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 __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, 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
self._setup(copy.deepcopy(self.config))
self._local_ip = ray.services.get_node_ip_address()
def __init__(self, config=None, registry=None, logger_creator=None):
"""Initialize an Trainable.
Subclasses should prefer defining ``_setup()`` instead of overriding
``__init__()`` directly.
Args:
config (dict): Trainable-specific configuration data.
registry (obj): Object registry for user-defined envs, models, etc.
If unspecified, the default registry will be used.
logger_creator (func): Function that creates a ray.tune.Logger
object. If unspecified, a default logger is created.
"""
if registry is None:
from ray.tune.registry import get_registry
registry = get_registry()
self._initialize_ok = False
self._experiment_id = uuid.uuid4().hex
self.config = config or {}
self.registry = registry
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, None)
self._iteration = 0
self._time_total = 0.0
self._timesteps_total = 0
self._setup()
self._initialize_ok = True
self._local_ip = ray.services.get_node_ip_address()
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``, ``_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")
ray.utils.try_to_create_directory(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.
Args:
config (dict): The Trainer's config dict.
"""
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))
TrainableUtil.make_checkpoint_dir(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
if os.path.isdir(checkpoint_path):
# Add trailing slash to prevent tune metadata from
# being written outside the directory.
checkpoint_path = os.path.join(checkpoint_path, "")
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_dict = TrainableUtil.pickle_checkpoint(checkpoint_path)
out = io.BytesIO()
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 on %s from checkpoint: %s", self.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().
"""
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
os.makedirs(os.path.dirname(path), exist_ok=True)
with open(path, "wb") as f:
f.write(file_contents)
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 (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 os.path.join(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.utils 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 {}
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 ``_train``, ``_save``, and
``_restore`` here when subclassing Trainable.
Note that, if you don't require checkpoint/restore functionality, then
instead of implementing this class you can also get away with supplying
just a ``my_train(config, reporter)`` function to the config.
The function will be automatically converted to this interface
(sans checkpoint functionality).
"""
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, 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
self._setup(copy.deepcopy(self.config))
self._local_ip = ray.services.get_node_ip_address()
@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.
"""
return Resources(cpu=1, gpu=0)
@classmethod
def resource_help(cls, config):
"""Returns a help string for configuring this trainable's resources."""
return ""
def 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.
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().
`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."
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)
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 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)
or checkpoint == checkpoint_dir):
raise ValueError(
"The returned checkpoint path must be within the "
"given checkpoint dir {}: {}".format(
checkpoint_dir, checkpoint))
if not os.path.exists(checkpoint):
raise ValueError(
"The returned checkpoint path does not exist: {}".format(
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(
"`_save` must return a dict or string type: {}".format(
str(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
}, 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_prefix = self.save(tmpdir)
data = {}
base_dir = os.path.dirname(checkpoint_prefix)
for path in os.listdir(base_dir):
path = os.path.join(base_dir, path)
if path.startswith(checkpoint_prefix):
with open(path, "rb") as f:
data[os.path.basename(path)] = f.read()
out = io.BytesIO()
data_dict = pickle.dumps({
"checkpoint_name": os.path.basename(checkpoint_prefix),
"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)
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
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 file_name, file_contents in data.items():
with open(os.path.join(tmpdir, file_name), "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.
Return:
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.
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.close()
self._stop()
def _train(self):
"""Subclasses should override this to implement train().
Returns:
A dict that describes training progress."""
raise NotImplementedError
def _save(self, checkpoint_dir):
"""Subclasses should override this to implement save().
Args:
checkpoint_dir (str): The directory where the checkpoint
file must be stored.
Returns:
checkpoint (str | dict): If string, the return value is
expected to be the checkpoint path that will be passed to
`_restore()`. 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"}
"""
raise NotImplementedError
def _restore(self, checkpoint):
"""Subclasses should override this to implement restore().
Args:
checkpoint (str | dict): Value as returned by `_save`.
If a string, then it is the checkpoint path.
"""
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."""
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 {}
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 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).
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._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):
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
}
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.
"""
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):
"""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: 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)
# Maybe sync to cloud
self._maybe_save_to_cloud()
return checkpoint_path
def _maybe_save_to_cloud(self):
# Derived classes like the FunctionRunner might call this
if self.uses_cloud_checkpointing:
self.storage_client.sync_up(self.logdir,
self.remote_checkpoint_dir)
self.storage_client.wait()
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.
