Source code for ray.tune.tuner

import logging
from pathlib import Path
from typing import Any, Callable, Dict, Optional, Type, Union, TYPE_CHECKING

import ray

from ray.air.config import RunConfig
from ray.air._internal.remote_storage import list_at_uri
from ray.air._internal.usage import AirEntrypoint
from ray.air.util.node import _force_on_current_node
from ray.tune import TuneError
from ray.tune.execution.experiment_state import _ResumeConfig
from ray.tune.experimental.output import (
from ray.tune.result_grid import ResultGrid
from ray.tune.trainable import Trainable
from ray.tune.impl.tuner_internal import TunerInternal, _TUNER_PKL
from ray.tune.tune_config import TuneConfig
from ray.tune.progress_reporter import (
from ray.util import PublicAPI

logger = logging.getLogger(__name__)

    from ray.train.base_trainer import BaseTrainer

ClientActorHandle = Any

# try:
#     # Breaks lint right now.
#     from ray.util.client.common import ClientActorHandle
# except Exception:
#     pass

# The magic key that is used when instantiating Tuner during resume.
_TUNER_INTERNAL = "_tuner_internal"
_SELF = "self"

    "The Ray Tune run failed. Please inspect the previous error messages for a "
    "cause. After fixing the issue, you can restart the run from scratch or "
    "continue this run. To continue this run, you can use "
    '`tuner = Tuner.restore("{path}", trainable=...)`.'

