp = self._stderr_stream = None
self._stderr_logging_handler = None
stdout_file = self.config.pop(STDOUT_FILE, None)
stderr_file = self.config.pop(STDERR_FILE, None)
self._open_logfiles(stdout_file, stderr_file)
self._iteration = 0
self._time_total = 0.0
self._timesteps_total = None
self._episodes_total = None
self._time_since_restore = 0.0
self._timesteps_since_restore = 0
self._iterations_since_restore = 0
self._restored = False
self._trial_info = trial_info
start_time = time.time()
self.setup(copy.deepcopy(self.config))
setup_time = time.time() - start_time
if setup_time > SETUP_TIME_THRESHOLD:
logger.info("Trainable.setup took {:.3f} seconds. If your "
"trainable is slow to initialize, consider setting "
"reuse_actors=True to reduce actor creation "
"overheads.".format(setup_time))
self._local_ip = self.get_current_ip()
log_sys_usage = self.config.get("log_sys_usage", False)
self._monitor = UtilMonitor(start=log_sys_usage)
@classmethod
def default_resource_request(cls, config):
"""Provides a static resource requirement for the given configuration.
This can be overridden by sub-classes to set the correct trial resource
allocation, so the user does not need to.
.. code-block:: python
@classmethod
def default_resource_request(cls, config):
return Resources(
cpu=0,
gpu=0,
extra_cpu=config["workers"],
extra_gpu=int(config["use_gpu"]) * config["workers"])
Returns:
Resources: A Resources object consumed by Tune for queueing.
"""
return None
@classmethod
def resource_help(cls, config):
"""Returns a help string for configuring this trainable's resources.
Args:
config (dict): The Trainer's config dict.
"""
return ""
def get_current_ip(self):
self._local_ip = ray.services.get_node_ip_address()
return self._local_ip
def train(self):
"""Runs one logical iteration of training.
Calls ``step()`` internally. Subclasses should override ``step()``
instead to return results.
This method automatically fills the following fields in the result:
`done` (bool): training is terminated. Filled only if not provided.
`time_this_iter_s` (float): Time in seconds this iteration
took to run. This may be overriden in order to override the
system-computed time difference.
`time_total_s` (float): Accumulated time in seconds for this
entire experiment.
`experiment_id` (str): Unique string identifier
for this experiment. This id is preserved
across checkpoint / restore calls.
`training_iteration` (int): The index of this
training iteration, e.g. call to train(). This is incremented
after `step()` is called.
`pid` (str): The pid of the training process.
`date` (str): A formatted date of when the result was processed.
`timestamp` (str): A UNIX timestamp of when the result
was processed.
`hostname` (str): Hostname of the machine hosting the training
process.
`node_ip` (str): Node ip of the machine hosting the training
process.
Returns:
A dict that describes training progress.
"""
start = time.time()
result = self.step()
assert isinstance(result, dict), "step() needs to return a dict."
# We do not modify internal state nor update this result if duplicate.
if RESULT_DUPLICATE in result:
return result
result = result.copy()
self._iteration += 1
self._iterations_since_restore += 1
if result.get(TIME_THIS_ITER_S) is not None:
time_this_iter = result[TIME_THIS_ITER_S]
else:
time_this_iter = time.time() - start
self._time_total += time_this_iter
self._time_since_restore += time_this_iter
result.setdefault(DONE, False)
# self._timesteps_total should only be tracked if increments provided
if result.get(TIMESTEPS_THIS_ITER) is not None:
if self._timesteps_total is None:
self._timesteps_total = 0
self._timesteps_total += result[TIMESTEPS_THIS_ITER]
self._timesteps_since_restore += result[TIMESTEPS_THIS_ITER]
# self._episodes_total should only be tracked if increments provided
if result.get(EPISODES_THIS_ITER) is not None:
if self._episodes_total is None:
self._episodes_total = 0
self._episodes_total += result[EPISODES_THIS_ITER]
# self._timesteps_total should not override user-provided total
result.setdefault(TIMESTEPS_TOTAL, self._timesteps_total)
result.setdefault(EPISODES_TOTAL, self._episodes_total)
result.setdefault(TRAINING_ITERATION, self._iteration)
# Provides auto-filled neg_mean_loss for avoiding regressions
if result.get("mean_loss"):
result.setdefault("neg_mean_loss", -result["mean_loss"])
now = datetime.today()
result.update(
experiment_id=self._experiment_id,
date=now.strftime("%Y-%m-%d_%H-%M-%S"),
timestamp=int(time.mktime(now.timetuple())),
time_this_iter_s=time_this_iter,
time_total_s=self._time_total,
pid=os.getpid(),
hostname=platform.node(),
node_ip=self._local_ip,
config=self.config,
time_since_restore=self._time_since_restore,
timesteps_since_restore=self._timesteps_since_restore,
iterations_since_restore=self._iterations_since_restore)
monitor_data = self._monitor.get_data()
if monitor_data:
result.update(monitor_data)
self.log_result(result)
if self._stdout_context:
self._stdout_stream.flush()
if self._stderr_context:
self._stderr_stream.flush()
return result
def get_state(self):
return {
"experiment_id": self._experiment_id,
"iteration": self._iteration,
"timesteps_total": self._timesteps_total,
"time_total": self._time_total,
"episodes_total": self._episodes_total,
"ray_version": ray.__version__,
}
def save(self, checkpoint_dir=None):
"""Saves the current model state to a checkpoint.
Subclasses should override ``_save()`` instead to save state.
This method dumps additional metadata alongside the saved path.
Args:
checkpoint_dir (str): Optional dir to place the checkpoint.
Returns:
str: Checkpoint path or prefix that may be passed to restore().
"""
checkpoint_dir = TrainableUtil.make_checkpoint_dir(
checkpoint_dir or self.logdir, index=self.iteration)
checkpoint = self.save_checkpoint(checkpoint_dir)
trainable_state = self.get_state()
checkpoint_path = TrainableUtil.process_checkpoint(
checkpoint,
parent_dir=checkpoint_dir,
trainable_state=trainable_state)
return checkpoint_path
def save_to_object(self):
"""Saves the current model state to a Python object.
It also saves to disk but does not return the checkpoint path.
Returns:
Object holding checkpoint data.
"""
tmpdir = tempfile.mkdtemp("save_to_object", dir=self.logdir)
checkpoint_path = self.save(tmpdir)
# Save all files in subtree and delete the tmpdir.
obj = TrainableUtil.checkpoint_to_object(checkpoint_path)
shutil.rmtree(tmpdir)
return obj
def restore(self, checkpoint_path):
"""Restores training state from a given model checkpoint.
These checkpoints are returned from calls to save().
Subclasses should override ``_restore()`` instead to restore state.
This method restores additional metadata saved with the checkpoint.
"""
with open(checkpoint_path + ".tune_metadata", "rb") as f:
metadata = pickle.load(f)
self._experiment_id = metadata["experiment_id"]
self._iteration = metadata["iteration"]
self._timesteps_total = metadata["timesteps_total"]
self._time_total = metadata["time_total"]
self._episodes_total = metadata["episodes_total"]
saved_as_dict = metadata["saved_as_dict"]
if saved_as_dict:
with open(checkpoint_path, "rb") as loaded_state:
checkpoint_dict = pickle.load(loaded_state)
checkpoint_dict.update(tune_checkpoint_path=checkpoint_path)
self.load_checkpoint(checkpoint_dict)
else:
self.load_checkpoint(checkpoint_path)
self._time_since_restore = 0.0
self._timesteps_since_restore = 0
self._iterations_since_restore = 0
self._restored = True
logger.info("Restored on %s from checkpoint: %s",
self.get_current_ip(), checkpoint_path)
state = {
"_iteration": self._iteration,
"_timesteps_total": self._timesteps_total,
"_time_total": self._time_total,
"_episodes_total": self._episodes_total,
}
logger.info("Current state after restoring: %s", state)
def restore_from_object(self, obj):
"""Restores training state from a checkpoint object.
These checkpoints are returned from calls to save_to_object().
"""
tmpdir = tempfile.mkdtemp("restore_from_object", dir=self.logdir)
checkpoint_path = TrainableUtil.create_from_pickle(obj, tmpdir)
self.restore(checkpoint_path)
shutil.rmtree(tmpdir)
def delete_checkpoint(self, checkpoint_path):
"""Deletes local copy of checkpoint.
Args:
checkpoint_path (str): Path to checkpoint.
"""
try:
checkpoint_dir = TrainableUtil.find_checkpoint_dir(checkpoint_path)
except FileNotFoundError:
# The checkpoint won't exist locally if the
# trial was rescheduled to another worker.
logger.debug("Checkpoint not found during garbage collection.")
return
if os.path.exists(checkpoint_dir):
shutil.rmtree(checkpoint_dir)
def export_model(self, export_formats, export_dir=None):
"""Exports model based on export_formats.
Subclasses should override _export_model() to actually
export model to local directory.
Args:
export_formats (Union[list,str]): Format or list of (str) formats
that should be exported.
export_dir (str): Optional dir to place the exported model.
Defaults to self.logdir.
Returns:
A dict that maps ExportFormats to successfully exported models.
"""
if isinstance(export_formats, str):
export_formats = [export_formats]
export_dir = export_dir or self.logdir
return self._export_model(export_formats, export_dir)
def reset(self, new_config, logger_creator=None):
"""Resets trial for use with new config.
Subclasses should override reset_config() to actually
reset actor behavior for the new config."""
self.config = new_config
self._result_logger.flush()
self._result_logger.close()
self._create_logger(new_config.copy(), logger_creator)
stdout_file = new_config.pop(STDOUT_FILE, None)
stderr_file = new_config.pop(STDERR_FILE, None)
self._close_logfiles()
self._open_logfiles(stdout_file, stderr_file)
return self.reset_config(new_config)
def reset_config(self, new_config):
"""Resets configuration without restarting the trial.
This method is optional, but can be implemented to speed up algorithms
such as PBT, and to allow performance optimizations such as running
experiments with reuse_actors=True.
Args:
new_config (dict): Updated hyperparameter configuration
for the trainable.
Returns:
True if reset was successful else False.
"""
return False
def _create_logger(self, config, logger_creator=None):
"""Create logger from logger creator.
Sets _logdir and _result_logger.
"""
if logger_creator:
self._result_logger = logger_creator(config)
self._logdir = self._result_logger.logdir
else:
logdir_prefix = datetime.today().strftime("%Y-%m-%d_%H-%M-%S")
ray.utils.try_to_create_directory(DEFAULT_RESULTS_DIR)
self._logdir = tempfile.mkdtemp(
prefix=logdir_prefix, dir=DEFAULT_RESULTS_DIR)
self._result_logger = UnifiedLogger(
config, self._logdir, loggers=None)
def _open_logfiles(self, stdout_file, stderr_file):
"""Create loggers. Open stdout and stderr logfiles."""
if stdout_file:
stdout_path = os.path.expanduser(
os.path.join(self._logdir, stdout_file))
self._stdout_fp = open(stdout_path, "a+")
self._stdout_stream = Tee(sys.stdout, self._stdout_fp)
self._stdout_context = redirect_stdout(self._stdout_stream)
self._stdout_context.__enter__()
if stderr_file:
stderr_path = os.path.expanduser(
os.path.join(self._logdir, stderr_file))
self._stderr_fp = open(stderr_path, "a+")
self._stderr_stream = Tee(sys.stderr, self._stderr_fp)
self._stderr_context = redirect_stderr(self._stderr_stream)
self._stderr_context.__enter__()
# Add logging handler to root ray logger
formatter = logging.Formatter("[%(levelname)s %(asctime)s] "
"%(filename)s: %(lineno)d "
"%(message)s")
self._stderr_logging_handler = logging.StreamHandler(
self._stderr_fp)
self._stderr_logging_handler.setFormatter(formatter)
ray.logger.addHandler(self._stderr_logging_handler)
def _close_logfiles(self):
"""Close stdout and stderr logfiles."""
if self._stderr_logging_handler:
ray.logger.removeHandler(self._stderr_logging_handler)
if self._stdout_context:
self._stdout_stream.flush()
self._stdout_context.__exit__(None, None, None)
self._stdout_fp.close()
self._stdout_context = None
if self._stderr_context:
self._stderr_stream.flush()
self._stderr_context.__exit__(None, None, None)
self._stderr_fp.close()
self._stderr_context = None
def stop(self):
"""Releases all resources used by this trainable.
Calls ``Trainable.cleanup`` internally. Subclasses should override
``Trainable.cleanup`` for custom cleanup procedures.
"""
self._result_logger.flush()
self._result_logger.close()
self.cleanup()
self._close_logfiles()
@property
def logdir(self):
"""Directory of the results and checkpoints for this Trainable.
Tune will automatically sync this folder with the driver if execution
is distributed.
Note that the current working directory will also be changed to this.
"""
return os.path.join(self._logdir, "")
@property
def trial_name(self):
"""Trial name for the corresponding trial of this Trainable.
This is not set if not using Tune.
.. code-block:: python
name = self.trial_name
"""
if self._trial_info:
return self._trial_info.trial_name
else:
return "default"
@property
def trial_id(self):
"""Trial ID for the corresponding trial of this Trainable.
This is not set if not using Tune.
