Source code for ray.tune.ray_trial_executor

# coding: utf-8
import copy
from functools import partial
import logging
import os
import random
import time
import traceback
from contextlib import contextmanager
from typing import Iterable, Optional

import ray
from import ActorHandle
from ray.exceptions import GetTimeoutError
from ray import ray_constants
from ray.resource_spec import ResourceSpec
from ray.tune.durable_trainable import DurableTrainable
from ray.tune.error import AbortTrialExecution, TuneError
from ray.tune.logger import NoopLogger
from ray.tune.result import TRIAL_INFO, STDOUT_FILE, STDERR_FILE
from ray.tune.resources import Resources
from ray.tune.utils.placement_groups import PlacementGroupManager, \
from ray.tune.utils.trainable import TrainableUtil
from ray.tune.trial import Trial, Checkpoint, Location, TrialInfo
from ray.tune.trial_executor import TrialExecutor
from ray.tune.utils import warn_if_slow
from ray.util import log_once

logger = logging.getLogger(__name__)

TUNE_STATE_REFRESH_PERIOD = 10  # Refresh resources every 10 s
DEFAULT_GET_TIMEOUT = 60.0  # seconds

class _ActorClassCache:
    """Caches actor classes.

    ray.remote is a registration call. It sends the serialized object to the
    key value store (redis), and will be fetched at an arbitrary worker
    later. Registration does not use any Ray scheduling resources.

    Later, class.remote() actually creates the remote actor. The
    actor will be instantiated on some arbitrary machine,
    according to the underlying Ray scheduler.

    Without this cache, you would register the same serialized object
    over and over again. Naturally, since redis doesn’t spill to disk,
    this can easily nuke the redis instance (and basically blow up Ray).
    This cache instead allows us to register once and only once.

    Note that we assume there can be multiple trainables in the
    system at once.

    def __init__(self):
        self._cache = {}

    def get(self, trainable_cls):
        """Gets the wrapped trainable_cls, otherwise calls ray.remote."""
        if trainable_cls not in self._cache:
            remote_cls = ray.remote(trainable_cls)
            self._cache[trainable_cls] = remote_cls
        return self._cache[trainable_cls]

_class_cache = _ActorClassCache()

class _LocalWrapper:
    def __init__(self, result):
        self._result = result

    def unwrap(self):
        """Returns the wrapped result."""
        return self._result

class _TrialCleanup:
    """Mechanism for ensuring trial stop futures are cleaned up.

        threshold (int): Number of futures to hold at once. If the threshold
            is passed, cleanup will kick in and remove futures.

    def __init__(self, threshold: int = TRIAL_CLEANUP_THRESHOLD):
        self.threshold = threshold
        self._cleanup_map = {}

    def add(self, trial: Trial, actor: ActorHandle):
        """Adds a trial actor to be stopped.

        If the number of futures exceeds the threshold, the cleanup mechanism
        will kick in.

            trial (Trial): The trial corresponding to the future.
            actor (ActorHandle): Handle to the trainable to be stopped.
        future = actor.stop.remote()

        del actor

        self._cleanup_map[future] = trial
        if len(self._cleanup_map) > self.threshold:

    def cleanup(self, partial: bool = True):
        """Waits for cleanup to finish.

        If partial=False, all futures are expected to return. If a future
        does not return within the timeout period, the cleanup terminates.
        logger.debug("Cleaning up futures")
        num_to_keep = int(self.threshold) / 2 if partial else 0
        while len(self._cleanup_map) > num_to_keep:
            dones, _ = ray.wait(
                list(self._cleanup_map), timeout=DEFAULT_GET_TIMEOUT)
            if not dones:
                    "Skipping cleanup - trainable.stop did not return in "
                    "time. Consider making `stop` a faster operation.")
                done = dones[0]
                del self._cleanup_map[done]

def noop_logger_creator(config, logdir):
    # Set the working dir in the remote process, for user file writes
    os.makedirs(logdir, exist_ok=True)
    if not ray.worker._mode() == ray.worker.LOCAL_MODE:
    return NoopLogger(config, logdir)

