Source code for ray.tune.tune

import abc
import contextlib
import copy
import datetime
import logging
import os
from pathlib import Path
import signal
import sys
import threading
import time
import warnings
from typing import (

import ray
from ray.air import CheckpointConfig
from ray.air.util.node import _force_on_current_node
from ray.tune.analysis import ExperimentAnalysis
from ray.tune.callback import Callback
from ray.tune.error import TuneError
from ray.tune.execution.tune_controller import TuneController
from ray.tune.experiment import Experiment, _convert_to_experiment_list
from ray.tune.experimental.output import (
    _detect_reporter as _detect_air_reporter,

from ray.tune.impl.placeholder import create_resolvers_map, inject_placeholders
from ray.tune.progress_reporter import (
from ray.tune.execution.ray_trial_executor import RayTrialExecutor
from ray.tune.registry import get_trainable_cls, is_function_trainable

# Must come last to avoid circular imports
from ray.tune.schedulers import (
from ray.tune.schedulers.util import (
    _set_search_properties_backwards_compatible as scheduler_set_search_props,
from ray.tune.stopper import Stopper
from import (
from import (
    _set_search_properties_backwards_compatible as searcher_set_search_props,
from import _has_unresolved_values
from ray.tune.syncer import SyncConfig
from ray.tune.trainable import Trainable
from ray.tune.experiment import Trial
from ray.tune.execution.trial_runner import TrialRunner
from ray.tune.utils.callback import _create_default_callbacks
from ray.tune.utils.log import (
from ray.tune.execution.placement_groups import PlacementGroupFactory
from ray.util.annotations import PublicAPI
from ray.util.queue import Queue

    from ray.tune.experimental.output import ProgressReporter as AirProgressReporter

logger = logging.getLogger(__name__)

def _get_trainable(
    run_identifier: Union[Experiment, str, Type, Callable]
) -> Optional[Type[Trainable]]:
    if isinstance(run_identifier, Experiment):
        run_identifier = run_identifier.run_identifier

    if isinstance(run_identifier, type):
        if not issubclass(run_identifier, Trainable):
            # If obscure dtype, assume it is overridden.
            return None
        trainable_cls = run_identifier
    elif callable(run_identifier):
        trainable_cls = run_identifier
    elif isinstance(run_identifier, str):
        trainable_cls = get_trainable_cls(run_identifier)
        return None

    return trainable_cls

def _check_default_resources_override(
    run_identifier: Union[Experiment, str, Type, Callable]
) -> bool:
    trainable_cls = _get_trainable(run_identifier)
    if not trainable_cls:
        # Default to True
        return True

    return hasattr(trainable_cls, "default_resource_request") and (
        != Trainable.default_resource_request.__code__

def _check_mixin(run_identifier: Union[Experiment, str, Type, Callable]) -> bool:
    trainable_cls = _get_trainable(run_identifier)
    if not trainable_cls:
        # Default to True
        return True

    return hasattr(trainable_cls, "__mixins__") or getattr(
        trainable_cls, "_is_mixin", False

def _check_gpus_in_resources(
    resources: Optional[Union[Dict, PlacementGroupFactory]]
) -> bool:
    if not resources:
        return False

    if isinstance(resources, PlacementGroupFactory):
        return bool(resources.required_resources.get("GPU", None))

    if isinstance(resources, dict):
        return bool(resources.get("gpu", None))

def _report_progress(
    runner: TrialRunner, reporter: ProgressReporter, done: bool = False
    """Reports experiment progress.

