import copy
import datetime
import logging
import pprint as pp
import traceback
from functools import partial
from pathlib import Path
from pickle import PicklingError
from typing import (
TYPE_CHECKING,
Any,
Callable,
Dict,
List,
Mapping,
Optional,
Sequence,
Type,
Union,
)
import ray
from ray.exceptions import RpcError
from ray.train._internal.storage import StorageContext
from ray.train.constants import DEFAULT_STORAGE_PATH
from ray.tune import CheckpointConfig, SyncConfig
from ray.tune.error import TuneError
from ray.tune.registry import is_function_trainable, register_trainable
from ray.tune.stopper import CombinedStopper, FunctionStopper, Stopper, TimeoutStopper
from ray.util.annotations import Deprecated, DeveloperAPI
if TYPE_CHECKING:
import pyarrow.fs
from ray.tune import PlacementGroupFactory
from ray.tune.experiment import Trial
logger = logging.getLogger(__name__)
def _validate_log_to_file(log_to_file):
"""Validate ``tune.RunConfig``'s ``log_to_file`` parameter. Return
validated relative stdout and stderr filenames."""
if not log_to_file:
stdout_file = stderr_file = None
elif isinstance(log_to_file, bool) and log_to_file:
stdout_file = "stdout"
stderr_file = "stderr"
elif isinstance(log_to_file, str):
stdout_file = stderr_file = log_to_file
elif isinstance(log_to_file, Sequence):
if len(log_to_file) != 2:
raise ValueError(
"If you pass a Sequence to `log_to_file` it has to have "
"a length of 2 (for stdout and stderr, respectively). The "
"Sequence you passed has length {}.".format(len(log_to_file))
)
stdout_file, stderr_file = log_to_file
else:
raise ValueError(
"You can pass a boolean, a string, or a Sequence of length 2 to "
"`log_to_file`, but you passed something else ({}).".format(
type(log_to_file)
)
)
return stdout_file, stderr_file
[docs]
@DeveloperAPI
class Experiment:
"""Tracks experiment specifications.
Implicitly registers the Trainable if needed. The args here take
the same meaning as the arguments defined `tune.py:run`.
.. code-block:: python
experiment_spec = Experiment(
"my_experiment_name",
my_func,
stop={"mean_accuracy": 100},
config={
"alpha": tune.grid_search([0.2, 0.4, 0.6]),
"beta": tune.grid_search([1, 2]),
},
resources_per_trial={
"cpu": 1,
"gpu": 0
},
num_samples=10,
local_dir="~/ray_results",
checkpoint_freq=10,
max_failures=2)
"""
# Keys that will be present in `public_spec` dict.
PUBLIC_KEYS = {"stop", "num_samples", "time_budget_s"}
_storage_context_cls = StorageContext
def __init__(
self,
name: str,
run: Union[str, Callable, Type],
*,
stop: Optional[Union[Mapping, Stopper, Callable[[str, Mapping], bool]]] = None,
time_budget_s: Optional[Union[int, float, datetime.timedelta]] = None,
config: Optional[Dict[str, Any]] = None,
resources_per_trial: Union[
None, Mapping[str, Union[float, int, Mapping]], "PlacementGroupFactory"
] = None,
num_samples: int = 1,
storage_path: Optional[str] = None,
storage_filesystem: Optional["pyarrow.fs.FileSystem"] = None,
sync_config: Optional[Union[SyncConfig, dict]] = None,
checkpoint_config: Optional[Union[CheckpointConfig, dict]] = None,
trial_name_creator: Optional[Callable[["Trial"], str]] = None,
trial_dirname_creator: Optional[Callable[["Trial"], str]] = None,
log_to_file: bool = False,
export_formats: Optional[Sequence] = None,
max_failures: int = 0,
restore: Optional[str] = None,
# Deprecated
local_dir: Optional[str] = None,
):
if isinstance(checkpoint_config, dict):
checkpoint_config = CheckpointConfig(**checkpoint_config)
else:
checkpoint_config = checkpoint_config or CheckpointConfig()
if is_function_trainable(run):
if checkpoint_config.checkpoint_at_end:
raise ValueError(
"'checkpoint_at_end' cannot be used with a function trainable. "
"You should include one last call to "
"`ray.tune.report(metrics=..., checkpoint=...)` "
"at the end of your training loop to get this behavior."