"""
# Maybe sync from cloud
if self.uses_cloud_checkpointing:
self.storage_client.sync_down(self.remote_checkpoint_dir,
self.logdir)
self.storage_client.wait()
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(
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))
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 _update_resources(
self, new_resources: Union[PlacementGroupFactory, Resources]):
"""Internal version of ``update_resources``."""
self._trial_info.trial_resources = new_resources
return self.update_resources(new_resources)
def update_resources(
self, new_resources: Union[PlacementGroupFactory, Resources]):
"""Fires whenever Trainable resources are changed.
This method will be called before the checkpoint is loaded.
The current trial resources can also be obtained through
``self.trial_resources``.
Args:
new_resources (PlacementGroupFactory|Resources):
Updated resources. Will be a PlacementGroupFactory if
trial uses placement groups and Resources otherwise.
"""
return
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.
"""
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 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 ``_train``, ``_save``, and
``_restore`` here when subclassing Trainable.
Note that, if you don't require checkpoint/restore functionality, then
instead of implementing this class you can also get away with supplying
just a `my_train(config, reporter)` function and calling:
``register_trainable("my_func", train)``
to register it for use with Tune. The function will be automatically
converted to this interface (sans checkpoint functionality).
Attributes:
config (obj): The hyperparam configuration for this trial.
logdir (str): Directory in which training outputs should be placed.
registry (obj): Tune object registry which holds user-registered
classes and objects by name.
"""
def __init__(self, config=None, registry=None, logger_creator=None):
"""Initialize an Trainable.
Subclasses should prefer defining ``_setup()`` instead of overriding
``__init__()`` directly.
Args:
config (dict): Trainable-specific configuration data.
registry (obj): Object registry for user-defined envs, models, etc.
If unspecified, the default registry will be used.
logger_creator (func): Function that creates a ray.tune.Logger
object. If unspecified, a default logger is created.
"""
if registry is None:
from ray.tune.registry import get_registry
registry = get_registry()
self._initialize_ok = False
self._experiment_id = uuid.uuid4().hex
self.config = config or {}
self.registry = registry
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, None)
self._iteration = 0
self._time_total = 0.0
self._timesteps_total = 0
self._setup()
self._initialize_ok = True
self._local_ip = ray.services.get_node_ip_address()
def train(self):
"""Runs one logical iteration of training.
Subclasses should override ``_train()`` instead to return results.
This method auto-fills many fields, so only ``timesteps_this_iter``
is requied to be present.
Returns:
A TrainingResult that describes training progress.
"""
if not self._initialize_ok:
raise ValueError(
"Trainable initialization failed, see previous errors")
start = time.time()
result = self._train()
self._iteration += 1
if result.time_this_iter_s is not None:
time_this_iter = result.time_this_iter_s
else:
time_this_iter = time.time() - start
if result.timesteps_this_iter is None:
raise TuneError(
"Must specify timesteps_this_iter in result", result)
self._time_total += time_this_iter
self._timesteps_total += result.timesteps_this_iter
# Include the negative loss to use as a stopping condition
if result.mean_loss is not None:
neg_loss = -result.mean_loss
else:
neg_loss = result.neg_mean_loss
now = datetime.today()
result = result._replace(
experiment_id=self._experiment_id,
date=now.strftime("%Y-%m-%d_%H-%M-%S"),
timestamp=int(time.mktime(now.timetuple())),
training_iteration=self._iteration,
timesteps_total=self._timesteps_total,
time_this_iter_s=time_this_iter,
time_total_s=self._time_total,
neg_mean_loss=neg_loss,
pid=os.getpid(),
hostname=os.uname()[1],
node_ip=self._local_ip,
config=self.config)
self._result_logger.on_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 that may be passed to restore().