        runner: Trial runner to report on.
        reporter: Progress reporter.
        done: Whether this is the last progress report attempt.
    trials = runner.get_trials()
    if reporter.should_report(trials, done=done):
        sched_debug_str = runner.scheduler_alg.debug_string()
        used_resources_str = runner._used_resources_string(), done, sched_debug_str, used_resources_str)

def _report_air_progress(
    runner: TrialRunner, reporter: "AirProgressReporter", force: bool = False
    trials = runner.get_trials()
    reporter_args = []
    used_resources_string = runner._used_resources_string()
    reporter.print_heartbeat(trials, *reporter_args, force=force)

def _setup_signal_catching() -> threading.Event:
    original_handler = signal.getsignal(signal.SIGINT)
    experiment_interrupted_event = threading.Event()

    def signal_interrupt_tune_run(sig: int, frame):
            "Stop signal received (e.g. via SIGINT/Ctrl+C), ending Ray Tune run. "
            "This will try to checkpoint the experiment state one last time. "
            "Press CTRL+C (or send SIGINT/SIGKILL/SIGTERM) "
            "to skip. "
        # Restore original signal handler to react to future SIGINT signals
        signal.signal(signal.SIGINT, original_handler)

    # We should only install the handler when it is safe to do so.
    # When is called from worker thread, signal.signal will
    # fail.
    allow_signal_catching = True
    if threading.current_thread() != threading.main_thread():
        allow_signal_catching = False

    if allow_signal_catching:
        if not int(os.getenv("TUNE_DISABLE_SIGINT_HANDLER", "0")):
            signal.signal(signal.SIGINT, signal_interrupt_tune_run)

        # Always register SIGUSR1 if available (not available e.g. on Windows)
        if hasattr(signal, "SIGUSR1"):
            signal.signal(signal.SIGUSR1, signal_interrupt_tune_run)

    return experiment_interrupted_event

def _ray_auto_init(entrypoint: str):
    """Initialize Ray unless already configured."""
    if os.environ.get("TUNE_DISABLE_AUTO_INIT") == "1":"'TUNE_DISABLE_AUTO_INIT=1' detected.")
    elif not ray.is_initialized():
            "Initializing Ray automatically. "
            "For cluster usage or custom Ray initialization, "
            f"call `ray.init(...)` before `{entrypoint}`."

class _Config(abc.ABC):
    def to_dict(self) -> dict:
        """Converts this configuration to a dict format."""
        raise NotImplementedError

def run(
    run_or_experiment: Union[str, Callable, Type],
    name: Optional[str] = None,
    metric: Optional[str] = None,
    mode: Optional[str] = None,
    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,
    local_dir: Optional[str] = None,
    search_alg: Optional[Union[Searcher, SearchAlgorithm, str]] = None,
    scheduler: Optional[Union[TrialScheduler, str]] = None,
    keep_checkpoints_num: Optional[int] = None,
    checkpoint_score_attr: Optional[str] = None,
    checkpoint_freq: int = 0,
    checkpoint_at_end: bool = False,
    verbose: Union[int, Verbosity] = Verbosity.V3_TRIAL_DETAILS,
    progress_reporter: Optional[ProgressReporter] = None,
    log_to_file: bool = False,
    trial_name_creator: Optional[Callable[[Trial], str]] = None,
    trial_dirname_creator: Optional[Callable[[Trial], str]] = None,
    chdir_to_trial_dir: bool = True,
    sync_config: Optional[SyncConfig] = None,
    export_formats: Optional[Sequence] = None,
    max_failures: int = 0,
    fail_fast: bool = False,
    restore: Optional[str] = None,
    server_port: Optional[int] = None,
    resume: Union[bool, str] = False,
    reuse_actors: Optional[bool] = None,
    raise_on_failed_trial: bool = True,
    callbacks: Optional[Sequence[Callback]] = None,
    max_concurrent_trials: Optional[int] = None,
    # Deprecated
    trial_executor: Optional[RayTrialExecutor] = None,
    # == internal only ==
    _experiment_checkpoint_dir: Optional[str] = None,
    _remote: Optional[bool] = None,
    # Passed by the Tuner.
    _remote_string_queue: Optional[Queue] = None,
    _tuner_api: bool = False,
) -> ExperimentAnalysis:
    """Executes training.

    When a SIGINT signal is received (e.g. through Ctrl+C), the tuning run
    will gracefully shut down and checkpoint the latest experiment state.
    Sending SIGINT again (or SIGKILL/SIGTERM instead) will skip this step.

    Many aspects of Tune, such as the frequency of global checkpointing,
    maximum pending placement group trials and the path of the result
    directory be configured through environment variables. Refer to
    :ref:`tune-env-vars` for a list of environment variables available.