)
if checkpoint_config.checkpoint_frequency:
raise ValueError(
"'checkpoint_frequency' cannot be set for a function trainable. "
"You will need to report a checkpoint every "
"`checkpoint_frequency` iterations within your training loop using "
"`ray.tune.report(metrics=..., checkpoint=...)` "
"to get this behavior."
)
try:
self._run_identifier = Experiment.register_if_needed(run)
except RpcError as e:
if e.rpc_code == ray._raylet.GRPC_STATUS_CODE_RESOURCE_EXHAUSTED:
raise TuneError(
f"The Trainable/training function is too large for grpc resource "
f"limit. Check that its definition is not implicitly capturing a "
f"large array or other object in scope. "
f"Tip: use tune.with_parameters() to put large objects "
f"in the Ray object store. \n"
f"Original exception: {traceback.format_exc()}"
)
else:
raise e
if not name:
name = StorageContext.get_experiment_dir_name(run)
storage_path = storage_path or DEFAULT_STORAGE_PATH
self.storage = self._storage_context_cls(
storage_path=storage_path,
storage_filesystem=storage_filesystem,
sync_config=sync_config,
experiment_dir_name=name,
)
logger.debug(f"StorageContext on the DRIVER:\n{self.storage}")
config = config or {}
if not isinstance(config, dict):
raise ValueError(
f"`Experiment(config)` must be a dict, got: {type(config)}. "
"Please convert your search space to a dict before passing it in."
)
self._stopper = None
stopping_criteria = {}
if not stop:
pass
elif isinstance(stop, list):
bad_stoppers = [s for s in stop if not isinstance(s, Stopper)]
if bad_stoppers:
stopper_types = [type(s) for s in stop]
raise ValueError(
"If you pass a list as the `stop` argument to "
"`tune.RunConfig()`, each element must be an instance of "
f"`tune.stopper.Stopper`. Got {stopper_types}."
)
self._stopper = CombinedStopper(*stop)
elif isinstance(stop, dict):
stopping_criteria = stop
elif callable(stop):
if FunctionStopper.is_valid_function(stop):
self._stopper = FunctionStopper(stop)
elif isinstance(stop, Stopper):
self._stopper = stop
else:
raise ValueError(
"Provided stop object must be either a dict, "
"a function, or a subclass of "
f"`ray.tune.Stopper`. Got {type(stop)}."
)
else:
raise ValueError(
f"Invalid stop criteria: {stop}. Must be a "
f"callable or dict. Got {type(stop)}."
)
if time_budget_s:
if self._stopper:
self._stopper = CombinedStopper(
self._stopper, TimeoutStopper(time_budget_s)
)
else:
self._stopper = TimeoutStopper(time_budget_s)
stdout_file, stderr_file = _validate_log_to_file(log_to_file)
spec = {
"run": self._run_identifier,
"stop": stopping_criteria,
"time_budget_s": time_budget_s,
"config": config,
"resources_per_trial": resources_per_trial,
"num_samples": num_samples,
"checkpoint_config": checkpoint_config,
"trial_name_creator": trial_name_creator,
"trial_dirname_creator": trial_dirname_creator,
"log_to_file": (stdout_file, stderr_file),
"export_formats": export_formats or [],
"max_failures": max_failures,
"restore": (
Path(restore).expanduser().absolute().as_posix() if restore else None
),
"storage": self.storage,
}
self.spec = spec
[docs]
@classmethod
def from_json(cls, name: str, spec: dict):
"""Generates an Experiment object from JSON.
Args:
name: Name of Experiment.
spec: JSON configuration of experiment.
"""
if "run" not in spec:
raise TuneError("No trainable specified!")
# Special case the `env` param for RLlib by automatically
# moving it into the `config` section.
if "env" in spec:
spec["config"] = spec.get("config", {})
spec["config"]["env"] = spec["env"]
del spec["env"]
if "sync_config" in spec and isinstance(spec["sync_config"], dict):
spec["sync_config"] = SyncConfig(**spec["sync_config"])
if "checkpoint_config" in spec and isinstance(spec["checkpoint_config"], dict):
spec["checkpoint_config"] = CheckpointConfig(**spec["checkpoint_config"])
spec = copy.deepcopy(spec)
run_value = spec.pop("run")
try:
exp = cls(name, run_value, **spec)
except TypeError as e:
raise TuneError(
f"Failed to load the following Tune experiment "
f"specification:\n\n {pp.pformat(spec)}.\n\n"
f"Please check that the arguments are valid. "
f"Experiment creation failed with the following "
f"error:\n {e}"
)
return exp
[docs]
@classmethod
def get_trainable_name(cls, run_object: Union[str, Callable, Type]):
"""Get Trainable name.