"""
checkpoint_path = self._save(checkpoint_dir or self.logdir)
pickle.dump(
[self._experiment_id, self._iteration, self._timesteps_total,
self._time_total],
open(checkpoint_path + ".tune_metadata", "wb"))
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_prefix = self.save(tmpdir)
data = {}
base_dir = os.path.dirname(checkpoint_prefix)
for path in os.listdir(base_dir):
path = os.path.join(base_dir, path)
if path.startswith(checkpoint_prefix):
data[os.path.basename(path)] = open(path, "rb").read()
out = io.BytesIO()
with gzip.GzipFile(fileobj=out, mode="wb") as f:
compressed = pickle.dumps({
"checkpoint_name": os.path.basename(checkpoint_prefix),
"data": data,
})
if len(compressed) > 10e6: # getting pretty large
print("Checkpoint size is {} bytes".format(len(compressed)))
f.write(compressed)
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.
"""
self._restore(checkpoint_path)
metadata = pickle.load(open(checkpoint_path + ".tune_metadata", "rb"))
self._experiment_id = metadata[0]
self._iteration = metadata[1]
self._timesteps_total = metadata[2]
self._time_total = metadata[3]
def restore_from_object(self, obj):
"""Restores training state from a checkpoint object.
These checkpoints are returned from calls to save_to_object().
"""
out = io.BytesIO(obj)
info = pickle.loads(gzip.GzipFile(fileobj=out, mode="rb").read())
data = info["data"]
tmpdir = tempfile.mkdtemp("restore_from_object", dir=self.logdir)
checkpoint_path = os.path.join(tmpdir, info["checkpoint_name"])
for file_name, file_contents in data.items():
with open(os.path.join(tmpdir, file_name), "wb") as f:
f.write(file_contents)
self.restore(checkpoint_path)
shutil.rmtree(tmpdir)
def stop(self):
"""Releases all resources used by this trainable."""
if self._initialize_ok:
self._result_logger.close()
self._stop()
def _train(self):
"""Subclasses should override this to implement train()."""
raise NotImplementedError
def _save(self, checkpoint_dir):
"""Subclasses should override this to implement save()."""
raise NotImplementedError
def _restore(self, checkpoint_path):
"""Subclasses should override this to implement restore()."""
raise NotImplementedError
def _setup(self):
"""Subclasses should override this for custom initialization."""
pass
def _stop(self):
"""Subclasses should override this for any cleanup on stop."""
pass
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 ``_train``, ``_save``, and
``_restore`` here when subclassing Trainable.
Note that, if you don't require checkpoint/restore functionality, then
instead of implementing this class you can also get away with supplying
just a ``my_train(config, reporter)`` function to the config.
The function will be automatically converted to this interface
(sans checkpoint functionality).
"""
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 {}
log_sys_usage = self.config.get("log_sys_usage", False)
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, 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
self._setup(copy.deepcopy(self.config))
self._local_ip = ray.services.get_node_ip_address()
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.
"""
return None
@classmethod
def resource_help(cls, config):
"""Returns a help string for configuring this trainable's resources."""
return ""
def 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.
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().
`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 delete_checkpoint(self, checkpoint_dir):
"""Removes subdirectory within checkpoint_folder
Parameters
----------
checkpoint_dir : path to checkpoint
"""
if os.path.isfile(checkpoint_dir):
shutil.rmtree(os.path.dirname(checkpoint_dir))
else:
shutil.rmtree(checkpoint_dir)
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
}, 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
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.
Return:
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.
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.close()
self._stop()
def _train(self):
"""Subclasses should override this to implement train().
Returns:
A dict that describes training progress."""
raise NotImplementedError
def _save(self, checkpoint_dir):
"""Subclasses should override this to implement save().
Args:
checkpoint_dir (str): The directory where the checkpoint
file must be stored.
Returns:
checkpoint (str | dict): If string, the return value is
expected to be the checkpoint path or prefix to be passed to
`_restore()`. 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"}
"""
raise NotImplementedError
def _restore(self, checkpoint):
"""Subclasses should override this to implement restore().
Args:
checkpoint (str | dict): Value as returned by `_save`.
If a string, then it is the checkpoint path.
"""
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."""
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 {}
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,
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.
"""
Trial._registration_check(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.
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.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 _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 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,
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
def should_stop(self, result):
"""Whether the given result meets this trial's stopping criteria."""
if result.get(DONE):
return True
return recursive_criteria_check(result, self.stopping_criterion)
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):
result.update(trial_id=self.trial_id, done=terminate)
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]
@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 ``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(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()
def logger_creator(config):
if not os.path.exists(custom_path):
os.makedirs(custom_path)
logdir = tempfile.mkdtemp(prefix=logdir_prefix, dir=custom_path)
return UnifiedLogger(config, logdir, loggers=None)
class Agent(Trainable):
"""All RLlib agents extend this base class.