.. code-block:: python
trial_id = self.trial_id
"""
if self._trial_info:
return self._trial_info.trial_id
else:
return "default"
@property
def iteration(self):
"""Current training iteration.
This value is automatically incremented every time `train()` is called
and is automatically inserted into the training result dict.
"""
return self._iteration
@property
def training_iteration(self):
"""Current training iteration (same as `self.iteration`).
This value is automatically incremented every time `train()` is called
and is automatically inserted into the training result dict.
"""
return self._iteration
def get_config(self):
"""Returns configuration passed in by Tune."""
return self.config
def step(self):
"""Subclasses should override this to implement train().
The return value will be automatically passed to the loggers. Users
can also return `tune.result.DONE` or `tune.result.SHOULD_CHECKPOINT`
as a key to manually trigger termination or checkpointing of this
trial. Note that manual checkpointing only works when subclassing
Trainables.
.. versionadded:: 0.8.7
Returns:
A dict that describes training progress.
"""
result = self._train()
if self._is_overriden("_train") and log_once("_train"):
logger.warning(
"Trainable._train is deprecated and will be removed in "
"a future version of Ray. Override Trainable.step instead.")
return result
def _train(self):
"""This method is deprecated. Override 'Trainable.step' instead.
.. versionchanged:: 0.8.7
"""
raise NotImplementedError
def save_checkpoint(self, tmp_checkpoint_dir):
"""Subclasses should override this to implement ``save()``.
Warning:
Do not rely on absolute paths in the implementation of
``Trainable.save_checkpoint`` and ``Trainable.load_checkpoint``.
Use ``validate_save_restore`` to catch ``Trainable.save_checkpoint``/
``Trainable.load_checkpoint`` errors before execution.
>>> from ray.tune.utils import validate_save_restore
>>> validate_save_restore(MyTrainableClass)
>>> validate_save_restore(MyTrainableClass, use_object_store=True)
.. versionadded:: 0.8.7
Args:
tmp_checkpoint_dir (str): The directory where the checkpoint
file must be stored. In a Tune run, if the trial is paused,
the provided path may be temporary and moved.
Returns:
A dict or string. If string, the return value is expected to be
prefixed by `tmp_checkpoint_dir`. If dict, the return value will
be automatically serialized by Tune and
passed to ``Trainable.load_checkpoint()``.
Examples:
>>> print(trainable1.save_checkpoint("/tmp/checkpoint_1"))
"/tmp/checkpoint_1/my_checkpoint_file"
>>> print(trainable2.save_checkpoint("/tmp/checkpoint_2"))
{"some": "data"}
>>> trainable.save_checkpoint("/tmp/bad_example")
"/tmp/NEW_CHECKPOINT_PATH/my_checkpoint_file" # This will error.
"""
checkpoint = self._save(tmp_checkpoint_dir)
if self._is_overriden("_save") and log_once("_save"):
logger.warning(
"Trainable._save is deprecated and will be removed in a "
"future version of Ray. Override "
"Trainable.save_checkpoint instead.")
return checkpoint
def _save(self, tmp_checkpoint_dir):
"""This method is deprecated. Override 'save_checkpoint' instead.
.. versionchanged:: 0.8.7
"""
raise NotImplementedError
def load_checkpoint(self, checkpoint):
"""Subclasses should override this to implement restore().
Warning:
In this method, do not rely on absolute paths. The absolute
path of the checkpoint_dir used in ``Trainable.save_checkpoint``
may be changed.
If ``Trainable.save_checkpoint`` returned a prefixed string, the
prefix of the checkpoint string returned by
``Trainable.save_checkpoint`` may be changed.
This is because trial pausing depends on temporary directories.
The directory structure under the checkpoint_dir provided to
``Trainable.save_checkpoint`` is preserved.
See the example below.
.. code-block:: python
class Example(Trainable):
def save_checkpoint(self, checkpoint_path):
print(checkpoint_path)
return os.path.join(checkpoint_path, "my/check/point")
def load_checkpoint(self, checkpoint):
print(checkpoint)
>>> trainer = Example()
>>> obj = trainer.save_to_object() # This is used when PAUSED.
<logdir>/tmpc8k_c_6hsave_to_object/checkpoint_0/my/check/point
>>> trainer.restore_from_object(obj) # Note the different prefix.
<logdir>/tmpb87b5axfrestore_from_object/checkpoint_0/my/check/point
.. versionadded:: 0.8.7
Args:
checkpoint (str|dict): If dict, the return value is as
returned by `save_checkpoint`. If a string, then it is
a checkpoint path that may have a different prefix than that
returned by `save_checkpoint`. The directory structure
underneath the `checkpoint_dir` `save_checkpoint` is preserved.
"""
self._restore(checkpoint)
if self._is_overriden("_restore") and log_once("_restore"):
logger.warning(
"Trainable._restore is deprecated and will be removed in a "
"future version of Ray. Override Trainable.load_checkpoint "
"instead.")
def _restore(self, checkpoint):
"""This method is deprecated. Override 'load_checkpoint' instead.
.. versionchanged:: 0.8.7
"""
raise NotImplementedError
def setup(self, config):
"""Subclasses should override this for custom initialization.
.. versionadded:: 0.8.7
Args:
config (dict): Hyperparameters and other configs given.
Copy of `self.config`.
"""
self._setup(config)
if self._is_overriden("_setup") and log_once("_setup"):
logger.warning(
"Trainable._setup is deprecated and will be removed in "
"a future version of Ray. Override Trainable.setup instead.")
def _setup(self, config):
"""This method is deprecated. Override 'setup' instead.
.. versionchanged:: 0.8.7
"""
pass
def log_result(self, result):
"""Subclasses can optionally override this to customize logging.
The logging here is done on the worker process rather than
the driver. You may want to turn off driver logging via the
``loggers`` parameter in ``tune.run`` when overriding this function.
.. versionadded:: 0.8.7
Args:
result (dict): Training result returned by step().
"""
self._log_result(result)
if self._is_overriden("_log_result") and log_once("_log_result"):
logger.warning(
"Trainable._log_result is deprecated and will be removed in "
"a future version of Ray. Override "
"Trainable.log_result instead.")
def _log_result(self, result):
"""This method is deprecated. Override 'log_result' instead.
.. versionchanged:: 0.8.7
"""
self._result_logger.on_result(result)
def cleanup(self):
"""Subclasses should override this for any cleanup on stop.
If any Ray actors are launched in the Trainable (i.e., with a RLlib
trainer), be sure to kill the Ray actor process here.
You can kill a Ray actor by calling `actor.__ray_terminate__.remote()`
on the actor.
.. versionadded:: 0.8.7
"""
self._stop()
if self._is_overriden("_stop") and log_once("trainable.cleanup"):
logger.warning(
"Trainable._stop is deprecated and will be removed in "
"a future version of Ray. Override Trainable.cleanup instead.")
def _stop(self):
"""This method is deprecated. Override 'cleanup' instead.
.. versionchanged:: 0.8.7
"""
pass
def _export_model(self, export_formats, export_dir):
"""Subclasses should override this to export model.
Args:
export_formats (list): List of formats that should be exported.
export_dir (str): Directory to place exported models.
Return:
A dict that maps ExportFormats to successfully exported models.
"""
return {}
def _is_overriden(self, key):
return getattr(self, key).__code__ != getattr(Trainable, key).__code__
class Trial(object):
"""A trial object holds the state for one model training run.
Trials are themselves managed by the TrialRunner class, which implements
the event loop for submitting trial runs to a Ray cluster.
Trials start in the PENDING state, and transition to RUNNING once started.
On error it transitions to ERROR, otherwise TERMINATED on success.
"""
PENDING = "PENDING"
RUNNING = "RUNNING"
PAUSED = "PAUSED"
TERMINATED = "TERMINATED"
ERROR = "ERROR"
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,
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._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",
"config",
"loggers",
"sync_function",
"last_result",
"results",
"best_result",
"param_config",
"extra_arg",
]
if trial_name_creator:
self.custom_trial_name = trial_name_creator(self)
@classmethod
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)
@classmethod
def generate_id(cls):
return binary_to_hex(_random_string())[:8]
def init_logger(self):
"""Init logger."""
if not self.result_logger:
if not os.path.exists(self.local_dir):
os.makedirs(self.local_dir)
if not self.logdir:
self.logdir = tempfile.mkdtemp(
prefix="{}_{}".format(
str(self)[:MAX_LEN_IDENTIFIER], date_str()),
dir=self.local_dir)
elif not os.path.exists(self.logdir):
os.makedirs(self.logdir)
self.result_logger = UnifiedLogger(
self.config,
self.logdir,
upload_uri=self.upload_dir,
loggers=self.loggers,
sync_function=self.sync_function)
def update_resources(self, cpu, gpu, **kwargs):
"""EXPERIMENTAL: Updates the resource requirements.
Should only be called when the trial is not running.
Raises:
ValueError if trial status is running.
"""
if self.status is Trial.RUNNING:
raise ValueError("Cannot update resources while Trial is running.")
self.resources = Resources(cpu, gpu, **kwargs)
def sync_logger_to_new_location(self, worker_ip):
"""Updates the logger location.
Also pushes logdir to worker_ip, allowing for cross-node recovery.
"""
if self.result_logger:
self.result_logger.sync_results_to_new_location(worker_ip)
def close_logger(self):
"""Close logger."""
if self.result_logger:
self.result_logger.close()
self.result_logger = None
def write_error_log(self, error_msg):
if error_msg and self.logdir:
self.num_failures += 1 # may be moved to outer scope?
error_file = os.path.join(self.logdir,
"error_{}.txt".format(date_str()))
with open(error_file, "w") as f:
f.write(error_msg)
self.error_file = error_file
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 should_checkpoint(self):
"""Whether this trial is due for checkpointing."""
result = self.last_result or {}
if result.get(DONE) and self.checkpoint_at_end:
return True
if self.checkpoint_freq:
return result.get(TRAINING_ITERATION,
0) % self.checkpoint_freq == 0
else:
return False
def progress_string(self):
"""Returns a progress message for printing out to the console."""
if not self.last_result:
return self._status_string()
def location_string(hostname, pid):
if hostname == os.uname()[1]:
return 'pid={}'.format(pid)
else:
return '{} pid={}'.format(hostname, pid)
pieces = [
'{}'.format(self._status_string()), '[{}]'.format(
self.resources.summary_string()), '[{}]'.format(
location_string(
self.last_result.get(HOSTNAME),
self.last_result.get(PID))), '{} s'.format(
int(self.last_result.get(TIME_TOTAL_S)))
]
if self.last_result.get(TRAINING_ITERATION) is not None:
pieces.append('{} iter'.format(
self.last_result[TRAINING_ITERATION]))
if self.last_result.get(TIMESTEPS_TOTAL) is not None:
pieces.append('{} ts'.format(self.last_result[TIMESTEPS_TOTAL]))
if self.last_result.get(EPISODE_REWARD_MEAN) is not None:
pieces.append('{} rew'.format(
format(self.last_result[EPISODE_REWARD_MEAN], '.3g')))
if self.last_result.get(MEAN_LOSS) is not None:
pieces.append('{} loss'.format(
format(self.last_result[MEAN_LOSS], '.3g')))
if self.last_result.get(MEAN_ACCURACY) is not None:
pieces.append('{} acc'.format(
format(self.last_result[MEAN_ACCURACY], '.3g')))
return ', '.join(pieces)
def _status_string(self):
return "{}{}".format(
self.status, ", {} failures: {}".format(self.num_failures,
self.error_file)
if self.error_file else "")
def has_checkpoint(self):
return self._checkpoint.value is not None
def clear_checkpoint(self):
self._checkpoint.value = None
def should_recover(self):
"""Returns whether the trial qualifies for restoring.
This is if a checkpoint frequency is set and has not failed more than
max_failures. This may return true even when there may not yet
be a checkpoint.
"""
return (self.checkpoint_freq > 0
and (self.num_failures < self.max_failures
or self.max_failures < 0))
def update_last_result(self, result, terminate=False):
if terminate:
result.update(done=True)
if self.verbose and (terminate or time.time() - self.last_debug >
DEBUG_PRINT_INTERVAL):
print("Result for {}:".format(self))
print(" {}".format(pretty_print(result).replace("\n", "\n ")))
self.last_debug = time.time()
self.last_result = result
self.last_update_time = time.time()
self.result_logger.on_result(self.last_result)
def _get_trainable_cls(self):
return ray.tune.registry._global_registry.get(
ray.tune.registry.TRAINABLE_CLASS, self.trainable_name)
def set_verbose(self, verbose):
self.verbose = verbose
def is_finished(self):
return self.status in [Trial.TERMINATED, Trial.ERROR]
def __repr__(self):
return str(self)
def __str__(self):
"""Combines ``env`` with ``trainable_name`` and ``experiment_tag``.
Can be overriden with a custom string creator.
"""
if self.custom_trial_name:
return self.custom_trial_name
if "env" in self.config:
env = self.config["env"]
if isinstance(env, type):
env = env.__name__
identifier = "{}_{}".format(self.trainable_name, env)
else:
identifier = self.trainable_name
if self.experiment_tag:
identifier += "_" + self.experiment_tag
return identifier.replace("/", "_")
def __getstate__(self):
"""Memento generator for Trial.