[docs]@PublicAPI(stability="beta") class Tuner: """Tuner is the recommended way of launching hyperparameter tuning jobs with Ray Tune. Args: trainable: The trainable to be tuned. param_space: Search space of the tuning job. One thing to note is that both preprocessor and dataset can be tuned here. tune_config: Tuning algorithm specific configs. Refer to ray.tune.tune_config.TuneConfig for more info. run_config: Runtime configuration that is specific to individual trials. If passed, this will overwrite the run config passed to the Trainer, if applicable. Refer to ray.air.config.RunConfig for more info. Usage pattern: .. code-block:: python from sklearn.datasets import load_breast_cancer from ray import tune from import from_pandas from ray.air.config import RunConfig, ScalingConfig from ray.train.xgboost import XGBoostTrainer from ray.tune.tuner import Tuner def get_dataset(): data_raw = load_breast_cancer(as_frame=True) dataset_df = data_raw["data"] dataset_df["target"] = data_raw["target"] dataset = from_pandas(dataset_df) return dataset trainer = XGBoostTrainer( label_column="target", params={}, datasets={"train": get_dataset()}, ) param_space = { "scaling_config": ScalingConfig( num_workers=tune.grid_search([2, 4]), resources_per_worker={ "CPU": tune.grid_search([1, 2]), }, ), # You can even grid search various datasets in Tune. # "datasets": { # "train": tune.grid_search( # [ds1, ds2] # ), # }, "params": { "objective": "binary:logistic", "tree_method": "approx", "eval_metric": ["logloss", "error"], "eta": tune.loguniform(1e-4, 1e-1), "subsample": tune.uniform(0.5, 1.0), "max_depth": tune.randint(1, 9), }, } tuner = Tuner(trainable=trainer, param_space=param_space, run_config=RunConfig(name="my_tune_run")) results = To retry a failed tune run, you can then do .. code-block:: python tuner = Tuner.restore(results.experiment_path, trainable=trainer) ``results.experiment_path`` can be retrieved from the :ref:`ResultGrid object <tune-analysis-docs>`. It can also be easily seen in the log output from your first run. """ # One of the following is assigned. _local_tuner: Optional[TunerInternal] # Only used in none ray client mode. _remote_tuner: Optional[ClientActorHandle] # Only used in ray client mode.
[docs] def __init__( self, trainable: Optional[ Union[str, Callable, Type[Trainable], "BaseTrainer"] ] = None, *, param_space: Optional[Dict[str, Any]] = None, tune_config: Optional[TuneConfig] = None, run_config: Optional[RunConfig] = None, # This is internal only arg. # Only for dogfooding purposes. We can slowly promote these args # to RunConfig or TuneConfig as needed. # TODO(xwjiang): Remove this later. _tuner_kwargs: Optional[Dict] = None, _tuner_internal: Optional[TunerInternal] = None, _entrypoint: AirEntrypoint = AirEntrypoint.TUNER, ): """Configure and construct a tune run.""" kwargs = locals().copy() self._is_ray_client = ray.util.client.ray.is_connected() if self._is_ray_client: _run_config = run_config or RunConfig() if get_air_verbosity(_run_config.verbose) is not None: "[output] This uses the legacy output and progress reporter, " "as Ray client is not supported by the new engine. " "For more information, see " "" ) if _tuner_internal: if not self._is_ray_client: self._local_tuner = kwargs[_TUNER_INTERNAL] else: self._remote_tuner = kwargs[_TUNER_INTERNAL] else: kwargs.pop(_TUNER_INTERNAL, None) kwargs.pop(_SELF, None) if not self._is_ray_client: self._local_tuner = TunerInternal(**kwargs) else: self._remote_tuner = _force_on_current_node( ray.remote(num_cpus=0)(TunerInternal) ).remote(**kwargs)
[docs] @classmethod def restore( cls, path: str, trainable: Union[str, Callable, Type[Trainable], "BaseTrainer"], resume_unfinished: bool = True, resume_errored: bool = False, restart_errored: bool = False, param_space: Optional[Dict[str, Any]] = None, ) -> "Tuner": """Restores Tuner after a previously failed run. All trials from the existing run will be added to the result table. The argument flags control how existing but unfinished or errored trials are resumed. Finished trials are always added to the overview table. They will not be resumed. Unfinished trials can be controlled with the ``resume_unfinished`` flag. If ``True`` (default), they will be continued. If ``False``, they will be added as terminated trials (even if they were only created and never trained). Errored trials can be controlled with the ``resume_errored`` and ``restart_errored`` flags. The former will resume errored trials from their latest checkpoints. The latter will restart errored trials from scratch and prevent loading their last checkpoints. Args: path: The path where the previous failed run is checkpointed. This information could be easily located near the end of the console output of previous run. Note: depending on whether ray client mode is used or not, this path may or may not exist on your local machine. trainable: The trainable to use upon resuming the experiment. This should be the same trainable that was used to initialize the original Tuner. param_space: The same `param_space` that was passed to the original Tuner. This can be optionally re-specified due to the `param_space` potentially containing Ray object references (tuning over Datasets or tuning over several `ray.put` object references). **Tune