Agent objects retain internal model state between calls to train(), so
you should create a new agent instance for each training session.
Attributes:
env_creator (func): Function that creates a new training env.
config (obj): Algorithm-specific configuration data.
logdir (str): Directory in which training outputs should be placed.
registry (obj): Tune object registry, for registering user-defined
classes and objects by name.
"""
_allow_unknown_configs = False
_default_logdir = "/tmp/ray"
def __init__(self,
config={},
env=None,
registry=None,
logger_creator=None):
"""Initialize an RLLib agent.
Args:
config (dict): Algorithm-specific configuration data.
env (str): Name of the environment to use. Note that this can also
be specified as the `env` key in config.
registry (obj): Object registry for user-defined envs, models, etc.
If unspecified, it will be assumed empty.
logger_creator (func): Function that creates a ray.tune.Logger
object. If unspecified, a default logger is created.
"""
self._initialize_ok = False
self._experiment_id = uuid.uuid4().hex
env = env or config.get("env")
if env:
config["env"] = env
if registry and registry.contains(ENV_CREATOR, env):
self.env_creator = registry.get(ENV_CREATOR, env)
else:
import gym
self.env_creator = lambda: gym.make(env)
self.config = self._default_config.copy()
self.registry = registry
if not self._allow_unknown_configs:
for k in config.keys():
if k not in self.config and k != "env":
raise Exception("Unknown agent config `{}`, "
"all agent configs: {}".format(
k, self.config.keys()))
self.config.update(config)
if logger_creator:
self._result_logger = logger_creator(self.config)
self.logdir = self._result_logger.logdir
else:
logdir_suffix = "{}_{}_{}".format(
env, self._agent_name,
datetime.today().strftime("%Y-%m-%d_%H-%M-%S"))
if not os.path.exists(self._default_logdir):
os.makedirs(self._default_logdir)
self.logdir = tempfile.mkdtemp(prefix=logdir_suffix,
dir=self._default_logdir)
self._result_logger = UnifiedLogger(self.config, self.logdir, None)
self._iteration = 0
self._time_total = 0.0
self._timesteps_total = 0
with tf.Graph().as_default():
self._init()
self._initialize_ok = True
def _init(self, config, env_creator):
"""Subclasses should override this for custom initialization."""
raise NotImplementedError
def train(self):
"""Runs one logical iteration of training.
Returns:
A TrainingResult that describes training progress.
"""
if not self._initialize_ok:
raise ValueError(
"Agent initialization failed, see previous errors")
start = time.time()
result = self._train()
self._iteration += 1
if result.time_this_iter_s is not None:
time_this_iter = result.time_this_iter_s
else:
time_this_iter = time.time() - start
assert result.timesteps_this_iter is not None
self._time_total += time_this_iter
self._timesteps_total += result.timesteps_this_iter
result = result._replace(experiment_id=self._experiment_id,
training_iteration=self._iteration,
timesteps_total=self._timesteps_total,
time_this_iter_s=time_this_iter,
time_total_s=self._time_total,
pid=os.getpid(),
hostname=os.uname()[1])
self._result_logger.on_result(result)
return result
def save(self):
"""Saves the current model state to a checkpoint.
Returns:
Checkpoint path that may be passed to restore().
"""
checkpoint_path = self._save()
pickle.dump([
self._experiment_id, self._iteration, self._timesteps_total,
self._time_total
], open(checkpoint_path + ".rllib_metadata", "wb"))
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.
"""
checkpoint_prefix = self.save()
data = {}
base_dir = os.path.dirname(checkpoint_prefix)
for path in os.listdir(base_dir):
path = os.path.join(base_dir, path)
if path.startswith(checkpoint_prefix):
data[os.path.basename(path)] = open(path, "rb").read()
out = io.BytesIO()
with gzip.GzipFile(fileobj=out, mode="wb") as f:
compressed = pickle.dumps({
"checkpoint_name":
os.path.basename(checkpoint_prefix),
"data":
data,
})
print("Saving checkpoint to object store, {} bytes".format(
len(compressed)))
f.write(compressed)
return out.getvalue()
def restore(self, checkpoint_path):
"""Restores training state from a given model checkpoint.