Sets RUNNING trials to PENDING, and flushes the result logger.
Note this can only occur if the trial holds a DISK checkpoint.
"""
assert self._checkpoint.storage == Checkpoint.DISK, (
"Checkpoint must not be in-memory.")
state = self.__dict__.copy()
state["resources"] = resources_to_json(self.resources)
for key in self._nonjson_fields:
state[key] = binary_to_hex(cloudpickle.dumps(state.get(key)))
state["runner"] = None
state["result_logger"] = None
if self.result_logger:
self.result_logger.flush()
state["__logger_started__"] = True
else:
state["__logger_started__"] = False
return copy.deepcopy(state)
def __setstate__(self, state):
logger_started = state.pop("__logger_started__")
state["resources"] = json_to_resources(state["resources"])
if state["status"] == Trial.RUNNING:
state["status"] = Trial.PENDING
for key in self._nonjson_fields:
state[key] = cloudpickle.loads(hex_to_binary(state[key]))
self.__dict__.update(state)
Trial._registration_check(self.trainable_name)
if logger_started:
self.init_logger()
class Trial:
"""A trial object holds the state for one model training run.
Trials are themselves managed by the TrialRunner class, which implements
the event loop for submitting trial runs to a Ray cluster.
Trials start in the PENDING state, and transition to RUNNING once started.
On error it transitions to ERROR, otherwise TERMINATED on success.
"""
PENDING = "PENDING"
RUNNING = "RUNNING"
PAUSED = "PAUSED"
TERMINATED = "TERMINATED"
ERROR = "ERROR"
def __init__(self,
trainable_name,
config=None,
trial_id=None,
local_dir=DEFAULT_RESULTS_DIR,
evaluated_params=None,
experiment_tag="",
resources=None,
stopping_criterion=None,
remote_checkpoint_dir=None,
checkpoint_freq=0,
checkpoint_at_end=False,
sync_on_checkpoint=True,
keep_checkpoints_num=None,
checkpoint_score_attr=TRAINING_ITERATION,
export_formats=None,
restore_path=None,
trial_name_creator=None,
loggers=None,
sync_to_driver_fn=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.
"""
validate_trainable(trainable_name)
# Trial config
self.trainable_name = trainable_name
self.trial_id = Trial.generate_id() if trial_id is None else trial_id
self.config = config or {}
self.local_dir = local_dir # This remains unexpanded for syncing.
#: Parameters that Tune varies across searches.
self.evaluated_params = evaluated_params or {}
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.location = Location()
self.resources = resources or Resources(cpu=1, gpu=0)
self.stopping_criterion = stopping_criterion or {}
self.loggers = loggers
self.sync_to_driver_fn = sync_to_driver_fn
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")
# stores in memory max/min/last result for each metric by trial
self.metric_analysis = {}
self.export_formats = export_formats
self.status = Trial.PENDING
self.start_time = None
self.logdir = None
self.runner = None
self.result_logger = None
self.last_debug = 0
self.error_file = None
self.error_msg = None
self.custom_trial_name = None
# Checkpointing fields
if remote_checkpoint_dir:
self.remote_checkpoint_dir_prefix = remote_checkpoint_dir
else:
self.remote_checkpoint_dir_prefix = None
self.checkpoint_freq = checkpoint_freq
self.checkpoint_at_end = checkpoint_at_end
self.sync_on_checkpoint = sync_on_checkpoint
newest_checkpoint = Checkpoint(Checkpoint.PERSISTENT, restore_path)
self.checkpoint_manager = CheckpointManager(
keep_checkpoints_num, checkpoint_score_attr,
checkpoint_deleter(str(self), self.runner))
self.checkpoint_manager.newest_checkpoint = newest_checkpoint
# Restoration fields
self.restoring_from = None
self.num_failures = 0
self.num_consecutive_start_attempts = 0
# AutoML fields
self.results = None
self.best_result = None
self.param_config = None
self.extra_arg = None
self._nonjson_fields = [
"loggers",
"sync_to_driver_fn",
"results",
"best_result",
"param_config",
"extra_arg",
]
if trial_name_creator:
self.custom_trial_name = trial_name_creator(self)
@property
def node_ip(self):
return self.location.hostname
@property
def checkpoint(self):
return self.checkpoint_manager.newest_checkpoint
@classmethod
def generate_id(cls):
return str(uuid.uuid1().hex)[:8]
@property
def remote_checkpoint_dir(self):
assert self.logdir, "Trial {}: logdir not initialized.".format(self)
if not self.remote_checkpoint_dir_prefix:
return None
logdir_name = os.path.basename(self.logdir)
return os.path.join(self.remote_checkpoint_dir_prefix, logdir_name)
@classmethod
def create_logdir(cls, identifier, local_dir):
local_dir = os.path.expanduser(local_dir)
os.makedirs(local_dir, exist_ok=True)
return tempfile.mkdtemp(prefix="{}_{}".format(
identifier[:MAX_LEN_IDENTIFIER], date_str()),
dir=local_dir)
def init_logger(self):
"""Init logger."""
if not self.result_logger:
if not self.logdir:
self.logdir = Trial.create_logdir(str(self), self.local_dir)
else:
os.makedirs(self.logdir, exist_ok=True)
self.result_logger = UnifiedLogger(
self.config,
self.logdir,
trial=self,
loggers=self.loggers,
sync_function=self.sync_to_driver_fn)
def update_resources(self, cpu, gpu, **kwargs):
"""EXPERIMENTAL: Updates the resource requirements.
Should only be called when the trial is not running.
Raises:
ValueError if trial status is running.
"""
if self.status is Trial.RUNNING:
raise ValueError("Cannot update resources while Trial is running.")
self.resources = Resources(cpu, gpu, **kwargs)
def set_runner(self, runner):
self.runner = runner
self.checkpoint_manager.delete = checkpoint_deleter(str(self), runner)
def set_location(self, location):
"""Sets the location of the trial."""
self.location = location
def set_status(self, status):
"""Sets the status of the trial."""
self.status = status
if status == Trial.RUNNING:
if self.start_time is None:
self.start_time = time.time()
def close_logger(self):
"""Closes logger."""
if self.result_logger:
self.result_logger.close()
self.result_logger = None
def write_error_log(self, error_msg):
if error_msg and self.logdir:
self.num_failures += 1
self.error_file = os.path.join(self.logdir, "error.txt")
with open(self.error_file, "a+") as f:
f.write("Failure # {} (occurred at {})\n".format(
self.num_failures, date_str()))
f.write(error_msg + "\n")
self.error_msg = error_msg
def should_stop(self, result):
"""Whether the given result meets this trial's stopping criteria."""
if result.get(DONE):
return True
if callable(self.stopping_criterion):
return self.stopping_criterion(self.trial_id, result)
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 should_checkpoint(self):
"""Whether this trial is due for checkpointing."""
result = self.last_result or {}
if result.get(DONE) and self.checkpoint_at_end:
return True
return (self.checkpoint_freq and
result.get(TRAINING_ITERATION, 0) % self.checkpoint_freq == 0)
def has_checkpoint(self):
return self.checkpoint.value is not None
def clear_checkpoint(self):
self.checkpoint.value = None
self.restoring_from = None
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 on_restore(self):
"""Handles restoration completion."""
assert self.is_restoring
self.last_result = self.restoring_from.result
self.restoring_from = None
def should_recover(self):
"""Returns whether the trial qualifies for retrying.
This is if the trial has not failed more than max_failures. Note this
may return true even when there is no checkpoint, either because
`self.checkpoint_freq` is `0` or because the trial failed before
a checkpoint has been made.
"""
return self.num_failures < self.max_failures or self.max_failures < 0
def update_last_result(self, result, terminate=False):
result.update(trial_id=self.trial_id, done=terminate)
if self.experiment_tag:
result.update(experiment_tag=self.experiment_tag)
if self.verbose and (terminate or time.time() - self.last_debug >
DEBUG_PRINT_INTERVAL):
print("Result for {}:".format(self))
print(" {}".format(pretty_print(result).replace("\n", "\n ")))
self.last_debug = time.time()
self.set_location(Location(result.get("node_ip"), result.get("pid")))
self.last_result = result
self.last_update_time = time.time()
self.result_logger.on_result(self.last_result)
for metric, value in flatten_dict(result).items():
if isinstance(value, Number):
if metric not in self.metric_analysis:
self.metric_analysis[metric] = {
"max": value,
"min": value,
"last": value
}
else:
self.metric_analysis[metric]["max"] = max(
value, self.metric_analysis[metric]["max"])
self.metric_analysis[metric]["min"] = min(
value, self.metric_analysis[metric]["min"])
self.metric_analysis[metric]["last"] = value
def get_trainable_cls(self):
return get_trainable_cls(self.trainable_name)
def set_verbose(self, verbose):
self.verbose = verbose
def is_finished(self):
return self.status in [Trial.ERROR, Trial.TERMINATED]
@property
def is_restoring(self):
return self.restoring_from is not None
def __repr__(self):
return str(self)
def __str__(self):
"""Combines ``env`` with ``trainable_name`` and ``trial_id``.
Can be overridden with a custom string creator.
"""
if self.custom_trial_name:
return self.custom_trial_name
if "env" in self.config:
env = self.config["env"]
if isinstance(env, type):
env = env.__name__
identifier = "{}_{}".format(self.trainable_name, env)
else:
identifier = self.trainable_name
identifier += "_" + self.trial_id
return identifier.replace("/", "_")
def __getstate__(self):
"""Memento generator for Trial.
Sets RUNNING trials to PENDING, and flushes the result logger.
Note this can only occur if the trial holds a PERSISTENT checkpoint.
"""
assert self.checkpoint.storage == Checkpoint.PERSISTENT, (
"Checkpoint must not be in-memory.")
state = self.__dict__.copy()
state["resources"] = resources_to_json(self.resources)
for key in self._nonjson_fields:
state[key] = binary_to_hex(cloudpickle.dumps(state.get(key)))
state["runner"] = None
state["result_logger"] = None
if self.result_logger:
self.result_logger.flush(sync_down=False)
state["__logger_started__"] = True
else:
state["__logger_started__"] = False
return copy.deepcopy(state)
def __setstate__(self, state):
logger_started = state.pop("__logger_started__")
state["resources"] = json_to_resources(state["resources"])
if state["status"] == Trial.RUNNING:
state["status"] = Trial.PENDING
for key in self._nonjson_fields:
state[key] = cloudpickle.loads(hex_to_binary(state[key]))
self.__dict__.update(state)
validate_trainable(self.trainable_name)
if logger_started:
self.init_logger()
class Trainable:
"""Abstract class for trainable models, functions, etc.
A call to ``train()`` on a trainable will execute one logical iteration of
training. As a rule of thumb, the execution time of one train call should
be large enough to avoid overheads (i.e. more than a few seconds), but
short enough to report progress periodically (i.e. at most a few minutes).
Calling ``save()`` should save the training state of a trainable to disk,
and ``restore(path)`` should restore a trainable to the given state.
Generally you only need to implement ``setup``, ``step``,
``save_checkpoint``, and ``load_checkpoint`` when subclassing Trainable.
Other implementation methods that may be helpful to override are
``log_result``, ``reset_config``, ``cleanup``, and ``_export_model``.
When using Tune, Tune will convert this class into a Ray actor, which
runs on a separate process. Tune will also change the current working
directory of this process to ``self.logdir``. This is designed so that
different trials that run on the same physical node won't accidently
write to the same location and overstep each other.
If you want to know the orginal working directory path on the driver node,
you can do so through env variable "TUNE_ORIG_WORKING_DIR".
It is advised that you access this path for read only purposes and you
need to make sure that the path exists on the remote nodes.
This class supports checkpointing to and restoring from remote storage.
"""
_sync_function_tpl = None
def __init__(
self,
config: Dict[str, Any] = None,
logger_creator: Callable[[Dict[str, Any]], Logger] = None,
remote_checkpoint_dir: Optional[str] = None,
sync_function_tpl: Optional[str] = None,
):
"""Initialize an Trainable.
Sets up logging and points ``self.logdir`` to a directory in which
training outputs should be placed.
Subclasses should prefer defining ``setup()`` instead of overriding
``__init__()`` directly.
Args:
config (dict): Trainable-specific configuration data. By default
will be saved as ``self.config``.
logger_creator (func): Function that creates a ray.tune.Logger
object. If unspecified, a default logger is created.
remote_checkpoint_dir (str): Upload directory (S3 or GS path).
This is **per trial** directory,
which is different from **per checkpoint** directory.
sync_function_tpl (str): Sync function template to use. Defaults
to `cls._sync_function` (which defaults to `None`).