Args:
run_object: Trainable to run. If string,
assumes it is an ID and does not modify it. Otherwise,
returns a string corresponding to the run_object name.
Returns:
A string representing the trainable identifier.
Raises:
TuneError: if ``run_object`` passed in is invalid.
"""
from ray.tune.search.sample import Domain
if isinstance(run_object, str) or isinstance(run_object, Domain):
return run_object
elif isinstance(run_object, type) or callable(run_object):
name = "DEFAULT"
if hasattr(run_object, "_name"):
name = run_object._name
elif hasattr(run_object, "__name__"):
fn_name = run_object.__name__
if fn_name == "<lambda>":
name = "lambda"
elif fn_name.startswith("<"):
name = "DEFAULT"
else:
name = fn_name
elif (
isinstance(run_object, partial)
and hasattr(run_object, "func")
and hasattr(run_object.func, "__name__")
):
name = run_object.func.__name__
else:
logger.warning("No name detected on trainable. Using {}.".format(name))
return name
else:
raise TuneError("Improper 'run' - not string nor trainable.")
[docs]
@classmethod
def register_if_needed(cls, run_object: Union[str, Callable, Type]):
"""Registers Trainable or Function at runtime.
Assumes already registered if run_object is a string.
Also, does not inspect interface of given run_object.
Args:
run_object: Trainable to run. If string,
assumes it is an ID and does not modify it. Otherwise,
returns a string corresponding to the run_object name.
Returns:
A string representing the trainable identifier.
"""
from ray.tune.search.sample import Domain
if isinstance(run_object, str):
return run_object
elif isinstance(run_object, Domain):
logger.warning("Not registering trainable. Resolving as variant.")
return run_object
name = cls.get_trainable_name(run_object)
try:
register_trainable(name, run_object)
except (TypeError, PicklingError) as e:
extra_msg = (
"Other options: "
"\n-Try reproducing the issue by calling "
"`pickle.dumps(trainable)`. "
"\n-If the error is typing-related, try removing "
"the type annotations and try again."
)
raise type(e)(str(e) + " " + extra_msg) from None
return name
@property
def stopper(self):
return self._stopper
@property
def local_path(self) -> Optional[str]:
return self.storage.experiment_driver_staging_path
@property
@Deprecated("Replaced by `local_path`")
def local_dir(self):
# TODO(justinvyu): [Deprecated] Remove in 2.11.
raise DeprecationWarning("Use `local_path` instead of `local_dir`.")
@property
def remote_path(self) -> Optional[str]:
return self.storage.experiment_fs_path
@property
def path(self) -> Optional[str]:
return self.remote_path or self.local_path
@property
def checkpoint_config(self):
return self.spec.get("checkpoint_config")
@property
@Deprecated("Replaced by `local_path`")
def checkpoint_dir(self):
# TODO(justinvyu): [Deprecated] Remove in 2.11.
raise DeprecationWarning("Use `local_path` instead of `checkpoint_dir`.")
@property
def run_identifier(self):
"""Returns a string representing the trainable identifier."""
return self._run_identifier
@property
def public_spec(self) -> Dict[str, Any]:
"""Returns the spec dict with only the public-facing keys.
Intended to be used for passing information to callbacks,
Searchers and Schedulers.
"""
return {k: v for k, v in self.spec.items() if k in self.PUBLIC_KEYS}
def _convert_to_experiment_list(experiments: Union[Experiment, List[Experiment], Dict]):
"""Produces a list of Experiment objects.
Converts input from dict, single experiment, or list of
experiments to list of experiments. If input is None,
will return an empty list.
Arguments:
experiments: Experiments to run.
Returns:
List of experiments.
"""
exp_list = experiments
# Transform list if necessary
if experiments is None:
exp_list = []
elif isinstance(experiments, Experiment):
exp_list = [experiments]
elif type(experiments) is dict:
exp_list = [
Experiment.from_json(name, spec) for name, spec in experiments.items()
]
# Validate exp_list
if type(exp_list) is list and all(isinstance(exp, Experiment) for exp in exp_list):
if len(exp_list) > 1:
logger.info(
"Running with multiple concurrent experiments. "
"All experiments will be using the same SearchAlgorithm."
)
else:
raise TuneError("Invalid argument: {}".format(experiments))
return exp_list