These checkpoints are returned from calls to save().
"""
self._restore(checkpoint_path)
metadata = pickle.load(open(checkpoint_path + ".rllib_metadata", "rb"))
self._experiment_id = metadata[0]
self._iteration = metadata[1]
self._timesteps_total = metadata[2]
self._time_total = metadata[3]
def restore_from_object(self, obj):
"""Restores training state from a checkpoint object.
These checkpoints are returned from calls to save_to_object().
"""
out = io.BytesIO(obj)
info = pickle.loads(gzip.GzipFile(fileobj=out, mode="rb").read())
data = info["data"]
tmpdir = tempfile.mkdtemp("restore_from_object", dir=self.logdir)
checkpoint_path = os.path.join(tmpdir, info["checkpoint_name"])
for file_name, file_contents in data.items():
with open(os.path.join(tmpdir, file_name), "wb") as f:
f.write(file_contents)
self.restore(checkpoint_path)
shutil.rmtree(tmpdir)
def stop(self):
"""Releases all resources used by this agent."""
if self._initialize_ok:
self._result_logger.close()
self._stop()
def _stop(self):
"""Subclasses should override this for custom stopping."""
pass
def compute_action(self, observation):
"""Computes an action using the current trained policy."""
raise NotImplementedError
@property
def iteration(self):
"""Current training iter, auto-incremented with each train() call."""
return self._iteration
@property
def _agent_name(self):
"""Subclasses should override this to declare their name."""
raise NotImplementedError
@property
def _default_config(self):
"""Subclasses should override this to declare their default config."""
raise NotImplementedError
def _train(self):
"""Subclasses should override this to implement train()."""
raise NotImplementedError
def _save(self):
"""Subclasses should override this to implement save()."""
raise NotImplementedError
def _restore(self):
"""Subclasses should override this to implement restore()."""
raise NotImplementedError
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.
"""
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)
# 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.trial_name_creator = trial_name_creator
self.custom_loggers = custom_loggers
self.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.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
@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)
self.logdir = tempfile.mkdtemp(prefix="{}_{}".format(
str(self)[:MAX_LEN_IDENTIFIER], date_str()),
dir=self.local_dir)
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.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_creator:
return self.trial_name_creator(self)
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
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 ``_train``, ``_save``, and
``_restore`` here when subclassing Trainable.
Note that, if you don't require checkpoint/restore functionality, then
instead of implementing this class you can also get away with supplying
just a ``my_train(config, reporter)`` function to the config.
The function will be automatically converted to this interface
(sans checkpoint functionality).
"""
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, None)
self._iteration = 0
self._time_total = 0.0
self._timesteps_total = None
self._time_since_restore = 0.0
self._timesteps_since_restore = 0
self._iterations_since_restore = 0
self._restored = False
self._setup()
self._local_ip = ray.services.get_node_ip_address()
@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.
"""
return Resources(cpu=1, gpu=0)
@classmethod
def resource_help(cls, config):
"""Returns a help string for configuring this trainable's resources."""
return ""
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().
`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()
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):
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._timesteps_total should not override user-provided total
result.setdefault(TIMESTEPS_TOTAL, self._timesteps_total)
# 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())),
training_iteration=self._iteration,
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)
self._result_logger.on_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 that may be passed to restore().
"""
checkpoint_path = self._save(checkpoint_dir or self.logdir)
pickle.dump([
self._experiment_id, self._iteration, self._timesteps_total,
self._time_total
], open(checkpoint_path + ".tune_metadata", "wb"))
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_prefix = self.save(tmpdir)
data = {}
base_dir = os.path.dirname(checkpoint_prefix)
for path in os.listdir(base_dir):
path = os.path.join(base_dir, path)
if path.startswith(checkpoint_prefix):
data[os.path.basename(path)] = open(path, "rb").read()
out = io.BytesIO()
with gzip.GzipFile(fileobj=out, mode="wb") as f:
compressed = pickle.dumps({
"checkpoint_name": os.path.basename(checkpoint_prefix),
"data": data,
})
if len(compressed) > 10e6: # getting pretty large
logger.info("Checkpoint size is {} bytes".format(
len(compressed)))
f.write(compressed)
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.