"""
self._experiment_id = uuid.uuid4().hex
self.config = config or {}
trial_info = self.config.pop(TRIAL_INFO, None)
if self.is_actor():
disable_ipython()
self._result_logger = self._logdir = None
self._create_logger(self.config, logger_creator)
self._stdout_context = self._stdout_fp = self._stdout_stream = None
self._stderr_context = self._stderr_fp = self._stderr_stream = None
self._stderr_logging_handler = None
stdout_file = self.config.pop(STDOUT_FILE, None)
stderr_file = self.config.pop(STDERR_FILE, None)
self._open_logfiles(stdout_file, stderr_file)
self._iteration = 0
self._time_total = 0.0
self._timesteps_total = None
self._episodes_total = None
self._time_since_restore = 0.0
self._timesteps_since_restore = 0
self._iterations_since_restore = 0
self._restored = False
self._trial_info = trial_info
self._stdout_file = stdout_file
self._stderr_file = stderr_file
start_time = time.time()
self._local_ip = self.get_current_ip()
self.setup(copy.deepcopy(self.config))
setup_time = time.time() - start_time
if setup_time > SETUP_TIME_THRESHOLD:
logger.info("Trainable.setup took {:.3f} seconds. If your "
"trainable is slow to initialize, consider setting "
"reuse_actors=True to reduce actor creation "
"overheads.".format(setup_time))
log_sys_usage = self.config.get("log_sys_usage", False)
self._start_time = start_time
self._warmup_time = None
self._monitor = UtilMonitor(start=log_sys_usage)
self.remote_checkpoint_dir = remote_checkpoint_dir
self.sync_function_tpl = sync_function_tpl or self._sync_function_tpl
self.storage_client = None
if self.uses_cloud_checkpointing:
self.storage_client = self._create_storage_client()
@property
def uses_cloud_checkpointing(self):
return bool(self.remote_checkpoint_dir)
def _create_storage_client(self):
"""Returns a storage client."""
return get_sync_client(
self.sync_function_tpl) or get_cloud_sync_client(
self.remote_checkpoint_dir)
def _storage_path(self, local_path):
"""Converts a `local_path` to be based off of
`self.remote_checkpoint_dir`."""
rel_local_path = os.path.relpath(local_path, self.logdir)
return os.path.join(self.remote_checkpoint_dir, rel_local_path)
@classmethod
def default_resource_request(
cls, config: Dict[str,
Any]) -> Union[Resources, PlacementGroupFactory]:
"""Provides a static resource requirement for the given configuration.
This can be overridden by sub-classes to set the correct trial resource
allocation, so the user does not need to.
.. code-block:: python
@classmethod
def default_resource_request(cls, config):
return PlacementGroupFactory([{"CPU": 1}, {"CPU": 1}]])
Args:
config[Dict[str, Any]]: The Trainable's config dict.
Returns:
Union[Resources, PlacementGroupFactory]: A Resources object or
PlacementGroupFactory consumed by Tune for queueing.
"""
return None
@classmethod
def resource_help(cls, config):
"""Returns a help string for configuring this trainable's resources.
Args:
config (dict): The Trainer's config dict.
"""
return ""
def get_current_ip(self):
self._local_ip = ray.util.get_node_ip_address()
return self._local_ip
def get_auto_filled_metrics(
self,
now: Optional[datetime] = None,
time_this_iter: Optional[float] = None,
debug_metrics_only: bool = False,
) -> dict:
"""Return a dict with metrics auto-filled by the trainable.
If ``debug_metrics_only`` is True, only metrics that don't
require at least one iteration will be returned
(``ray.tune.result.DEBUG_METRICS``).
"""
if now is None:
now = datetime.today()
autofilled = {
TRIAL_ID: self.trial_id,
"experiment_id": self._experiment_id,
"date": now.strftime("%Y-%m-%d_%H-%M-%S"),
"timestamp": int(time.mktime(now.timetuple())),
TIME_THIS_ITER_S: time_this_iter,
TIME_TOTAL_S: self._time_total,
PID: os.getpid(),
HOSTNAME: platform.node(),
NODE_IP: self._local_ip,
"config": self.config,
"time_since_restore": self._time_since_restore,
"timesteps_since_restore": self._timesteps_since_restore,
"iterations_since_restore": self._iterations_since_restore,
"warmup_time": self._warmup_time,
}
if debug_metrics_only:
autofilled = {
k: v
for k, v in autofilled.items() if k in DEBUG_METRICS
}
return autofilled
def is_actor(self):
try:
actor_id = ray.worker.global_worker.actor_id
return actor_id != actor_id.nil()
except Exception:
# If global_worker is not instantiated, we're not in an actor
return False
def train_buffered(self,
buffer_time_s: float,
max_buffer_length: int = 1000):
"""Runs multiple iterations of training.
Calls ``train()`` internally. Collects and combines multiple results.
This function will run ``self.train()`` repeatedly until one of
the following conditions is met: 1) the maximum buffer length is
reached, 2) the maximum buffer time is reached, or 3) a checkpoint
was created. Even if the maximum time is reached, it will always
block until at least one result is received.
Args:
buffer_time_s (float): Maximum time to buffer. The next result
received after this amount of time has passed will return
the whole buffer.
max_buffer_length (int): Maximum number of results to buffer.
"""
results = []
now = time.time()
send_buffer_at = now + buffer_time_s
while now < send_buffer_at or not results: # At least one result
result = self.train()
results.append(result)
if result.get(DONE, False):
# If the trial is done, return
break
elif result.get(SHOULD_CHECKPOINT, False):
# If a checkpoint was created, return
break
elif result.get(RESULT_DUPLICATE):
# If the function API trainable completed, return
break
elif len(results) >= max_buffer_length:
# If the buffer is full, return
break
now = time.time()
return results
def train(self):
"""Runs one logical iteration of training.
Calls ``step()`` internally. Subclasses should override ``step()``
instead to return results.
This method automatically fills the following fields in the result:
`done` (bool): training is terminated. Filled only if not provided.
`time_this_iter_s` (float): Time in seconds this iteration
took to run. This may be overridden in order to override the
system-computed time difference.
`time_total_s` (float): Accumulated time in seconds for this
entire experiment.
`experiment_id` (str): Unique string identifier
for this experiment. This id is preserved
across checkpoint / restore calls.
`training_iteration` (int): The index of this
training iteration, e.g. call to train(). This is incremented
after `step()` is called.
`pid` (str): The pid of the training process.
`date` (str): A formatted date of when the result was processed.
`timestamp` (str): A UNIX timestamp of when the result
was processed.
`hostname` (str): Hostname of the machine hosting the training
process.
`node_ip` (str): Node ip of the machine hosting the training
process.
Returns:
A dict that describes training progress.
"""
if self._warmup_time is None:
self._warmup_time = time.time() - self._start_time
start = time.time()
result = self.step()
assert isinstance(result, dict), "step() needs to return a dict."
# We do not modify internal state nor update this result if duplicate.
if RESULT_DUPLICATE in result:
return result
result = result.copy()
self._iteration += 1
self._iterations_since_restore += 1
if result.get(TIME_THIS_ITER_S) is not None:
time_this_iter = result[TIME_THIS_ITER_S]
else:
time_this_iter = time.time() - start
self._time_total += time_this_iter
self._time_since_restore += time_this_iter
result.setdefault(DONE, False)
# self._timesteps_total should only be tracked if increments provided
if result.get(TIMESTEPS_THIS_ITER) is not None:
if self._timesteps_total is None:
self._timesteps_total = 0
self._timesteps_total += result[TIMESTEPS_THIS_ITER]
self._timesteps_since_restore += result[TIMESTEPS_THIS_ITER]
# self._episodes_total should only be tracked if increments provided
if result.get(EPISODES_THIS_ITER) is not None:
if self._episodes_total is None:
self._episodes_total = 0
self._episodes_total += result[EPISODES_THIS_ITER]
# self._timesteps_total should not override user-provided total
result.setdefault(TIMESTEPS_TOTAL, self._timesteps_total)
result.setdefault(EPISODES_TOTAL, self._episodes_total)
result.setdefault(TRAINING_ITERATION, self._iteration)
# Provides auto-filled neg_mean_loss for avoiding regressions
if result.get("mean_loss"):
result.setdefault("neg_mean_loss", -result["mean_loss"])
now = datetime.today()
result.update(self.get_auto_filled_metrics(now, time_this_iter))
monitor_data = self._monitor.get_data()
if monitor_data:
result.update(monitor_data)
self.log_result(result)
if self._stdout_context:
self._stdout_stream.flush()
if self._stderr_context:
self._stderr_stream.flush()
return result
def get_state(self):
return {
"experiment_id": self._experiment_id,
"iteration": self._iteration,
"timesteps_total": self._timesteps_total,
"time_total": self._time_total,
"episodes_total": self._episodes_total,
"ray_version": ray.__version__,
}
def save(self, checkpoint_dir=None) -> str:
"""Saves the current model state to a checkpoint.
Subclasses should override ``save_checkpoint()`` instead to save state.
This method dumps additional metadata alongside the saved path.
If a remote checkpoint dir is given, this will also sync up to remote
storage.
Args:
checkpoint_dir (str): Optional dir to place the checkpoint.
Returns:
str: path that points to xxx.pkl file.
Note the return path should match up with what is expected of
`restore()`.
"""
checkpoint_dir = TrainableUtil.make_checkpoint_dir(
checkpoint_dir or self.logdir, index=self.iteration)
checkpoint = self.save_checkpoint(checkpoint_dir)
trainable_state = self.get_state()
checkpoint_path = TrainableUtil.process_checkpoint(
checkpoint,
parent_dir=checkpoint_dir,
trainable_state=trainable_state)
self._postprocess_checkpoint(checkpoint_dir)
# Maybe sync to cloud
self._maybe_save_to_cloud(checkpoint_dir)
return checkpoint_path
def _postprocess_checkpoint(self, checkpoint_path: str):
"""Run extra postprocessing before the checkpoint is saved to cloud."""
pass
def _maybe_save_to_cloud(self, checkpoint_dir):
# Derived classes like the FunctionRunner might call this
if self.uses_cloud_checkpointing:
self.storage_client.sync_up(checkpoint_dir,
self._storage_path(checkpoint_dir))
self.storage_client.wait_or_retry()
def save_to_object(self):
"""Saves the current model state to a Python object.
It also saves to disk but does not return the checkpoint path.
Returns:
Object holding checkpoint data.
"""
tmpdir = tempfile.mkdtemp("save_to_object", dir=self.logdir)
checkpoint_path = self.save(tmpdir)
# Save all files in subtree and delete the tmpdir.
obj = TrainableUtil.checkpoint_to_object(checkpoint_path)
shutil.rmtree(tmpdir)
return obj
def restore(self, checkpoint_path):
"""Restores training state from a given model checkpoint.
These checkpoints are returned from calls to save().
Subclasses should override ``load_checkpoint()`` instead to
restore state.
This method restores additional metadata saved with the checkpoint.
`checkpoint_path` should match with the return from ``save()``.
`checkpoint_path` can be
`~/ray_results/exp/MyTrainable_abc/
checkpoint_00000/checkpoint`. Or,
`~/ray_results/exp/MyTrainable_abc/checkpoint_00000`.
`self.logdir` should generally be corresponding to `checkpoint_path`,
for example, `~/ray_results/exp/MyTrainable_abc`.
`self.remote_checkpoint_dir` in this case, is something like,
`REMOTE_CHECKPOINT_BUCKET/exp/MyTrainable_abc`
"""
if self.uses_cloud_checkpointing:
rel_checkpoint_dir = TrainableUtil.find_rel_checkpoint_dir(
self.logdir, checkpoint_path)
self.storage_client.sync_down(
os.path.join(self.remote_checkpoint_dir, rel_checkpoint_dir),
os.path.join(self.logdir, rel_checkpoint_dir),
)
self.storage_client.wait_or_retry()
# Ensure TrialCheckpoints are converted
if isinstance(checkpoint_path, TrialCheckpoint):
checkpoint_path = checkpoint_path.local_path
with open(checkpoint_path + ".tune_metadata", "rb") as f:
metadata = pickle.load(f)
self._experiment_id = metadata["experiment_id"]
self._iteration = metadata["iteration"]
self._timesteps_total = metadata["timesteps_total"]
self._time_total = metadata["time_total"]
self._episodes_total = metadata["episodes_total"]
saved_as_dict = metadata["saved_as_dict"]
if saved_as_dict:
with open(checkpoint_path, "rb") as loaded_state:
checkpoint_dict = pickle.load(loaded_state)
checkpoint_dict.update(tune_checkpoint_path=checkpoint_path)
self.load_checkpoint(checkpoint_dict)
else:
self.load_checkpoint(checkpoint_path)
self._time_since_restore = 0.0
self._timesteps_since_restore = 0
self._iterations_since_restore = 0
self._restored = True
logger.info("Restored on %s from checkpoint: %s",
self.get_current_ip(), checkpoint_path)
state = {
"_iteration": self._iteration,
"_timesteps_total": self._timesteps_total,
"_time_total": self._time_total,
"_episodes_total": self._episodes_total,
}
logger.info("Current state after restoring: %s", state)
def restore_from_object(self, obj):
"""Restores training state from a checkpoint object.
These checkpoints are returned from calls to save_to_object().
"""
tmpdir = tempfile.mkdtemp("restore_from_object", dir=self.logdir)
checkpoint_path = TrainableUtil.create_from_pickle(obj, tmpdir)
self.restore(checkpoint_path)
shutil.rmtree(tmpdir)
def delete_checkpoint(self, checkpoint_path):
"""Deletes local copy of checkpoint.
Args:
checkpoint_path (str): Path to checkpoint.