    .. code-block:: python

        # Run 10 trials (each trial is one instance of a Trainable). Tune runs
        # in parallel and automatically determines concurrency., num_samples=10)

        # Run 1 trial, stop when trial has reached 10 iterations, stop={"training_iteration": 10})

        # automatically retry failed trials up to 3 times, stop={"training_iteration": 10}, max_failures=3)

        # Run 1 trial, search over hyperparameters, stop after 10 iterations.
        space = {"lr": tune.uniform(0, 1), "momentum": tune.uniform(0, 1)}, config=space, stop={"training_iteration": 10})

        # Resumes training if a previous machine crashed, config=space,
                 local_dir=<path/to/dir>, resume=True)

        # Rerun ONLY failed trials after an experiment is finished., config=space,
                 local_dir=<path/to/dir>, resume="ERRORED_ONLY")

        run_or_experiment: If function|class|str, this is the algorithm or
            model to train. This may refer to the name of a built-on algorithm
            (e.g. RLlib's DQN or PPO), a user-defined trainable
            function or class, or the string identifier of a
            trainable function or class registered in the tune registry.
            If Experiment, then Tune will execute training based on
            Experiment.spec. If you want to pass in a Python lambda, you
            will need to first register the function:
            ``tune.register_trainable("lambda_id", lambda x: ...)``. You can
            then use ``"lambda_id")``.
        metric: Metric to optimize. This metric should be reported
            with ``. If set, will be passed to the search
            algorithm and scheduler.
        mode: Must be one of [min, max]. Determines whether objective is
            minimizing or maximizing the metric attribute. If set, will be
            passed to the search algorithm and scheduler.
        name: Name of experiment.
        stop: Stopping criteria. If dict,
            the keys may be any field in the return result of 'train()',
            whichever is reached first. If function, it must take (trial_id,
            result) as arguments and return a boolean (True if trial should be
            stopped, False otherwise). This can also be a subclass of
            ``ray.tune.Stopper``, which allows users to implement
            custom experiment-wide stopping (i.e., stopping an entire Tune
            run based on some time constraint).
        time_budget_s: Global time budget in
            seconds after which all trials are stopped. Can also be a
            ``datetime.timedelta`` object.
        config: Algorithm-specific configuration for Tune variant
            generation (e.g. env, hyperparams). Defaults to empty dict.
            Custom search algorithms may ignore this.
        resources_per_trial: Machine resources
            to allocate per trial, e.g. ``{"cpu": 64, "gpu": 8}``.
            Note that GPUs will not be assigned unless you specify them here.
            Defaults to 1 CPU and 0 GPUs in
            ``Trainable.default_resource_request()``. This can also
            be a PlacementGroupFactory object wrapping arguments to create a
            per-trial placement group.
        num_samples: Number of times to sample from the
            hyperparameter space. Defaults to 1. If `grid_search` is
            provided as an argument, the grid will be repeated
            `num_samples` of times. If this is -1, (virtually) infinite
            samples are generated until a stopping condition is met.
        local_dir: Local dir to save training results to.
            Defaults to ``~/ray_results``.
        search_alg: Search algorithm for
            optimization. You can also use the name of the algorithm.
        scheduler: Scheduler for executing
            the experiment. Choose among FIFO (default), MedianStopping,
            AsyncHyperBand, HyperBand and PopulationBasedTraining. Refer to
            ray.tune.schedulers for more options. You can also use the
            name of the scheduler.
        keep_checkpoints_num: Number of checkpoints to keep. A value of
            `None` keeps all checkpoints. Defaults to `None`. If set, need
            to provide `checkpoint_score_attr`.
        checkpoint_score_attr: Specifies by which attribute to rank the
            best checkpoint. Default is increasing order. If attribute starts
            with `min-` it will rank attribute in decreasing order, i.e.
        checkpoint_freq: How many training iterations between
            checkpoints. A value of 0 (default) disables checkpointing.
            This has no effect when using the Functional Training API.
        checkpoint_at_end: Whether to checkpoint at the end of the
            experiment regardless of the checkpoint_freq. Default is False.
            This has no effect when using the Functional Training API.
        verbose: 0, 1, 2, or 3. Verbosity mode.
            0 = silent, 1 = only status updates, 2 = status and brief trial
            results, 3 = status and detailed trial results. Defaults to 3.
        progress_reporter: Progress reporter for reporting
            intermediate experiment progress. Defaults to CLIReporter if
            running in command-line, or JupyterNotebookReporter if running in
            a Jupyter notebook.