expects the `param_space` to be unmodified**, and the only part that will be used during restore are the updated object references. Changing the hyperparameter search space then resuming is NOT supported by this API. resume_unfinished: If True, will continue to run unfinished trials. resume_errored: If True, will re-schedule errored trials and try to restore from their latest checkpoints. restart_errored: If True, will re-schedule errored trials but force restarting them from scratch (no checkpoint will be loaded). """ # TODO(xwjiang): Add some comments to clarify the config behavior across # retored runs. # For example, is callbacks supposed to be automatically applied # when a Tuner is restored and fit again? resume_config = _ResumeConfig( resume_unfinished=resume_unfinished, resume_errored=resume_errored, restart_errored=restart_errored, ) if not ray.util.client.ray.is_connected(): tuner_internal = TunerInternal( restore_path=path, resume_config=resume_config, trainable=trainable, param_space=param_space, ) return Tuner(_tuner_internal=tuner_internal) else: tuner_internal = _force_on_current_node( ray.remote(num_cpus=0)(TunerInternal) ).remote( restore_path=path, resume_config=resume_config, trainable=trainable, param_space=param_space, ) return Tuner(_tuner_internal=tuner_internal)
[docs] @classmethod def can_restore(cls, path: Union[str, Path]) -> bool: """Checks whether a given directory contains a restorable Tune experiment. Usage Pattern: Use this utility to switch between starting a new Tune experiment and restoring when possible. This is useful for experiment fault-tolerance when re-running a failed tuning script. .. code-block:: python import os from ray.tune import Tuner from ray.air import RunConfig def train_fn(config): # Make sure to implement checkpointing so that progress gets # saved on restore. pass name = "exp_name" local_dir = "~/ray_results" exp_dir = os.path.join(local_dir, name) if Tuner.can_restore(exp_dir): tuner = Tuner.restore(exp_dir, trainable=train_fn, resume_errored=True) else: tuner = Tuner( train_fn, run_config=RunConfig(name=name, local_dir=local_dir), ) Args: path: The path to the experiment directory of the Tune experiment. This can be either a local directory (e.g. ~/ray_results/exp_name) or a remote URI (e.g. s3://bucket/exp_name). Returns: bool: True if this path exists and contains the Tuner state to resume from """ return _TUNER_PKL in list_at_uri(str(path))
def _prepare_remote_tuner_for_jupyter_progress_reporting(self): run_config: RunConfig = ray.get(self._remote_tuner.get_run_config.remote()) progress_reporter, string_queue = _prepare_progress_reporter_for_ray_client( run_config.progress_reporter, run_config.verbose ) run_config.progress_reporter = progress_reporter ray.get( self._remote_tuner.set_run_config_and_remote_string_queue.remote( run_config, string_queue ) ) return progress_reporter, string_queue
[docs] def fit(self) -> ResultGrid: """Executes hyperparameter tuning job as configured and returns result. Failure handling: For the kind of exception that happens during the execution of a trial, one may inspect it together with stacktrace through the returned result grid. See ``ResultGrid`` for reference. Each trial may fail up to a certain number. This is configured by ``RunConfig.FailureConfig.max_failures``. Exception that happens beyond trials will be thrown by this method as well. In such cases, there will be instruction like the following printed out at the end of console output to inform users on how to resume. Please use `Tuner.restore` to resume. .. code-block:: python tuner = Tuner.restore("~/ray_results/tuner_resume", trainable=trainable) Raises: RayTaskError: If user-provided trainable raises an exception TuneError: General Ray Tune error. """ if not self._is_ray_client: try: return except TuneError as e: raise TuneError( _TUNER_FAILED_MSG.format( path=self._local_tuner.get_experiment_checkpoint_dir() ) ) from e else: experiment_checkpoint_dir = ray.get( self._remote_tuner.get_experiment_checkpoint_dir.remote() ) ( progress_reporter, string_queue, ) = self._prepare_remote_tuner_for_jupyter_progress_reporting() try: fit_future = _stream_client_output( fit_future, progress_reporter, string_queue, ) return ray.get(fit_future) except TuneError as e: raise TuneError( _TUNER_FAILED_MSG.format(path=experiment_checkpoint_dir) ) from e
[docs] def get_results(self) -> ResultGrid: """Get results of a hyperparameter tuning run. This method returns the same results as :meth:`fit() <>` and can be used to retrieve the results after restoring a tuner without calling ``fit()`` again. If the tuner has not been fit before, an error will be raised. .. code-block:: python from ray.tune import Tuner # `trainable` is what was passed in to the original `Tuner` tuner = Tuner.restore("/path/to/experiment', trainable=trainable) results = tuner.get_results() Returns: Result grid of a previously fitted tuning run. """ if not self._is_ray_client: return self._local_tuner.get_results() else: ( progress_reporter, string_queue, ) = self._prepare_remote_tuner_for_jupyter_progress_reporting() get_results_future = self._remote_tuner.get_results.remote() _stream_client_output( get_results_future, progress_reporter, string_queue, ) return ray.get(get_results_future)