"""
# Ensure TrialCheckpoints are converted
if isinstance(checkpoint_path, TrialCheckpoint):
checkpoint_path = checkpoint_path.local_path
try:
checkpoint_dir = TrainableUtil.find_checkpoint_dir(checkpoint_path)
except FileNotFoundError:
# The checkpoint won't exist locally if the
# trial was rescheduled to another worker.
logger.debug(
f"Local checkpoint not found during garbage collection: "
f"{self.trial_id} - {checkpoint_path}")
return
else:
if self.uses_cloud_checkpointing:
self.storage_client.delete(self._storage_path(checkpoint_dir))
self.storage_client.wait_or_retry()
if os.path.exists(checkpoint_dir):
shutil.rmtree(checkpoint_dir)
def export_model(self, export_formats, export_dir=None):
"""Exports model based on export_formats.
Subclasses should override _export_model() to actually
export model to local directory.
Args:
export_formats (Union[list,str]): Format or list of (str) formats
that should be exported.
export_dir (str): Optional dir to place the exported model.
Defaults to self.logdir.
Returns:
A dict that maps ExportFormats to successfully exported models.
"""
if isinstance(export_formats, str):
export_formats = [export_formats]
export_dir = export_dir or self.logdir
return self._export_model(export_formats, export_dir)
def reset(self, new_config, logger_creator=None):
"""Resets trial for use with new config.
Subclasses should override reset_config() to actually
reset actor behavior for the new config."""
self.config = new_config
trial_info = new_config.pop(TRIAL_INFO, None)
if trial_info:
self._trial_info = trial_info
self._result_logger.flush()
self._result_logger.close()
if logger_creator:
logger.debug("Logger reset.")
self._create_logger(new_config.copy(), logger_creator)
else:
logger.debug("Did not reset logger. Got: "
f"trainable.reset(logger_creator={logger_creator}).")
stdout_file = new_config.pop(STDOUT_FILE, None)
stderr_file = new_config.pop(STDERR_FILE, None)
self._close_logfiles()
self._open_logfiles(stdout_file, stderr_file)
success = self.reset_config(new_config)
if not success:
return False
# Reset attributes. Will be overwritten by `restore` if a checkpoint
# is provided.
self._iteration = 0
self._time_total = 0.0
self._timesteps_total = None
self._episodes_total = None
self._time_since_restore = 0.0
self._timesteps_since_restore = 0
self._iterations_since_restore = 0
self._restored = False
return True
def reset_config(self, new_config):
"""Resets configuration without restarting the trial.
This method is optional, but can be implemented to speed up algorithms
such as PBT, and to allow performance optimizations such as running
experiments with reuse_actors=True.
Args:
new_config (dict): Updated hyperparameter configuration
for the trainable.
Returns:
True if reset was successful else False.
"""
return False
def _create_logger(
self,
config: Dict[str, Any],
logger_creator: Callable[[Dict[str, Any]], Logger] = None,
):
"""Create logger from logger creator.
Sets _logdir and _result_logger.
`_logdir` is the **per trial** directory for the Trainable.
"""
if logger_creator:
self._result_logger = logger_creator(config)
self._logdir = self._result_logger.logdir
else:
from ray.tune.logger import UnifiedLogger
logdir_prefix = datetime.today().strftime("%Y-%m-%d_%H-%M-%S")
ray._private.utils.try_to_create_directory(DEFAULT_RESULTS_DIR)
self._logdir = tempfile.mkdtemp(prefix=logdir_prefix,
dir=DEFAULT_RESULTS_DIR)
self._result_logger = UnifiedLogger(config,
self._logdir,
loggers=None)
def _open_logfiles(self, stdout_file, stderr_file):
"""Create loggers. Open stdout and stderr logfiles."""
if stdout_file:
stdout_path = os.path.expanduser(
os.path.join(self._logdir, stdout_file))
self._stdout_fp = open(stdout_path, "a+")
self._stdout_stream = Tee(sys.stdout, self._stdout_fp)
self._stdout_context = redirect_stdout(self._stdout_stream)
self._stdout_context.__enter__()
if stderr_file:
stderr_path = os.path.expanduser(
os.path.join(self._logdir, stderr_file))
self._stderr_fp = open(stderr_path, "a+")
self._stderr_stream = Tee(sys.stderr, self._stderr_fp)
self._stderr_context = redirect_stderr(self._stderr_stream)
self._stderr_context.__enter__()
# Add logging handler to root ray logger
formatter = logging.Formatter("[%(levelname)s %(asctime)s] "
"%(filename)s: %(lineno)d "
"%(message)s")
self._stderr_logging_handler = logging.StreamHandler(
self._stderr_fp)
self._stderr_logging_handler.setFormatter(formatter)
ray.logger.addHandler(self._stderr_logging_handler)
def _close_logfiles(self):
"""Close stdout and stderr logfiles."""
if self._stderr_logging_handler:
ray.logger.removeHandler(self._stderr_logging_handler)
if self._stdout_context:
self._stdout_stream.flush()
self._stdout_context.__exit__(None, None, None)
self._stdout_fp.close()
self._stdout_context = None
if self._stderr_context:
self._stderr_stream.flush()
self._stderr_context.__exit__(None, None, None)
self._stderr_fp.close()
self._stderr_context = None
def stop(self):
"""Releases all resources used by this trainable.
Calls ``Trainable.cleanup`` internally. Subclasses should override
``Trainable.cleanup`` for custom cleanup procedures.
"""
self._result_logger.flush()
self._result_logger.close()
if self._monitor.is_alive():
self._monitor.stop()
self._monitor.join()
self.cleanup()
self._close_logfiles()
@property
def logdir(self):
"""Directory of the results and checkpoints for this Trainable.
Tune will automatically sync this folder with the driver if execution
is distributed.
Note that the current working directory will also be changed to this.
"""
return os.path.join(self._logdir, "")
@property
def trial_name(self):
"""Trial name for the corresponding trial of this Trainable.
This is not set if not using Tune.
.. code-block:: python
name = self.trial_name
"""
if self._trial_info:
return self._trial_info.trial_name
else:
return "default"
@property
def trial_id(self):
"""Trial ID for the corresponding trial of this Trainable.
This is not set if not using Tune.
.. code-block:: python
trial_id = self.trial_id
"""
if self._trial_info:
return self._trial_info.trial_id
else:
return "default"
@property
def trial_resources(self) -> Union[Resources, PlacementGroupFactory]:
"""Resources currently assigned to the trial of this Trainable.
This is not set if not using Tune.
.. code-block:: python
trial_resources = self.trial_resources
"""
if self._trial_info:
return self._trial_info.trial_resources
else:
return "default"
@property
def iteration(self):
"""Current training iteration.
This value is automatically incremented every time `train()` is called
and is automatically inserted into the training result dict.
"""
return self._iteration
@property
def training_iteration(self):
"""Current training iteration (same as `self.iteration`).
This value is automatically incremented every time `train()` is called
and is automatically inserted into the training result dict.
"""
return self._iteration
def get_config(self):
"""Returns configuration passed in by Tune."""
return self.config
def step(self):
"""Subclasses should override this to implement train().
The return value will be automatically passed to the loggers. Users
can also return `tune.result.DONE` or `tune.result.SHOULD_CHECKPOINT`
as a key to manually trigger termination or checkpointing of this
trial. Note that manual checkpointing only works when subclassing
Trainables.
.. versionadded:: 0.8.7
Returns:
A dict that describes training progress.
"""
if self._implements_method("_train") and log_once("_train"):
raise DeprecationWarning(
"Trainable._train is deprecated and is now removed. Override "
"Trainable.step instead.")
raise NotImplementedError
def save_checkpoint(self, tmp_checkpoint_dir):
"""Subclasses should override this to implement ``save()``.
Warning:
Do not rely on absolute paths in the implementation of
``Trainable.save_checkpoint`` and ``Trainable.load_checkpoint``.
Use ``validate_save_restore`` to catch ``Trainable.save_checkpoint``/
``Trainable.load_checkpoint`` errors before execution.
>>> from ray.tune.utils import validate_save_restore
>>> validate_save_restore(MyTrainableClass)
>>> validate_save_restore(MyTrainableClass, use_object_store=True)
.. versionadded:: 0.8.7
Args:
tmp_checkpoint_dir (str): The directory where the checkpoint
file must be stored. In a Tune run, if the trial is paused,
the provided path may be temporary and moved.
Returns:
A dict or string. If string, the return value is expected to be
prefixed by `tmp_checkpoint_dir`. If dict, the return value will
be automatically serialized by Tune and
passed to ``Trainable.load_checkpoint()``.
Examples:
>>> print(trainable1.save_checkpoint("/tmp/checkpoint_1"))
"/tmp/checkpoint_1/my_checkpoint_file"
>>> print(trainable2.save_checkpoint("/tmp/checkpoint_2"))
{"some": "data"}
>>> trainable.save_checkpoint("/tmp/bad_example")
"/tmp/NEW_CHECKPOINT_PATH/my_checkpoint_file" # This will error.
"""
if self._implements_method("_save") and log_once("_save"):
raise DeprecationWarning(
"Trainable._save is deprecated and is now removed. Override "
"Trainable.save_checkpoint instead.")
raise NotImplementedError
def load_checkpoint(self, checkpoint):
"""Subclasses should override this to implement restore().
Warning:
In this method, do not rely on absolute paths. The absolute
path of the checkpoint_dir used in ``Trainable.save_checkpoint``
may be changed.
If ``Trainable.save_checkpoint`` returned a prefixed string, the
prefix of the checkpoint string returned by
``Trainable.save_checkpoint`` may be changed.
This is because trial pausing depends on temporary directories.
The directory structure under the checkpoint_dir provided to
``Trainable.save_checkpoint`` is preserved.
See the example below.
.. code-block:: python
class Example(Trainable):
def save_checkpoint(self, checkpoint_path):
print(checkpoint_path)
return os.path.join(checkpoint_path, "my/check/point")
def load_checkpoint(self, checkpoint):
print(checkpoint)
>>> trainer = Example()
>>> obj = trainer.save_to_object() # This is used when PAUSED.
<logdir>/tmpc8k_c_6hsave_to_object/checkpoint_0/my/check/point
>>> trainer.restore_from_object(obj) # Note the different prefix.
<logdir>/tmpb87b5axfrestore_from_object/checkpoint_0/my/check/point
.. versionadded:: 0.8.7
Args:
checkpoint (str|dict): If dict, the return value is as
returned by `save_checkpoint`. If a string, then it is
a checkpoint path that may have a different prefix than that
returned by `save_checkpoint`. The directory structure
underneath the `checkpoint_dir` `save_checkpoint` is preserved.
"""
if self._implements_method("_restore") and log_once("_restore"):
raise DeprecationWarning(
"Trainable._restore is deprecated and is now removed. "
"Override Trainable.load_checkpoint instead.")
raise NotImplementedError
def setup(self, config):
"""Subclasses should override this for custom initialization.
.. versionadded:: 0.8.7
Args:
config (dict): Hyperparameters and other configs given.
Copy of `self.config`.
"""
if self._implements_method("_setup") and log_once("_setup"):
raise DeprecationWarning(
"Trainable._setup is deprecated and is now removed. Override "
"Trainable.setup instead.")
pass
def log_result(self, result):
"""Subclasses can optionally override this to customize logging.
The logging here is done on the worker process rather than
the driver. You may want to turn off driver logging via the
``loggers`` parameter in ``tune.run`` when overriding this function.
.. versionadded:: 0.8.7
Args:
result (dict): Training result returned by step().
"""
if self._implements_method("_log_result") and log_once("_log_result"):
raise DeprecationWarning(
"Trainable._log_result is deprecated and is now removed. "
"Override Trainable.log_result instead.")
self._result_logger.on_result(result)
def cleanup(self):
"""Subclasses should override this for any cleanup on stop.
If any Ray actors are launched in the Trainable (i.e., with a RLlib
trainer), be sure to kill the Ray actor process here.
You can kill a Ray actor by calling `actor.__ray_terminate__.remote()`
on the actor.
.. versionadded:: 0.8.7
"""
if self._implements_method("_stop") and log_once("_stop"):
raise DeprecationWarning(
"Trainable._stop is deprecated and is now removed. Override "
"Trainable.cleanup instead.")
pass
def _export_model(self, export_formats, export_dir):
"""Subclasses should override this to export model.
Args:
export_formats (list): List of formats that should be exported.
export_dir (str): Directory to place exported models.
Return:
A dict that maps ExportFormats to successfully exported models.
"""
return {}
def _implements_method(self, key):
return hasattr(self, key) and callable(getattr(self, key))
class Trial(object):
"""A trial object holds the state for one model training run.
Trials are themselves managed by the TrialRunner class, which implements
the event loop for submitting trial runs to a Ray cluster.
Trials start in the PENDING state, and transition to RUNNING once started.
On error it transitions to ERROR, otherwise TERMINATED on success.
"""
PENDING = "PENDING"
RUNNING = "RUNNING"
PAUSED = "PAUSED"
TERMINATED = "TERMINATED"
ERROR = "ERROR"
def __init__(self,
trainable_name,
config=None,
trial_id=None,
local_dir=DEFAULT_RESULTS_DIR,
evaluated_params=None,
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,
trial_name_creator=None,
loggers=None,
sync_to_driver_fn=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.