"""
self._restore(checkpoint_path)
metadata = pickle.load(open(checkpoint_path + ".tune_metadata", "rb"))
self._experiment_id = metadata[0]
self._iteration = metadata[1]
self._timesteps_total = metadata[2]
self._time_total = metadata[3]
self._restored = True
def restore_from_object(self, obj):
"""Restores training state from a checkpoint object.
These checkpoints are returned from calls to save_to_object().
"""
out = io.BytesIO(obj)
info = pickle.loads(gzip.GzipFile(fileobj=out, mode="rb").read())
data = info["data"]
tmpdir = tempfile.mkdtemp("restore_from_object", dir=self.logdir)
checkpoint_path = os.path.join(tmpdir, info["checkpoint_name"])
for file_name, file_contents in data.items():
with open(os.path.join(tmpdir, file_name), "wb") as f:
f.write(file_contents)
self.restore(checkpoint_path)
shutil.rmtree(tmpdir)
def reset_config(self, new_config):
"""Resets configuration without restarting the trial.
Args:
new_config (dir): Updated hyperparameter configuration
for the trainable.
Returns:
True if configuration reset successfully else False.
"""
return False
def stop(self):
"""Releases all resources used by this trainable."""
self._result_logger.close()
self._stop()
def _train(self):
"""Subclasses should override this to implement train().
Returns:
A dict that describes training progress."""
raise NotImplementedError
def _save(self, checkpoint_dir):
"""Subclasses should override this to implement save().
Args:
checkpoint_dir (str): The directory where the checkpoint
can be stored.
Returns:
Checkpoint path that may be passed to restore(). Typically
would default to `checkpoint_dir`.
"""
raise NotImplementedError
def _restore(self, checkpoint_path):
"""Subclasses should override this to implement restore().
Args:
checkpoint_path (str): The directory where the checkpoint
is stored.
"""
raise NotImplementedError
def _setup(self):
"""Subclasses should override this for custom initialization.
Subclasses can access the hyperparameter configuration via
``self.config``.
"""
pass
def _stop(self):
"""Subclasses should override this for any cleanup on stop."""
pass
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,
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.address = 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")
self.checkpoint_freq = checkpoint_freq
self.checkpoint_at_end = checkpoint_at_end
# stores in memory max/min/last result for each metric by trial
self.metric_analysis = {}
self.sync_on_checkpoint = sync_on_checkpoint
newest_checkpoint = Checkpoint(Checkpoint.DISK, restore_path)
self.checkpoint_manager = CheckpointManager(keep_checkpoints_num,
checkpoint_score_attr)
self.checkpoint_manager.newest_checkpoint = newest_checkpoint
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.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)
@property
def node_ip(self):
return self.address.hostname
@property
def checkpoint(self):
return self.checkpoint_manager.newest_checkpoint
@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)
self.set_location(Location(worker_ip))
def set_location(self, location):
"""Sets the location of the trial."""
self.address = location
def set_status(self, status):
"""Sets the status of the trial."""
if status == Trial.RUNNING and self.start_time is None:
self.start_time = time.time()
self.status = status
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 # may be moved to outer scope?
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
def on_checkpoint(self, checkpoint):
"""Hook for handling checkpoints taken by the Trainable.
Args:
checkpoint (Checkpoint): Checkpoint taken.
"""
if self.sync_on_checkpoint and checkpoint.storage == Checkpoint.DISK:
# Wait for any other syncs to finish. We need to sync again after
# this to handle checkpoints taken mid-sync.
self.result_logger.wait()
# Force sync down and wait before tracking the new checkpoint. This
# prevents attempts to restore from partially synced checkpoints.
if self.result_logger.sync_down():
self.result_logger.wait()
else:
logger.error(
"Trial %s: Checkpoint sync skipped. "
"This should not happen.", self)
self.checkpoint_manager.on_checkpoint(checkpoint)
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.TERMINATED, Trial.ERROR]
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 TrackSession:
"""Manages results for a single session.
Represents a single Trial in an experiment.