        log_to_file: Log stdout and stderr to files in
            Tune's trial directories. If this is `False` (default), no files
            are written. If `true`, outputs are written to `trialdir/stdout`
            and `trialdir/stderr`, respectively. If this is a single string,
            this is interpreted as a file relative to the trialdir, to which
            both streams are written. If this is a Sequence (e.g. a Tuple),
            it has to have length 2 and the elements indicate the files to
            which stdout and stderr are written, respectively.
        trial_name_creator: Optional function that takes in a Trial and returns
            its name (i.e. its string representation). Be sure to include some unique
            identifier (such as `Trial.trial_id`) in each trial's name.
        trial_dirname_creator: Optional function that takes in a trial and
            generates its trial directory name as a string. Be sure to include some
            unique identifier (such as `Trial.trial_id`) is used in each trial's
            directory name. Otherwise, trials could overwrite artifacts and checkpoints
            of other trials. The return value cannot be a path.
        chdir_to_trial_dir: Whether to change the working directory of each worker
            to its corresponding trial directory. Defaults to `True` to prevent
            contention between workers saving trial-level outputs.
            If set to `False`, files are accessible with paths relative to the
            original working directory. However, all workers on the same node now
            share the same working directory, so be sure to use
            `session.get_trial_dir()` as the path to save any outputs.
        sync_config: Configuration object for syncing. See
        export_formats: List of formats that exported at the end of
            the experiment. Default is None.
        max_failures: Try to recover a trial at least this many times.
            Ray will recover from the latest checkpoint if present.
            Setting to -1 will lead to infinite recovery retries.
            Setting to 0 will disable retries. Defaults to 0.
        fail_fast: Whether to fail upon the first error.
            If fail_fast='raise' provided, Tune will automatically
            raise the exception received by the Trainable. fail_fast='raise'
            can easily leak resources and should be used with caution (it
            is best used with `ray.init(local_mode=True)`).
        restore: Path to checkpoint. Only makes sense to set if
            running 1 trial. Defaults to None.
        server_port: Port number for launching TuneServer.
        resume: One of [True, False, "LOCAL", "REMOTE", "PROMPT", "AUTO"]. Can
            be suffixed with one or more of ["+ERRORED", "+ERRORED_ONLY",
            "LOCAL"/True restores the checkpoint from the
            local experiment directory, determined
            by ``name`` and ``local_dir``.
            "REMOTE" restores the checkpoint
            from ``upload_dir`` (as passed to ``sync_config``).
            "PROMPT" provides the CLI feedback.
            False forces a new experiment.
            "AUTO" will attempt to resume from a checkpoint and otherwise
            start a new experiment.
            The suffix "+ERRORED" resets and reruns errored trials upon resume -
            previous trial artifacts will be left untouched. It will try to continue
            from the last observed checkpoint.
            The suffix "+RESTART_ERRORED" will instead start the errored trials from
            scratch. "+ERRORED_ONLY" and "+RESTART_ERRORED_ONLY" will disable
            resuming non-errored trials - they will be added as finished instead. New
            trials can still be generated by the search algorithm.
            If resume is set but checkpoint does not exist,
            ValueError will be thrown.
        reuse_actors: Whether to reuse actors between different trials
            when possible. This can drastically speed up experiments that start
            and stop actors often (e.g., PBT in time-multiplexing mode). This
            requires trials to have the same resource requirements.
            Defaults to ``True`` for function trainables and ``False`` for
            class and registered trainables.
        raise_on_failed_trial: Raise TuneError if there exists failed
            trial (of ERROR state) when the experiments complete.
        callbacks: List of callbacks that will be called at different
            times in the training loop. Must be instances of the
            ``ray.tune.callback.Callback`` class. If not passed,
            `LoggerCallback` and `SyncerCallback` callbacks are automatically
        max_concurrent_trials: Maximum number of trials to run
            concurrently. Must be non-negative. If None or 0, no limit will
            be applied. This is achieved by wrapping the ``search_alg`` in
            a :class:`ConcurrencyLimiter`, and thus setting this argument
            will raise an exception if the ``search_alg`` is already a
            :class:`ConcurrencyLimiter`. Defaults to None.
        _remote: Whether to run the Tune driver in a remote function.
            This is disabled automatically if a custom trial executor is
            passed in. This is enabled by default in Ray client mode.