"""
validate_trainable(trainable_name)
# Trial config
self.trainable_name = trainable_name
self.trial_id = Trial.generate_id() if trial_id is None else trial_id
self.config = config or {}
self.local_dir = local_dir # This remains unexpanded for syncing.
#: Parameters that Tune varies across searches.
self.evaluated_params = evaluated_params or {}
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.loggers = loggers
self.sync_to_driver_fn = sync_to_driver_fn
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.error_file = None
self.error_msg = 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_to_driver_fn",
"results",
"best_result",
"param_config",
"extra_arg",
]
if trial_name_creator:
self.custom_trial_name = trial_name_creator(self)
@classmethod
def generate_id(cls):
return str(uuid.uuid1().hex)[:8]
@classmethod
def create_logdir(cls, identifier, local_dir):
local_dir = os.path.expanduser(local_dir)
if not os.path.exists(local_dir):
os.makedirs(local_dir)
return tempfile.mkdtemp(prefix="{}_{}".format(
identifier[:MAX_LEN_IDENTIFIER], date_str()),
dir=local_dir)
def init_logger(self):
"""Init logger."""
if not self.result_logger:
if not self.logdir:
self.logdir = Trial.create_logdir(str(self), self.local_dir)
elif not os.path.exists(self.logdir):
os.makedirs(self.logdir)
self.result_logger = UnifiedLogger(
self.config,
self.logdir,
trial=self,
loggers=self.loggers,
sync_function=self.sync_to_driver_fn)
def update_resources(self, cpu, gpu, **kwargs):
"""EXPERIMENTAL: Updates the resource requirements.
Should only be called when the trial is not running.
Raises:
ValueError if trial status is running.
"""
if self.status is Trial.RUNNING:
raise ValueError("Cannot update resources while Trial is running.")
self.resources = Resources(cpu, gpu, **kwargs)
def sync_logger_to_new_location(self, worker_ip):
"""Updates the logger location.
Also pushes logdir to worker_ip, allowing for cross-node recovery.
"""
if self.result_logger:
self.result_logger.sync_results_to_new_location(worker_ip)
def close_logger(self):
"""Close logger."""
if self.result_logger:
self.result_logger.close()
self.result_logger = None
def write_error_log(self, error_msg):
if error_msg and self.logdir:
self.num_failures += 1 # may be moved to outer scope?
error_file = os.path.join(self.logdir,
"error_{}.txt".format(date_str()))
with open(error_file, "w") as f:
f.write(error_msg)
self.error_file = error_file
self.error_msg = error_msg
def should_stop(self, result):
"""Whether the given result meets this trial's stopping criteria."""
if result.get(DONE):
return True
if callable(self.stopping_criterion):
return self.stopping_criterion(self.trial_id, result)
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 should_checkpoint(self):
"""Whether this trial is due for checkpointing."""
result = self.last_result or {}
if result.get(DONE) and self.checkpoint_at_end:
return True
if self.checkpoint_freq:
return result.get(TRAINING_ITERATION,
0) % self.checkpoint_freq == 0
else:
return False
def has_checkpoint(self):
return self._checkpoint.value is not None
def clear_checkpoint(self):
self._checkpoint.value = None
def should_recover(self):
"""Returns whether the trial qualifies for retrying.
This is if the trial has not failed more than max_failures. Note this
may return true even when there is no checkpoint, either because
`self.checkpoint_freq` is `0` or because the trial failed before
a checkpoint has been made.
"""
return self.num_failures < self.max_failures or self.max_failures < 0
def update_last_result(self, result, terminate=False):
result.update(trial_id=self.trial_id, done=terminate)
if self.experiment_tag:
result.update(experiment_tag=self.experiment_tag)
if self.verbose and (terminate or time.time() - self.last_debug >
DEBUG_PRINT_INTERVAL):
print("Result for {}:".format(self))
print(" {}".format(pretty_print(result).replace("\n", "\n ")))
self.last_debug = time.time()
self.last_result = result
self.last_update_time = time.time()
self.result_logger.on_result(self.last_result)
def compare_checkpoints(self, attr_mean):
"""Compares two checkpoints based on the attribute attr_mean param.
Greater than is used by default. If command-line parameter
checkpoint_score_attr starts with "min-" less than is used.
Arguments:
attr_mean: mean of attribute value for the current checkpoint
Returns:
True: when attr_mean is greater than previous checkpoint attr_mean
and greater than function is selected
when attr_mean is less than previous checkpoint attr_mean and
less than function is selected
False: when attr_mean is not in alignment with selected cmp fn
"""
if self._cmp_greater and attr_mean > self.best_checkpoint_attr_value:
return True
elif (not self._cmp_greater
and attr_mean < self.best_checkpoint_attr_value):
return True
return False
def get_trainable_cls(self):
return get_trainable_cls(self.trainable_name)
def set_verbose(self, verbose):
self.verbose = verbose
def is_finished(self):
return self.status in [Trial.TERMINATED, Trial.ERROR]
@property
def node_ip(self):
return self.last_result.get("node_ip")
def __repr__(self):
return str(self)
def __str__(self):
"""Combines ``env`` with ``trainable_name`` and ``trial_id``.
Can be overriden with a custom string creator.
"""
if self.custom_trial_name:
return self.custom_trial_name
if "env" in self.config:
env = self.config["env"]
if isinstance(env, type):
env = env.__name__
identifier = "{}_{}".format(self.trainable_name, env)
else:
identifier = self.trainable_name
identifier += "_" + self.trial_id
return identifier.replace("/", "_")
def __getstate__(self):
"""Memento generator for Trial.
Sets RUNNING trials to PENDING, and flushes the result logger.
Note this can only occur if the trial holds a DISK checkpoint.
"""
assert self._checkpoint.storage == Checkpoint.DISK, (
"Checkpoint must not be in-memory.")
state = self.__dict__.copy()
state["resources"] = resources_to_json(self.resources)
for key in self._nonjson_fields:
state[key] = binary_to_hex(cloudpickle.dumps(state.get(key)))
state["runner"] = None
state["result_logger"] = None
if self.result_logger:
self.result_logger.flush()
state["__logger_started__"] = True
else:
state["__logger_started__"] = False
return copy.deepcopy(state)
def __setstate__(self, state):
logger_started = state.pop("__logger_started__")
state["resources"] = json_to_resources(state["resources"])
if state["status"] == Trial.RUNNING:
state["status"] = Trial.PENDING
for key in self._nonjson_fields:
state[key] = cloudpickle.loads(hex_to_binary(state[key]))
self.__dict__.update(state)
validate_trainable(self.trainable_name)
if logger_started:
self.init_logger()
class Trial(object):
"""A trial object holds the state for one model training run.
Trials are themselves managed by the TrialRunner class, which implements
the event loop for submitting trial runs to a Ray cluster.
Trials start in the PENDING state, and transition to RUNNING once started.
On error it transitions to ERROR, otherwise TERMINATED on success.
"""
PENDING = "PENDING"
RUNNING = "RUNNING"
PAUSED = "PAUSED"
TERMINATED = "TERMINATED"
ERROR = "ERROR"
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)
@classmethod
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)
@classmethod
def generate_id(cls):
return binary_to_hex(_random_string())[:8]
def init_logger(self):
"""Init logger."""
if not self.result_logger:
if not os.path.exists(self.local_dir):
os.makedirs(self.local_dir)
if not self.logdir:
self.logdir = tempfile.mkdtemp(prefix="{}_{}".format(
str(self)[:MAX_LEN_IDENTIFIER], date_str()),
dir=self.local_dir)
elif not os.path.exists(self.logdir):
os.makedirs(self.logdir)
self.result_logger = UnifiedLogger(
self.config,
self.logdir,
upload_uri=self.upload_dir,
loggers=self.loggers,
sync_function=self.sync_function)
def update_resources(self, cpu, gpu, **kwargs):
"""EXPERIMENTAL: Updates the resource requirements.
Should only be called when the trial is not running.
Raises:
ValueError if trial status is running.
"""
if self.status is Trial.RUNNING:
raise ValueError("Cannot update resources while Trial is running.")
self.resources = Resources(cpu, gpu, **kwargs)
def sync_logger_to_new_location(self, worker_ip):
"""Updates the logger location.
Also pushes logdir to worker_ip, allowing for cross-node recovery.
"""
if self.result_logger:
self.result_logger.sync_results_to_new_location(worker_ip)
def close_logger(self):
"""Close logger."""
if self.result_logger:
self.result_logger.close()
self.result_logger = None
def write_error_log(self, error_msg):
if error_msg and self.logdir:
self.num_failures += 1 # may be moved to outer scope?
error_file = os.path.join(self.logdir,
"error_{}.txt".format(date_str()))
with open(error_file, "w") as f:
f.write(error_msg)
self.error_file = error_file
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 should_checkpoint(self):
"""Whether this trial is due for checkpointing."""
result = self.last_result or {}
if result.get(DONE) and self.checkpoint_at_end:
return True
if self.checkpoint_freq:
return result.get(TRAINING_ITERATION,
0) % self.checkpoint_freq == 0
else:
return False
def progress_string(self):
"""Returns a progress message for printing out to the console."""
if not self.last_result:
return self._status_string()
def location_string(hostname, pid):
if hostname == os.uname()[1]:
return 'pid={}'.format(pid)
else:
return '{} pid={}'.format(hostname, pid)
pieces = [
'{}'.format(self._status_string()),
'[{}]'.format(self.resources.summary_string()), '[{}]'.format(
location_string(self.last_result.get(HOSTNAME),
self.last_result.get(PID))),
'{} s'.format(int(self.last_result.get(TIME_TOTAL_S)))
]
if self.last_result.get(TRAINING_ITERATION) is not None:
pieces.append('{} iter'.format(
self.last_result[TRAINING_ITERATION]))
if self.last_result.get(TIMESTEPS_TOTAL) is not None:
pieces.append('{} ts'.format(self.last_result[TIMESTEPS_TOTAL]))
if self.last_result.get(EPISODE_REWARD_MEAN) is not None:
pieces.append('{} rew'.format(
format(self.last_result[EPISODE_REWARD_MEAN], '.3g')))
if self.last_result.get(MEAN_LOSS) is not None:
pieces.append('{} loss'.format(
format(self.last_result[MEAN_LOSS], '.3g')))
if self.last_result.get(MEAN_ACCURACY) is not None:
pieces.append('{} acc'.format(
format(self.last_result[MEAN_ACCURACY], '.3g')))
return ', '.join(pieces)
def _status_string(self):
return "{}{}".format(
self.status, ", {} failures: {}".format(
self.num_failures, self.error_file) if self.error_file else "")
def has_checkpoint(self):
return self._checkpoint.value is not None
def clear_checkpoint(self):
self._checkpoint.value = None
def should_recover(self):
"""Returns whether the trial qualifies for restoring.
This is if a checkpoint frequency is set and has not failed more than
max_failures. This may return true even when there may not yet
be a checkpoint.
"""
return (self.checkpoint_freq > 0
and (self.num_failures < self.max_failures
or self.max_failures < 0))
def update_last_result(self, result, terminate=False):
if terminate:
result.update(done=True)
if self.verbose and (terminate or time.time() - self.last_debug >
DEBUG_PRINT_INTERVAL):
print("Result for {}:".format(self))
print(" {}".format(pretty_print(result).replace("\n", "\n ")))
self.last_debug = time.time()
self.last_result = result
self.last_update_time = time.time()
self.result_logger.on_result(self.last_result)
def compare_checkpoints(self, attr_mean):
"""Compares two checkpoints based on the attribute attr_mean param.
Greater than is used by default. If command-line parameter
checkpoint_score_attr starts with "min-" less than is used.
Arguments:
attr_mean: mean of attribute value for the current checkpoint
Returns:
True: when attr_mean is greater than previous checkpoint attr_mean
and greater than function is selected
when attr_mean is less than previous checkpoint attr_mean and
less than function is selected
False: when attr_mean is not in alignment with selected cmp fn
"""
if self._cmp_greater and attr_mean > self.best_checkpoint_attr_value:
return True
elif (not self._cmp_greater
and attr_mean < self.best_checkpoint_attr_value):
return True
return False
def _get_trainable_cls(self):
return ray.tune.registry._global_registry.get(
ray.tune.registry.TRAINABLE_CLASS, self.trainable_name)
def set_verbose(self, verbose):
self.verbose = verbose
def is_finished(self):
return self.status in [Trial.TERMINATED, Trial.ERROR]
def __repr__(self):
return str(self)
def __str__(self):
"""Combines ``env`` with ``trainable_name`` and ``experiment_tag``.
Can be overriden with a custom string creator.
"""
if self.custom_trial_name:
return self.custom_trial_name
if "env" in self.config:
env = self.config["env"]
if isinstance(env, type):
env = env.__name__
identifier = "{}_{}".format(self.trainable_name, env)
else:
identifier = self.trainable_name
if self.experiment_tag:
identifier += "_" + self.experiment_tag
return identifier.replace("/", "_")
def __getstate__(self):
"""Memento generator for Trial.
Sets RUNNING trials to PENDING, and flushes the result logger.
Note this can only occur if the trial holds a DISK checkpoint.