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="",
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)
self.trial_id = Trial.generate_id()
if trial_name:
self.trial_id = trial_name + "_" + self.trial_id
if self.is_tune_session:
self._logger = _ReporterHook(_tune_reporter)
self._logdir = _tune_reporter.logdir
else:
self._initialize_logging(trial_name, experiment_dir, upload_dir,
trial_config)
def _initialize_logging(self,
trial_name="",
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(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):
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
def logger_creator(config):
if not os.path.exists(result_dir):
os.makedirs(result_dir)
logdir = tempfile.mkdtemp(prefix=logdir_prefix, dir=result_dir)
return UnifiedLogger(config, logdir, loggers=None)
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.
Attributes:
trainable_name (str): Name of the trainable object to be executed.
config (dict): Provided configuration dictionary with evaluated params.
trial_id (str): Unique identifier for the trial.
local_dir (str): Local_dir as passed to tune.run.
logdir (str): Directory where the trial logs are saved.
evaluated_params (dict): Evaluated parameters by search algorithm,
experiment_tag (str): Identifying trial name to show in the console.
resources (Resources): Amount of resources that this trial will use.
status (str): One of PENDING, RUNNING, PAUSED, TERMINATED, ERROR/
error_file (str): Path to the errors that this trial has raised.
"""
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,
log_to_file=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:
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.log_to_file = log_to_file
# Make sure `stdout_file, stderr_file = Trial.log_to_file` works
if not self.log_to_file or not isinstance(self.log_to_file, Sequence) \
or not len(self.log_to_file) == 2:
self.log_to_file = (None, None)
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/avg/last-n-avg/last result for each
# metric by trial
self.metric_analysis = {}
# keep a moving average over these last n steps
self.n_steps = [5, 10]
self.metric_n_steps = {}
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
self.saving_to = None
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
self.checkpoint_manager = CheckpointManager(
keep_checkpoints_num, checkpoint_score_attr,
checkpoint_deleter(self._trainable_name(), self.runner))
# Restoration fields
self.restore_path = restore_path
self.restoring_from = None
self.num_failures = 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):
"""Returns the most recent checkpoint.
If the trial is in ERROR state, the most recent PERSISTENT checkpoint
is returned.
"""
if self.status == Trial.ERROR:
checkpoint = self.checkpoint_manager.newest_persistent_checkpoint
else:
checkpoint = self.checkpoint_manager.newest_checkpoint
if checkpoint.value is None:
checkpoint = Checkpoint(Checkpoint.PERSISTENT, self.restore_path)
return 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(
self._trainable_name() + "_" + self.experiment_tag,
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(
self._trainable_name(), 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
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:
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 - %s", self, 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,
"avg": value,
"last": value
}
self.metric_n_steps[metric] = {}
for n in self.n_steps:
key = "last-{:d}-avg".format(n)
self.metric_analysis[metric][key] = value
# Store n as string for correct restore.
self.metric_n_steps[metric][str(n)] = deque([value],
maxlen=n)
else:
step = result["training_iteration"] or 1
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]["avg"] = 1 / step * (
value +
(step - 1) * self.metric_analysis[metric]["avg"])
self.metric_analysis[metric]["last"] = value
for n in self.n_steps:
key = "last-{:d}-avg".format(n)
self.metric_n_steps[metric][str(n)].append(value)
self.metric_analysis[metric][key] = sum(
self.metric_n_steps[metric][str(n)]) / len(
self.metric_n_steps[metric][str(n)])
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
@property
def is_saving(self):
return self.saving_to is not None
def __repr__(self):
return self._trainable_name(include_trial_id=True)
def __str__(self):
return self._trainable_name(include_trial_id=True)
def _trainable_name(self, include_trial_id=False):
"""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
if include_trial_id:
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
# Avoid waiting for events that will never occur on resume.
state["resuming_from"] = None
state["saving_to"] = 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 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.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,
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.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)
# These are non-pickleable entries.
pickle_data = {
"_checkpoint": self._checkpoint,
"config": self.config,
"loggers": self.loggers,
"sync_function": self.sync_function,
"last_result": self.last_result
}
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", "loggers", "sync_function",
"last_result"
]:
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()
def logger_creator(config):
if not os.path.exists(checkpoint_path):
os.makedirs(checkpoint_path)
logdir = tempfile.mkdtemp(prefix=logdir_prefix, dir=checkpoint_path)
print('logger creator: ', logdir)
return UnifiedLogger(config, logdir, None)