        ExperimentAnalysis: Object for experiment analysis.

        TuneError: Any trials failed and `raise_on_failed_trial` is True.
    # remote_run_kwargs must be defined before any other
    # code is ran to ensure that at this point,
    # `locals()` is equal to args and kwargs
    remote_run_kwargs = locals().copy()

    error_message_map = (
            "entrypoint": "Tuner(...)",
            "search_space_arg": "param_space",
            "restore_entrypoint": 'Tuner.restore(path="{path}", trainable=...)',
        if _tuner_api
        else {
            "entrypoint": "",
            "search_space_arg": "config",
            "restore_entrypoint": ", resume=True)",

    if _remote is None:
        _remote = ray.util.client.ray.is_connected()

    if _remote is True and trial_executor:
        raise ValueError("cannot use custom trial executor")
    elif trial_executor:
            "Passing a custom `trial_executor` is deprecated and will be removed "
            "in the future.",

    if _remote:
        if get_air_verbosity():
                "Ignoring AIR_VERBOSITY setting, "
                "as it doesn't support ray client mode yet."

        remote_run = ray.remote(num_cpus=0)(run)

        # Make sure is called on the sever node.
        remote_run = _force_on_current_node(remote_run)

        progress_reporter, string_queue = _prepare_progress_reporter_for_ray_client(
            progress_reporter, verbose, _remote_string_queue

        # Override with detected progress reporter
        remote_run_kwargs["progress_reporter"] = progress_reporter

        remote_future = remote_run.remote(_remote=False, **remote_run_kwargs)

        return ray.get(remote_future)

    del remote_run_kwargs


    all_start = time.time()

    if mode and mode not in ["min", "max"]:
        raise ValueError(
            f"The `mode` parameter passed to `{error_message_map['entrypoint']}` "
            "must be one of ['min', 'max']"

    air_verbosity = get_air_verbosity()
    if air_verbosity:
            f"Testing new AIR console output flow with verbosity={air_verbosity}. "
            f"This will also disable the old flow - setting it to 0 now."

    config = config or {}
    if isinstance(config, _Config):
        config = config.to_dict()
    if not isinstance(config, dict):
        raise ValueError(
            f"The `{error_message_map['search_space_arg']}` passed to "
            f"`{error_message_map['entrypoint']}` must be a dict. "
            f"Got '{type(config)}' instead."

    sync_config = sync_config or SyncConfig()

    checkpoint_score_attr = checkpoint_score_attr or ""
    if checkpoint_score_attr.startswith("min-"):
        checkpoint_score_attr = checkpoint_score_attr[4:]
        checkpoint_score_order = "min"
        checkpoint_score_order = "max"

    checkpoint_config = CheckpointConfig(

    if num_samples == -1:
        num_samples = sys.maxsize

    result_buffer_length = None

    # Create scheduler here as we need access to some of its properties
    if isinstance(scheduler, str):
        # importing at top level causes a recursive dependency
        from ray.tune.schedulers import create_scheduler

        scheduler = create_scheduler(scheduler)
    scheduler = scheduler or FIFOScheduler()

    if not scheduler.supports_buffered_results:
        # Result buffering with e.g. a Hyperband scheduler is a bad idea, as
        # hyperband tries to stop trials when processing brackets. With result
        # buffering, we might trigger this multiple times when evaluating
        # a single trial, which leads to unexpected behavior.
        env_result_buffer_length = os.getenv("TUNE_RESULT_BUFFER_LENGTH", "")
        if env_result_buffer_length:
                f"You are using a {type(scheduler)} scheduler, but "
                f"TUNE_RESULT_BUFFER_LENGTH is set "
                f"({env_result_buffer_length}). This can lead to undesired "
                f"and faulty behavior, so the buffer length was forcibly set "
                f"to 1 instead."
        result_buffer_length = 1