"""
assert self._checkpoint.storage == Checkpoint.DISK, (
"Checkpoint must not be in-memory.")
state = self.__dict__.copy()
state["resources"] = resources_to_json(self.resources)
for key in self._nonjson_fields:
state[key] = binary_to_hex(cloudpickle.dumps(state.get(key)))
state["runner"] = None
state["result_logger"] = None
if self.result_logger:
self.result_logger.flush()
state["__logger_started__"] = True
else:
state["__logger_started__"] = False
return copy.deepcopy(state)
def __setstate__(self, state):
logger_started = state.pop("__logger_started__")
state["resources"] = json_to_resources(state["resources"])
if state["status"] == Trial.RUNNING:
state["status"] = Trial.PENDING
for key in self._nonjson_fields:
state[key] = cloudpickle.loads(hex_to_binary(state[key]))
self.__dict__.update(state)
Trial._registration_check(self.trainable_name)
if logger_started:
self.init_logger()
class TrackSession:
"""Manages results for a single session.
Represents a single Trial in an experiment. This is automatically
created when using ``tune.run``.
Attributes:
trial_name (str): Custom trial name.
experiment_dir (str): Directory where results for all trials
are stored. Each session is stored into a unique directory
inside experiment_dir.
upload_dir (str): Directory to sync results to.
trial_config (dict): Parameters that will be logged to disk.
_tune_reporter (StatusReporter): For rerouting when using Tune.
Will not instantiate logging if not None.
"""
def __init__(self,
trial_name=None,
experiment_dir=None,
upload_dir=None,
trial_config=None,
_tune_reporter=None):
self._experiment_dir = None
self._logdir = None
self._upload_dir = None
self.trial_config = None
self._iteration = -1
self.is_tune_session = bool(_tune_reporter)
if self.is_tune_session:
self._logger = _ReporterHook(_tune_reporter)
self._logdir = _tune_reporter.logdir
self._trial_name = _tune_reporter.trial_name
self._trial_id = _tune_reporter.trial_id
else:
self._trial_id = Trial.generate_id()
self._trial_name = trial_name or self._trial_id
self._initialize_logging(experiment_dir, upload_dir, trial_config)
def _initialize_logging(self,
experiment_dir=None,
upload_dir=None,
trial_config=None):
if upload_dir:
raise NotImplementedError("Upload Dir is not yet implemented.")
# TODO(rliaw): In other parts of the code, this is `local_dir`.
if experiment_dir is None:
experiment_dir = os.path.join(DEFAULT_RESULTS_DIR, "default")
self._experiment_dir = os.path.expanduser(experiment_dir)
# TODO(rliaw): Refactor `logdir` to `trial_dir`.
self._logdir = Trial.create_logdir(self.trial_name,
self._experiment_dir)
self._upload_dir = upload_dir
self.trial_config = trial_config or {}
# misc metadata to save as well
self.trial_config["trial_id"] = self.trial_id
self._logger = UnifiedLogger(self.trial_config, self._logdir)
def log(self, **metrics):
"""Logs all named arguments specified in `metrics`.
This will log trial metrics locally, and they will be synchronized
with the driver periodically through ray.
Arguments:
metrics: named arguments with corresponding values to log.
"""
self._iteration += 1
# TODO: Implement a batching mechanism for multiple calls to `log`
# within the same iteration.
metrics_dict = metrics.copy()
metrics_dict.update({"trial_id": self.trial_id})
# TODO: Move Trainable autopopulation to a util function
metrics_dict.setdefault(TRAINING_ITERATION, self._iteration)
self._logger.on_result(metrics_dict)
def close(self):
"""Closes loggers.
No need to call this when using ``tune.run``.
"""
self.trial_config["trial_completed"] = True
self.trial_config["end_time"] = datetime.now().isoformat()
# TODO(rliaw): Have Tune support updated configs
self._logger.update_config(self.trial_config)
self._logger.flush()
self._logger.close()
@property
def logdir(self):
"""Trial logdir (subdir of given experiment directory)"""
return self._logdir
@property
def trial_name(self):
"""Trial name for the corresponding trial of this Trainable"""
return self._trial_name
@property
def trial_id(self):
"""Trial id for the corresponding trial of this Trainable"""
return self._trial_id
class Trial(object):
"""A trial object holds the state for one model training run.
Trials are themselves managed by the TrialRunner class, which implements
the event loop for submitting trial runs to a Ray cluster.
Trials start in the PENDING state, and transition to RUNNING once started.
On error it transitions to ERROR, otherwise TERMINATED on success.
"""
PENDING = "PENDING"
RUNNING = "RUNNING"
PAUSED = "PAUSED"
TERMINATED = "TERMINATED"
ERROR = "ERROR"
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)
@classmethod
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)
@classmethod
def generate_id(cls):
return binary_to_hex(random_string())[:8]
def init_logger(self):
"""Init logger."""
if not self.result_logger:
if not os.path.exists(self.local_dir):
os.makedirs(self.local_dir)
if not self.logdir:
self.logdir = tempfile.mkdtemp(prefix="{}_{}".format(
str(self)[:MAX_LEN_IDENTIFIER], date_str()),
dir=self.local_dir)
elif not os.path.exists(self.logdir):
os.makedirs(self.logdir)
self.result_logger = UnifiedLogger(
self.config,
self.logdir,
upload_uri=self.upload_dir,
custom_loggers=self.custom_loggers,
sync_function=self.sync_function)
def close_logger(self):
"""Close logger."""
if self.result_logger:
self.result_logger.close()
self.result_logger = None
def write_error_log(self, error_msg):
if error_msg and self.logdir:
self.num_failures += 1 # may be moved to outer scope?
error_file = os.path.join(self.logdir,
"error_{}.txt".format(date_str()))
with open(error_file, "w") as f:
f.write(error_msg)
self.error_file = error_file
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 should_checkpoint(self):
"""Whether this trial is due for checkpointing."""
result = self.last_result or {}
if result.get(DONE) and self.checkpoint_at_end:
return True
if self.checkpoint_freq:
return result.get(TRAINING_ITERATION,
0) % self.checkpoint_freq == 0
else:
return False
def progress_string(self):
"""Returns a progress message for printing out to the console."""
if self.last_result is None:
return self._status_string()
def location_string(hostname, pid):
if hostname == os.uname()[1]:
return 'pid={}'.format(pid)
else:
return '{} pid={}'.format(hostname, pid)
pieces = [
'{} [{}]'.format(
self._status_string(),
location_string(self.last_result.get(HOSTNAME),
self.last_result.get(PID))),
'{} s'.format(int(self.last_result.get(TIME_TOTAL_S)))
]
if self.last_result.get(TRAINING_ITERATION) is not None:
pieces.append('{} iter'.format(
self.last_result[TRAINING_ITERATION]))
if self.last_result.get(TIMESTEPS_TOTAL) is not None:
pieces.append('{} ts'.format(self.last_result[TIMESTEPS_TOTAL]))
if self.last_result.get("episode_reward_mean") is not None:
pieces.append('{} rew'.format(
format(self.last_result["episode_reward_mean"], '.3g')))
if self.last_result.get("mean_loss") is not None:
pieces.append('{} loss'.format(
format(self.last_result["mean_loss"], '.3g')))
if self.last_result.get("mean_accuracy") is not None:
pieces.append('{} acc'.format(
format(self.last_result["mean_accuracy"], '.3g')))
return ', '.join(pieces)
def _status_string(self):
return "{}{}".format(
self.status, ", {} failures: {}".format(
self.num_failures, self.error_file) if self.error_file else "")
def has_checkpoint(self):
return self._checkpoint.value is not None
def should_recover(self):
"""Returns whether the trial qualifies for restoring.
This is if a checkpoint frequency is set and has not failed more than
max_failures. This may return true even when there may not yet
be a checkpoint.
"""
return (self.checkpoint_freq > 0
and self.num_failures < self.max_failures)
def update_last_result(self, result, terminate=False):
if terminate:
result.update(done=True)
if self.verbose and (terminate or time.time() - self.last_debug >
DEBUG_PRINT_INTERVAL):
logger.info("Result for {}:".format(self))
logger.info(" {}".format(
pretty_print(result).replace("\n", "\n ")))
self.last_debug = time.time()
self.last_result = result
self.last_update_time = time.time()
self.result_logger.on_result(self.last_result)
def _get_trainable_cls(self):
return ray.tune.registry._global_registry.get(
ray.tune.registry.TRAINABLE_CLASS, self.trainable_name)
def set_verbose(self, verbose):
self.verbose = verbose
def is_finished(self):
return self.status in [Trial.TERMINATED, Trial.ERROR]
def __repr__(self):
return str(self)
def __str__(self):
"""Combines ``env`` with ``trainable_name`` and ``experiment_tag``.
Can be overriden with a custom string creator.
"""
if self.trial_name:
return self.trial_name
if "env" in self.config:
env = self.config["env"]
if isinstance(env, type):
env = env.__name__
identifier = "{}_{}".format(self.trainable_name, env)
else:
identifier = self.trainable_name
if self.experiment_tag:
identifier += "_" + self.experiment_tag
return identifier.replace("/", "_")
def __getstate__(self):
"""Memento generator for Trial.
Sets RUNNING trials to PENDING, and flushes the result logger.
Note this can only occur if the trial holds a DISK checkpoint.
"""
assert self._checkpoint.storage == Checkpoint.DISK, (
"Checkpoint must not be in-memory.")
state = self.__dict__.copy()
state["resources"] = resources_to_json(self.resources)
pickle_data = {
"_checkpoint": self._checkpoint,
"config": self.config,
"custom_loggers": self.custom_loggers,
"sync_function": self.sync_function
}
for key, value in pickle_data.items():
state[key] = binary_to_hex(cloudpickle.dumps(value))
state["runner"] = None
state["result_logger"] = None
if self.status == Trial.RUNNING:
state["status"] = Trial.PENDING
if self.result_logger:
self.result_logger.flush()
state["__logger_started__"] = True
else:
state["__logger_started__"] = False
return copy.deepcopy(state)
def __setstate__(self, state):
logger_started = state.pop("__logger_started__")
state["resources"] = json_to_resources(state["resources"])
for key in [
"_checkpoint", "config", "custom_loggers", "sync_function"
]:
state[key] = cloudpickle.loads(hex_to_binary(state[key]))
self.__dict__.update(state)
Trial._registration_check(self.trainable_name)
if logger_started:
self.init_logger()
class Trainable(object):
"""Abstract class for trainable models, functions, etc.
A call to ``train()`` on a trainable will execute one logical iteration of
training. As a rule of thumb, the execution time of one train call should
be large enough to avoid overheads (i.e. more than a few seconds), but
short enough to report progress periodically (i.e. at most a few minutes).
Calling ``save()`` should save the training state of a trainable to disk,
and ``restore(path)`` should restore a trainable to the given state.
Generally you only need to implement ``_setup``, ``_train``,
``_save``, and ``_restore`` when subclassing Trainable.
Other implementation methods that may be helpful to override are
``_log_result``, ``reset_config``, ``_stop``, and ``_export_model``.
When using Tune, Tune will convert this class into a Ray actor, which
runs on a separate process. Tune will also change the current working
directory of this process to `self.logdir`.
"""
def __init__(self, config=None, logger_creator=None):
"""Initialize an Trainable.
Sets up logging and points ``self.logdir`` to a directory in which
training outputs should be placed.
Subclasses should prefer defining ``_setup()`` instead of overriding
``__init__()`` directly.
Args:
config (dict): Trainable-specific configuration data. By default
will be saved as ``self.config``.
logger_creator (func): Function that creates a ray.tune.Logger
object. If unspecified, a default logger is created.
"""
self._experiment_id = uuid.uuid4().hex
self.config = config or {}
if logger_creator:
self._result_logger = logger_creator(self.config)
self._logdir = self._result_logger.logdir
else:
logdir_prefix = datetime.today().strftime("%Y-%m-%d_%H-%M-%S")
if not os.path.exists(DEFAULT_RESULTS_DIR):
os.makedirs(DEFAULT_RESULTS_DIR)
self._logdir = tempfile.mkdtemp(prefix=logdir_prefix,
dir=DEFAULT_RESULTS_DIR)
self._result_logger = UnifiedLogger(self.config,
self._logdir,
loggers=None)
self._iteration = 0
self._time_total = 0.0
self._timesteps_total = None
self._episodes_total = None
self._time_since_restore = 0.0
self._timesteps_since_restore = 0
self._iterations_since_restore = 0
self._restored = False
start_time = time.time()
self._setup(copy.deepcopy(self.config))
setup_time = time.time() - start_time
if setup_time > SETUP_TIME_THRESHOLD:
logger.info("_setup took {:.3f} seconds. If your trainable is "
"slow to initialize, consider setting "
"reuse_actors=True to reduce actor creation "
"overheads.".format(setup_time))
self._local_ip = ray.services.get_node_ip_address()
log_sys_usage = self.config.get("log_sys_usage", False)
self._monitor = UtilMonitor(start=log_sys_usage)
@classmethod
def default_resource_request(cls, config):
"""Returns the resource requirement for the given configuration.
This can be overriden by sub-classes to set the correct trial resource
allocation, so the user does not need to.