[docs]class RayTrialExecutor(TrialExecutor): """An implementation of TrialExecutor based on Ray.""" def __init__(self, queue_trials: bool = False, reuse_actors: bool = False, result_buffer_length: Optional[int] = None, refresh_period: Optional[float] = None, wait_for_placement_group: Optional[float] = None): super(RayTrialExecutor, self).__init__(queue_trials) # Check for if we are launching a trial without resources in kick off # autoscaler. self._trial_queued = False self._running = {} # Since trial resume after paused should not run # trial.train.remote(), thus no more new remote object ref generated. # We use self._paused to store paused trials here. self._paused = {} self._trial_cleanup = _TrialCleanup() self._has_cleaned_up_pgs = False self._reuse_actors = reuse_actors self._cached_actor_pg = (None, None) self._avail_resources = Resources(cpu=0, gpu=0) self._committed_resources = Resources(cpu=0, gpu=0) self._pg_manager = PlacementGroupManager(prefix=get_tune_pg_prefix()) self._staged_trials = set() self._just_staged_trials = set() self._trial_just_finished = False self._trial_just_finished_before = False self._resources_initialized = False if refresh_period is None: refresh_period = float( os.environ.get("TUNE_STATE_REFRESH_PERIOD", TUNE_STATE_REFRESH_PERIOD)) self._refresh_period = refresh_period self._wait_for_pg = wait_for_placement_group or float( os.environ.get("TUNE_PLACEMENT_GROUP_WAIT_S", "-1")) if self._wait_for_pg < 0: self._wait_for_pg = None self.last_pg_recon = 0 self.pg_recon_interval = float( os.environ.get("TUNE_PLACEMENT_GROUP_RECON_INTERVAL", "5")) self._default_buffer_length = result_buffer_length or int( os.getenv("TUNE_RESULT_BUFFER_LENGTH", 1000)) self._buffer_length = result_buffer_length self._buffer_min_time_s = float( os.getenv("TUNE_RESULT_BUFFER_MIN_TIME_S", 0.)) self._buffer_max_time_s = float( os.getenv("TUNE_RESULT_BUFFER_MAX_TIME_S", 100.)) self._last_resource_refresh = float("-inf") self._last_ip_refresh = float("-inf") self._last_ip_addresses = set() self._last_nontrivial_wait = time.time() if ray.is_initialized(): self._update_avail_resources()
[docs] def in_staging_grace_period(self) -> bool: """Returns True if trials have recently been staged.""" return self._pg_manager.in_staging_grace_period()
[docs] def set_max_pending_trials(self, max_pending: int): self._pg_manager.set_max_staging(max_pending)
[docs] def stage_and_update_status(self, trials: Iterable[Trial]): """Check and update statuses of scheduled placement groups. Stages placement groups of all trials. """ if not self._has_cleaned_up_pgs: # Clean up existing placement groups after trigger the tuning # run step() method for the first time self._pg_manager.cleanup_existing_pg() self._has_cleaned_up_pgs = True for trial in trials: if trial.status != Trial.PENDING: continue if not trial.uses_placement_groups: continue if trial in self._staged_trials: continue if self._pg_manager.trial_in_use(trial): continue if not self._pg_manager.stage_trial_pg(trial): # Break if we reached the limit of pending placement groups. break self._staged_trials.add(trial) self._just_staged_trials.add(trial) self._pg_manager.update_status()
[docs] def get_staged_trial(self): """Get a trial whose placement group was successfully staged. Can also return None if no trial is available. Returns: Trial object or None. """ for trial in self._staged_trials: if self._pg_manager.has_ready(trial): return trial return None
def _setup_remote_runner(self, trial): trial.init_logdir() # We checkpoint metadata here to try mitigating logdir duplication self.try_checkpoint_metadata(trial) logger_creator = partial(noop_logger_creator, logdir=trial.logdir) if self._reuse_actors and self._cached_actor_pg[0] is not None: logger.debug(f"Trial {trial}: Reusing cached runner " f"{self._cached_actor_pg[0]}") existing_runner, pg = self._cached_actor_pg self._cached_actor_pg = (None, None) trial.set_runner(existing_runner) if pg and trial.uses_placement_groups: self._pg_manager.assign_cached_pg(pg, trial) if not self.reset_trial(trial, trial.config, trial.experiment_tag, logger_creator): raise AbortTrialExecution( "Trainable runner reuse requires reset_config() to be " "implemented and return True.") return existing_runner if self._cached_actor_pg[0]: logger.debug("Cannot reuse cached runner {} for new trial".format( self._cached_actor_pg[0])) existing_runner, pg = self._cached_actor_pg if pg: self._pg_manager.return_or_clean_cached_pg(pg) with self._change_working_directory(trial): self._trial_cleanup.add(trial, actor=existing_runner) self._cached_actor_pg = (None, None) trainable_cls = trial.get_trainable_cls() if not trainable_cls: raise AbortTrialExecution( f"Invalid