    # If reuse_actors is unset, default to False for string and class trainables,
    # and default to True for everything else (i.e. function trainables)
    if reuse_actors is None:
        trainable = (
            if isinstance(run_or_experiment, Experiment)
            else run_or_experiment
        reuse_actors = (
            # Only default to True for function trainables that meet certain conditions
            and not (
                # Changing resources requires restarting actors
                and isinstance(scheduler, ResourceChangingScheduler)
            and not (
                # If GPUs are requested we could run into problems with device memory
            and not (
                # If the resource request is overridden, we don't know if GPUs
                # will be requested, yet, so default to False
            and not (
                # Mixins do not work with reuse_actors as the mixin setup will only
                # be invoked once
        logger.debug(f"Auto-detected `reuse_actors={reuse_actors}`")

    if (
        isinstance(scheduler, (PopulationBasedTraining, PopulationBasedTrainingReplay))
        and not reuse_actors
            "Consider boosting PBT performance by enabling `reuse_actors` as "
            "well as implementing `reset_config` for Trainable."

    # Before experiments are created, we first clean up the passed in
    # Config dictionary by replacing all the non-primitive config values
    # with placeholders. This serves two purposes:
    # 1. we can replace and "fix" these objects if a Trial is restored.
    # 2. the config dictionary will then be compatible with all supported
    #   search algorithms, since a lot of them do not support non-primitive
    #   config values.
    placeholder_resolvers = create_resolvers_map()
    config = inject_placeholders(
        # Make a deep copy here to avoid modifying the original config dict.

    if isinstance(run_or_experiment, list):
        experiments = run_or_experiment
        experiments = [run_or_experiment]

    for i, exp in enumerate(experiments):
        if not isinstance(exp, Experiment):
            experiments[i] = Experiment(

    if fail_fast and max_failures != 0:
        raise ValueError("max_failures must be 0 if fail_fast=True.")

    if isinstance(search_alg, str):
        search_alg = create_searcher(search_alg)

    # if local_mode=True is set during ray.init().
    is_local_mode = ray._private.worker._mode() == ray._private.worker.LOCAL_MODE

    if is_local_mode:
        max_concurrent_trials = 1

    if not search_alg:
        search_alg = BasicVariantGenerator(max_concurrent=max_concurrent_trials or 0)
    elif max_concurrent_trials or is_local_mode:
        if isinstance(search_alg, ConcurrencyLimiter):
            if not is_local_mode:
                if search_alg.max_concurrent != max_concurrent_trials:
                    raise ValueError(
                        "You have specified `max_concurrent_trials="
                        f"{max_concurrent_trials}`, but the `search_alg` is "
                        "already a `ConcurrencyLimiter` with `max_concurrent="
                        f"{search_alg.max_concurrent}. FIX THIS by setting "
                        "You have specified `max_concurrent_trials="
                        f"{max_concurrent_trials}`, but the `search_alg` is "
                        "already a `ConcurrencyLimiter`. "
                        "`max_concurrent_trials` will be ignored."
            if max_concurrent_trials < 1:
                raise ValueError(
                    "`max_concurrent_trials` must be greater or equal than 1, "
                    f"got {max_concurrent_trials}."
            if isinstance(search_alg, Searcher):
                search_alg = ConcurrencyLimiter(
                    search_alg, max_concurrent=max_concurrent_trials
            elif not is_local_mode:
                    "You have passed a `SearchGenerator` instance as the "
                    "`search_alg`, but `max_concurrent_trials` requires a "
                    "`Searcher` instance`. `max_concurrent_trials` "
                    "will be ignored."