Example:
>>> def default_resource_request(cls, config):
>>> return Resources(
>>> cpu=0,
>>> gpu=0,
>>> extra_cpu=config["workers"],
>>> extra_gpu=int(config["use_gpu"]) * config["workers"])
"""
return None
@classmethod
def resource_help(cls, config):
"""Returns a help string for configuring this trainable's resources."""
return ""
def current_ip(self):
logger.warning("Getting current IP.")
self._local_ip = ray.services.get_node_ip_address()
return self._local_ip
def train(self):
"""Runs one logical iteration of training.
Subclasses should override ``_train()`` instead to return results.
This class automatically fills the following fields in the result:
`done` (bool): training is terminated. Filled only if not provided.
`time_this_iter_s` (float): Time in seconds this iteration
took to run. This may be overriden in order to override the
system-computed time difference.
`time_total_s` (float): Accumulated time in seconds for this
entire experiment.
`experiment_id` (str): Unique string identifier
for this experiment. This id is preserved
across checkpoint / restore calls.
`training_iteration` (int): The index of this
training iteration, e.g. call to train(). This is incremented
after `_train()` is called.
`pid` (str): The pid of the training process.
`date` (str): A formatted date of when the result was processed.
`timestamp` (str): A UNIX timestamp of when the result
was processed.
`hostname` (str): Hostname of the machine hosting the training
process.
`node_ip` (str): Node ip of the machine hosting the training
process.
Returns:
A dict that describes training progress.
"""
start = time.time()
result = self._train()
assert isinstance(result, dict), "_train() needs to return a dict."
# We do not modify internal state nor update this result if duplicate.
if RESULT_DUPLICATE in result:
return result
result = result.copy()
self._iteration += 1
self._iterations_since_restore += 1
if result.get(TIME_THIS_ITER_S) is not None:
time_this_iter = result[TIME_THIS_ITER_S]
else:
time_this_iter = time.time() - start
self._time_total += time_this_iter
self._time_since_restore += time_this_iter
result.setdefault(DONE, False)
# self._timesteps_total should only be tracked if increments provided
if result.get(TIMESTEPS_THIS_ITER) is not None:
if self._timesteps_total is None:
self._timesteps_total = 0
self._timesteps_total += result[TIMESTEPS_THIS_ITER]
self._timesteps_since_restore += result[TIMESTEPS_THIS_ITER]
# self._episodes_total should only be tracked if increments provided
if result.get(EPISODES_THIS_ITER) is not None:
if self._episodes_total is None:
self._episodes_total = 0
self._episodes_total += result[EPISODES_THIS_ITER]
# self._timesteps_total should not override user-provided total
result.setdefault(TIMESTEPS_TOTAL, self._timesteps_total)
result.setdefault(EPISODES_TOTAL, self._episodes_total)
result.setdefault(TRAINING_ITERATION, self._iteration)
# Provides auto-filled neg_mean_loss for avoiding regressions
if result.get("mean_loss"):
result.setdefault("neg_mean_loss", -result["mean_loss"])
now = datetime.today()
result.update(experiment_id=self._experiment_id,
date=now.strftime("%Y-%m-%d_%H-%M-%S"),
timestamp=int(time.mktime(now.timetuple())),
time_this_iter_s=time_this_iter,
time_total_s=self._time_total,
pid=os.getpid(),
hostname=os.uname()[1],
node_ip=self._local_ip,
config=self.config,
time_since_restore=self._time_since_restore,
timesteps_since_restore=self._timesteps_since_restore,
iterations_since_restore=self._iterations_since_restore)
monitor_data = self._monitor.get_data()
if monitor_data:
result.update(monitor_data)
self._log_result(result)
return result
def save(self, checkpoint_dir=None):
"""Saves the current model state to a checkpoint.
Subclasses should override ``_save()`` instead to save state.
This method dumps additional metadata alongside the saved path.
Args:
checkpoint_dir (str): Optional dir to place the checkpoint.
Returns:
Checkpoint path or prefix that may be passed to restore().
"""
checkpoint_dir = os.path.join(checkpoint_dir or self.logdir,
"checkpoint_{}".format(self._iteration))
if not os.path.exists(checkpoint_dir):
os.makedirs(checkpoint_dir)
checkpoint = self._save(checkpoint_dir)
saved_as_dict = False
if isinstance(checkpoint, string_types):
if not checkpoint.startswith(checkpoint_dir):
raise ValueError(
"The returned checkpoint path must be within the "
"given checkpoint dir {}: {}".format(
checkpoint_dir, checkpoint))
checkpoint_path = checkpoint
elif isinstance(checkpoint, dict):
saved_as_dict = True
checkpoint_path = os.path.join(checkpoint_dir, "checkpoint")
with open(checkpoint_path, "wb") as f:
pickle.dump(checkpoint, f)
else:
raise ValueError("Returned unexpected type {}. "
"Expected str or dict.".format(type(checkpoint)))
with open(checkpoint_path + ".tune_metadata", "wb") as f:
pickle.dump(
{
"experiment_id": self._experiment_id,
"iteration": self._iteration,
"timesteps_total": self._timesteps_total,
"time_total": self._time_total,
"episodes_total": self._episodes_total,
"saved_as_dict": saved_as_dict,
"ray_version": ray.__version__,
}, f)
return checkpoint_path
def save_to_object(self):
"""Saves the current model state to a Python object.
It also saves to disk but does not return the checkpoint path.
Returns:
Object holding checkpoint data.
"""
tmpdir = tempfile.mkdtemp("save_to_object", dir=self.logdir)
checkpoint_path = self.save(tmpdir)
# Save all files in subtree.
data = {}
for basedir, _, file_names in os.walk(tmpdir):
for file_name in file_names:
path = os.path.join(basedir, file_name)
with open(path, "rb") as f:
data[os.path.relpath(path, tmpdir)] = f.read()
out = io.BytesIO()
data_dict = pickle.dumps({
"checkpoint_name":
os.path.relpath(checkpoint_path, tmpdir),
"data":
data,
})
if len(data_dict) > 10e6: # getting pretty large
logger.info("Checkpoint size is {} bytes".format(len(data_dict)))
out.write(data_dict)
shutil.rmtree(tmpdir)
return out.getvalue()
def restore(self, checkpoint_path):
"""Restores training state from a given model checkpoint.
These checkpoints are returned from calls to save().
Subclasses should override ``_restore()`` instead to restore state.
This method restores additional metadata saved with the checkpoint.
"""
with open(checkpoint_path + ".tune_metadata", "rb") as f:
metadata = pickle.load(f)
self._experiment_id = metadata["experiment_id"]
self._iteration = metadata["iteration"]
self._timesteps_total = metadata["timesteps_total"]
self._time_total = metadata["time_total"]
self._episodes_total = metadata["episodes_total"]
saved_as_dict = metadata["saved_as_dict"]
if saved_as_dict:
with open(checkpoint_path, "rb") as loaded_state:
checkpoint_dict = pickle.load(loaded_state)
checkpoint_dict.update(tune_checkpoint_path=checkpoint_path)
self._restore(checkpoint_dict)
else:
self._restore(checkpoint_path)
self._time_since_restore = 0.0
self._timesteps_since_restore = 0
self._iterations_since_restore = 0
self._restored = True
logger.info("Restored from checkpoint: %s", checkpoint_path)
state = {
"_iteration": self._iteration,
"_timesteps_total": self._timesteps_total,
"_time_total": self._time_total,
"_episodes_total": self._episodes_total,
}
logger.info("Current state after restoring: {}".format(state))
def restore_from_object(self, obj):
"""Restores training state from a checkpoint object.
These checkpoints are returned from calls to save_to_object().
"""
info = pickle.loads(obj)
data = info["data"]
tmpdir = tempfile.mkdtemp("restore_from_object", dir=self.logdir)
checkpoint_path = os.path.join(tmpdir, info["checkpoint_name"])
for relpath_name, file_contents in data.items():
path = os.path.join(tmpdir, relpath_name)
# This may be a subdirectory, hence not just using tmpdir
if not os.path.exists(os.path.dirname(path)):
os.makedirs(os.path.dirname(path))
with open(path, "wb") as f:
f.write(file_contents)
self.restore(checkpoint_path)
shutil.rmtree(tmpdir)
def export_model(self, export_formats, export_dir=None):
"""Exports model based on export_formats.
Subclasses should override _export_model() to actually
export model to local directory.
Args:
export_formats (list): List of formats that should be exported.
export_dir (str): Optional dir to place the exported model.
Defaults to self.logdir.
Returns:
A dict that maps ExportFormats to successfully exported models.
"""
export_dir = export_dir or self.logdir
return self._export_model(export_formats, export_dir)
def reset_config(self, new_config):
"""Resets configuration without restarting the trial.
This method is optional, but can be implemented to speed up algorithms
such as PBT, and to allow performance optimizations such as running
experiments with reuse_actors=True. Note that self.config need to
be updated to reflect the latest parameter information in Ray logs.
Args:
new_config (dir): Updated hyperparameter configuration
for the trainable.
Returns:
True if reset was successful else False.
"""
return False
def stop(self):
"""Releases all resources used by this trainable."""
self._result_logger.flush()
self._result_logger.close()
self._stop()
@property
def logdir(self):
"""Directory of the results and checkpoints for this Trainable.
Tune will automatically sync this folder with the driver if execution
is distributed.
Note that the current working directory will also be changed to this.
"""
return self._logdir
@property
def iteration(self):
"""Current training iteration.
This value is automatically incremented every time `train()` is called
and is automatically inserted into the training result dict.
"""
return self._iteration
def get_config(self):
"""Returns configuration passed in by Tune."""
return self.config
def _train(self):
"""Subclasses should override this to implement train().
The return value will be automatically passed to the loggers. Users
can also return `tune.result.DONE` or `tune.result.SHOULD_CHECKPOINT`
as a key to manually trigger termination or checkpointing of this
trial. Note that manual checkpointing only works when subclassing
Trainables.
Returns:
A dict that describes training progress.
"""
raise NotImplementedError
def _save(self, tmp_checkpoint_dir):
"""Subclasses should override this to implement ``save()``.
Warning:
Do not rely on absolute paths in the implementation of ``_save``
and ``_restore``.
Use ``validate_save_restore`` to catch ``_save``/``_restore`` errors
before execution.
>>> from ray.tune.util import validate_save_restore
>>> validate_save_restore(MyTrainableClass)
>>> validate_save_restore(MyTrainableClass, use_object_store=True)
Args:
tmp_checkpoint_dir (str): The directory where the checkpoint
file must be stored. In a Tune run, if the trial is paused,
the provided path may be temporary and moved.
Returns:
A dict or string. If string, the return value is expected to be
prefixed by `tmp_checkpoint_dir`. If dict, the return value will
be automatically serialized by Tune and passed to `_restore()`.
Examples:
>>> print(trainable1._save("/tmp/checkpoint_1"))
"/tmp/checkpoint_1/my_checkpoint_file"
>>> print(trainable2._save("/tmp/checkpoint_2"))
{"some": "data"}
>>> trainable._save("/tmp/bad_example")
"/tmp/NEW_CHECKPOINT_PATH/my_checkpoint_file" # This will error.
"""
raise NotImplementedError
def _restore(self, checkpoint):
"""Subclasses should override this to implement restore().
Warning:
In this method, do not rely on absolute paths. The absolute
path of the checkpoint_dir used in ``_save`` may be changed.
If ``_save`` returned a prefixed string, the prefix of the checkpoint
string returned by ``_save`` may be changed. This is because trial
pausing depends on temporary directories.
The directory structure under the checkpoint_dir provided to ``_save``
is preserved.
See the example below.
.. code-block:: python
class Example(Trainable):
def _save(self, checkpoint_path):
print(checkpoint_path)
return os.path.join(checkpoint_path, "my/check/point")
def _restore(self, checkpoint):
print(checkpoint)
>>> trainer = Example()
>>> obj = trainer.save_to_object() # This is used when PAUSED.
<logdir>/tmpc8k_c_6hsave_to_object/checkpoint_0/my/check/point
>>> trainer.restore_from_object(obj) # Note the different prefix.
<logdir>/tmpb87b5axfrestore_from_object/checkpoint_0/my/check/point
Args:
checkpoint (str|dict): If dict, the return value is as
returned by `_save`. If a string, then it is a checkpoint path
that may have a different prefix than that returned by `_save`.
The directory structure underneath the `checkpoint_dir`
`_save` is preserved.
"""
raise NotImplementedError
def _setup(self, config):
"""Subclasses should override this for custom initialization.
Args:
config (dict): Hyperparameters and other configs given.
Copy of `self.config`.
"""
pass
def _log_result(self, result):
"""Subclasses can optionally override this to customize logging.
Args:
result (dict): Training result returned by _train().
"""
self._result_logger.on_result(result)
def _stop(self):
"""Subclasses should override this for any cleanup on stop.
If any Ray actors are launched in the Trainable (i.e., with a RLlib
trainer), be sure to kill the Ray actor process here.
You can kill a Ray actor by calling `actor.__ray_terminate__.remote()`
on the actor.
"""
pass
def _export_model(self, export_formats, export_dir):
"""Subclasses should override this to export model.
Args:
export_formats (list): List of formats that should be exported.
export_dir (str): Directory to place exported models.
Return:
A dict that maps ExportFormats to successfully exported models.
"""
return {}