trainable: {trial.trainable_name}. If you passed " f"a string, make sure the trainable was registered before.") _actor_cls = _class_cache.get(trainable_cls) if trial.uses_placement_groups: if not self._pg_manager.has_ready(trial, update=True): if trial not in self._staged_trials: if self._pg_manager.stage_trial_pg(trial): self._staged_trials.add(trial) self._just_staged_trials.add(trial) just_staged = trial in self._just_staged_trials # This part of the code is mostly here for testing # purposes. If self._wait_for_pg is set, we will wait here # for that many seconds until the placement group is ready. # This ensures that the trial can be started right away and # not just in the next step() of the trial runner. # We only do this if we have reason to believe that resources # will be ready, soon, i.e. when a) we just staged the PG, # b) another trial just exited, freeing resources, or c) # when there are no currently running trials. if self._wait_for_pg is not None and ( just_staged or self._trial_just_finished_before or not self.get_running_trials()): logger.debug( f"Waiting up to {self._wait_for_pg} seconds for " f"placement group of trial {trial} to become ready.") wait_end = time.monotonic() + self._wait_for_pg while time.monotonic() < wait_end: self._pg_manager.update_status() if self._pg_manager.has_ready(trial): break time.sleep(0.1) else: return None if not self._pg_manager.has_ready(trial): # PG may have become ready during waiting period return None full_actor_class = self._pg_manager.get_full_actor_cls( trial, _actor_cls) else: full_actor_class = _actor_cls.options( num_cpus=trial.resources.cpu, num_gpus=trial.resources.gpu, memory=trial.resources.memory or None, object_store_memory=trial.resources.object_store_memory or None, resources=trial.resources.custom_resources) # Clear the Trial's location (to be updated later on result) # since we don't know where the remote runner is placed. trial.set_location(Location()) logger.debug("Trial %s: Setting up new remote runner.", trial) # Logging for trials is handled centrally by TrialRunner, so # configure the remote runner to use a noop-logger. trial_config = copy.deepcopy(trial.config) trial_config[TRIAL_INFO] = TrialInfo(trial) stdout_file, stderr_file = trial.log_to_file trial_config[STDOUT_FILE] = stdout_file trial_config[STDERR_FILE] = stderr_file kwargs = { "config": trial_config, "logger_creator": logger_creator, } if issubclass(trial.get_trainable_cls(), DurableTrainable): kwargs["remote_checkpoint_dir"] = trial.remote_checkpoint_dir kwargs["sync_function_tpl"] = trial.sync_to_cloud with self._change_working_directory(trial): return full_actor_class.remote(**kwargs) def _train(self, trial): """Start one iteration of training and save remote id.""" if self._find_item(self._paused, trial): raise TuneError( "Should not call `train` on PAUSED trial {}. " "This is an internal error - please file an issue " "on".format( str(trial))) if self._find_item(self._running, trial): logging.debug( "Trial {} already has a queued future. Skipping this " "`train` call. This may occur if a trial has " "been unpaused within a scheduler callback.".format( str(trial))) return assert trial.status == Trial.RUNNING, trial.status buffer_time_s = max( self._buffer_min_time_s, min(self._buffer_max_time_s, len(self._running) // 10)) with self._change_working_directory(trial): buffer_length = self._buffer_length # If buffer length has not been explicitly set, we determine # it automatically if buffer_length is None: if trial.checkpoint_at_end: # If a trial checkpoint can be triggered externally, # it is not safe to buffer results. buffer_length = 1 else: # Else, use the default buffer length buffer_length = self._default_buffer_length else: if trial.checkpoint_at_end: if log_once("trial_executor_buffer_checkpoint"): logger.warning( "You passed `checkpoint_at_end` to ``, " "but still requested buffered training. " "If used with a custom stopper or early stopping, " "checkpoints may be created later than desired.") if buffer_length > 1: if trial.checkpoint_freq > 0: buffer_length = min(buffer_length, trial.checkpoint_freq) remote = trial.runner.train_buffered.remote( buffer_time_s, buffer_length) else: remote = trial.runner.train.remote() # Local Mode if isinstance(remote, dict): remote = _LocalWrapper(remote) self._running[remote] = trial trial_item = self._find_item(self._running, trial) assert len(trial_item) < 2, trial_item def _start_trial(self, trial, checkpoint=None, runner=None, train=True) -> bool: """Starts trial and restores last result if trial was paused. Args: trial (Trial): The trial to start. checkpoint (Optional[Checkpoint]): The checkpoint to