    if isinstance(search_alg, Searcher):
        search_alg = SearchGenerator(search_alg)

    if config and not searcher_set_search_props(
        if _has_unresolved_values(config):
            raise ValueError(
                f"You passed a `{error_message_map['search_space_arg']}` parameter to "
                f"`{error_message_map['entrypoint']}` with "
                "unresolved parameters, but the search algorithm was already "
                "instantiated with a search space. Make sure that `config` "
                "does not contain any more parameter definitions - include "
                "them in the search algorithm's search space if necessary."

    if not scheduler_set_search_props(
        scheduler.set_search_properties, metric, mode, **experiments[0].public_spec
        raise ValueError(
            "You passed a `metric` or `mode` argument to "
            f"`{error_message_map['entrypoint']}`, but "
            "the scheduler you are using was already instantiated with their "
            "own `metric` and `mode` parameters. Either remove the arguments "
            f"from your scheduler or from `{error_message_map['entrypoint']}` args."

    progress_metrics = _detect_progress_metrics(_get_trainable(run_or_experiment))

    # Create syncer callbacks
    callbacks = _create_default_callbacks(

    # User Warning for GPUs
    if ray.cluster_resources().get("GPU", 0):
        if _check_gpus_in_resources(resources=resources_per_trial):
            # "gpu" is manually set.
        elif _check_default_resources_override(experiments[0].run_identifier):
            # "default_resources" is manually overridden.
                "Tune detects GPUs, but no trials are using GPUs. "
                "To enable trials to use GPUs, wrap `train_func` with "
                "`tune.with_resources(train_func, resources_per_trial={'gpu': 1})` "
                "which allows Tune to expose 1 GPU to each trial. "
                "For Ray AIR Trainers, you can specify GPU resources "
                "through `ScalingConfig(use_gpu=True)`. "
                "You can also override "
                "`Trainable.default_resource_request` if using the "
                "Trainable API."

    experiment_interrupted_event = _setup_signal_catching()

    if progress_reporter and air_verbosity:
            "AIR_VERBOSITY is set, ignoring passed-in ProgressReporter for now."
        progress_reporter = None

    if not air_verbosity:
        progress_reporter = progress_reporter or _detect_reporter()

    trial_executor = trial_executor or RayTrialExecutor(
    runner_kwargs = dict(

    if bool(int(os.environ.get("TUNE_NEW_EXECUTION", "0"))):
        trial_runner_cls = TuneController
        runner_kwargs["reuse_actors"] = reuse_actors
        runner_kwargs["chdir_to_trial_dir"] = chdir_to_trial_dir
        trial_runner_cls = TrialRunner

    runner = trial_runner_cls(**runner_kwargs)

    if not runner.resumed:
        for exp in experiments:
            "You have resumed the Tune run, which means that any newly specified "
            "`Experiment`s will be ignored. "
            "Tune will just continue what was previously running."
        if resources_per_trial:

    # Calls setup on callbacks
        experiments=experiments, total_num_samples=search_alg.total_samples

    tune_start = time.time()

    air_progress_reporter = None
    if not air_verbosity:
        air_progress_reporter = _detect_air_reporter(
            air_verbosity, search_alg.total_samples, metric=metric, mode=mode

    # rich live context manager has to be called encapsulting
    # the while loop. For other kind of reporters, no op.
    # `ExitStack` allows us to *conditionally* apply context manager.
    with contextlib.ExitStack() as stack:
        from ray.tune.experimental.output import TuneRichReporter

        if air_progress_reporter and isinstance(
            air_progress_reporter, TuneRichReporter

            while (
                not runner.is_finished() and not experiment_interrupted_event.is_set()
                if has_verbosity(Verbosity.V1_EXPERIMENT):
                    _report_progress(runner, progress_reporter)

                if air_verbosity:
                    _report_air_progress(runner, air_progress_reporter)
        except Exception:

        tune_taken = time.time() - tune_start

            runner.checkpoint(force=True, wait=True)
        except Exception as e:
            logger.warning(f"Trial Runner checkpointing failed: {str(e)}")

        if has_verbosity(Verbosity.V1_EXPERIMENT):
            _report_progress(runner, progress_reporter, done=True)

        if air_verbosity:
            _report_air_progress(runner, air_progress_reporter, force=True)

    all_trials = runner.get_trials()
    experiment_checkpoint = runner.experiment_state_path


    incomplete_trials = []
    for trial in all_trials:
        if trial.status != Trial.TERMINATED:
            incomplete_trials += [trial]

    if incomplete_trials:
        if raise_on_failed_trial and not experiment_interrupted_event.is_set():
            raise TuneError("Trials did not complete", incomplete_trials)
            logger.error("Trials did not complete: %s", incomplete_trials)

    all_taken = time.time() - all_start
    if has_verbosity(Verbosity.V1_EXPERIMENT):
            f"Total run time: {all_taken:.2f} seconds "
            f"({tune_taken:.2f} seconds for the tuning loop)."

    if experiment_interrupted_event.is_set():
        restore_entrypoint = error_message_map["restore_entrypoint"].format(
            "Experiment has been interrupted, but the most recent state was saved.\n"
            f"Continue running this experiment with: {restore_entrypoint}"
    ea = ExperimentAnalysis(

    return ea

[docs]@PublicAPI def run_experiments( experiments: Union[Experiment, Mapping, Sequence[Union[Experiment, Mapping]]], scheduler: Optional[TrialScheduler] = None, server_port: Optional[int] = None, verbose: Union[int, Verbosity] = Verbosity.V3_TRIAL_DETAILS, progress_reporter: Optional[ProgressReporter] = None, resume: Union[bool, str] = False, reuse_actors: Optional[bool] = None, trial_executor: Optional[RayTrialExecutor] = None, raise_on_failed_trial: bool = True, concurrent: bool = True, callbacks: Optional[Sequence[Callback]] = None, _remote: Optional[bool] = None, ): """Runs and blocks until all trials finish. Example: >>> from ray.tune.experiment import Experiment >>> from ray.tune.tune import run_experiments >>> def my_func(config): return {"score": 0} >>> experiment_spec = Experiment("experiment", my_func) # doctest: +SKIP >>> run_experiments(experiments=experiment_spec) # doctest: +SKIP >>> experiment_spec = {"experiment": {"run": my_func}} # doctest: +SKIP >>> run_experiments(experiments=experiment_spec) # doctest: +SKIP Returns: List of Trial objects, holding data for each executed trial. """ if _remote is None: _remote = ray.util.client.ray.is_connected() if _remote is True and trial_executor: raise ValueError("cannot use custom trial executor") if not trial_executor or isinstance(trial_executor, RayTrialExecutor): _ray_auto_init(entrypoint="tune.run_experiments(...)") if _remote: if get_air_verbosity(): logger.warning( "Ignoring AIR_VERBOSITY setting, " "as it doesn't support ray client mode yet." ) remote_run = ray.remote(num_cpus=0)(run_experiments) # Make sure tune.run_experiments is run on the server node. remote_run = _force_on_current_node(remote_run) return ray.get( remote_run.remote( experiments, scheduler, server_port, verbose, progress_reporter, resume, reuse_actors, trial_executor, raise_on_failed_trial, concurrent, callbacks, _remote=False, ) ) # This is important to do this here # because it schematize the experiments # and it conducts the implicit registration. experiments = _convert_to_experiment_list(experiments) if concurrent: return run( experiments, server_port=server_port, verbose=verbose, progress_reporter=progress_reporter, resume=resume, reuse_actors=reuse_actors, trial_executor=trial_executor, raise_on_failed_trial=raise_on_failed_trial, scheduler=scheduler, callbacks=callbacks, ).trials else: trials = [] for exp in experiments: trials += run( exp, server_port=server_port, verbose=verbose, progress_reporter=progress_reporter, resume=resume, reuse_actors=reuse_actors, trial_executor=trial_executor, raise_on_failed_trial=raise_on_failed_trial, scheduler=scheduler, callbacks=callbacks, ).trials return trials