restore from. If None, and no trial checkpoint exists, the trial is started from the beginning. runner (Trainable): The remote runner to use. This can be the cached actor. If None, a new runner is created. train (bool): Whether or not to start training. Returns: True if trial was started successfully, False otherwise. See `RayTrialExecutor.restore` for possible errors raised. """ prior_status = trial.status self.set_status(trial, Trial.PENDING) if runner is None: runner = self._setup_remote_runner(trial) if not runner: return False trial.set_runner(runner) self.restore(trial, checkpoint) self.set_status(trial, Trial.RUNNING) if trial in self._staged_trials: self._staged_trials.remove(trial) previous_run = self._find_item(self._paused, trial) if prior_status == Trial.PAUSED and previous_run: # If Trial was in flight when paused, self._paused stores result. self._paused.pop(previous_run[0]) self._running[previous_run[0]] = trial elif train and not trial.is_restoring: self._train(trial) return True def _stop_trial(self, trial, error=False, error_msg=None, destroy_pg_if_cannot_replace=True): """Stops this trial. Stops this trial, releasing all allocating resources. If stopping the trial fails, the run will be marked as terminated in error, but no exception will be thrown. If the placement group will be used right away (destroy_pg_if_cannot_replace=False), we do not remove its placement group (or a surrogate placement group). Args: error (bool): Whether to mark this trial as terminated in error. error_msg (str): Optional error message. """ self.set_status(trial, Trial.ERROR if error else Trial.TERMINATED) self._trial_just_finished = True trial.set_location(Location()) try: trial.write_error_log(error_msg) if hasattr(trial, "runner") and trial.runner: if (not error and self._reuse_actors and self._cached_actor_pg[0] is None): logger.debug("Reusing actor for %s", trial.runner) # Move PG into cache (disassociate from trial) pg = self._pg_manager.cache_trial_pg(trial) if pg or not trial.uses_placement_groups: # True if a placement group was replaced self._cached_actor_pg = (trial.runner, pg) should_destroy_actor = False else: # False if no placement group was replaced. This should # only be the case if there are no more trials with # this placement group factory to run logger.debug( "Could not cache of trial {trial} actor for " "reuse, as there are no pending trials " "requiring its resources.") should_destroy_actor = True else: should_destroy_actor = True if should_destroy_actor: logger.debug("Trial %s: Destroying actor.", trial) # Try to return the placement group for other trials to use self._pg_manager.return_pg(trial, destroy_pg_if_cannot_replace) with self._change_working_directory(trial): self._trial_cleanup.add(trial, actor=trial.runner) if trial in self._staged_trials: self._staged_trials.remove(trial) except Exception: logger.exception("Trial %s: Error stopping runner.", trial) self.set_status(trial, Trial.ERROR) finally: trial.set_runner(None)
[docs] def start_trial(self, trial, checkpoint=None, train=True) -> bool: """Starts the trial. Will not return resources if trial repeatedly fails on start. Args: trial (Trial): Trial to be started. checkpoint (Checkpoint): A Python object or path storing the state of trial. train (bool): Whether or not to start training. Returns: True if the remote runner has been started. False if trial was not started (e.g. because of lacking resources/pending PG). """ if not trial.uses_placement_groups: self._commit_resources(trial.resources) try: return self._start_trial(trial, checkpoint, train=train) except AbortTrialExecution: logger.exception("Trial %s: Error starting runner, aborting!", trial) time.sleep(2) error_msg = traceback.format_exc() self._stop_trial(trial, error=True, error_msg=error_msg) return False except Exception: logger.exception("Trial %s: Unexpected error starting runner.", trial) time.sleep(2) error_msg = traceback.format_exc() self._stop_trial(trial, error=True, error_msg=error_msg) # Note that we don't return the resources, since they may # have been lost. TODO(ujvl): is this the right thing to do? return False
def _find_item(self, dictionary, item): out = [rid for rid, t in dictionary.items() if t is item] return out
[docs] def stop_trial(self, trial, error=False, error_msg=None, destroy_pg_if_cannot_replace=True): """Only returns resources if resources allocated. If destroy_pg_if_cannot_replace is False, the Trial placement group will not be removed if it can't replace any staging ones.""" prior_status = trial.status self._stop_trial( trial, error=error, error_msg=error_msg, destroy_pg_if_cannot_replace=destroy_pg_if_cannot_replace) if prior_status == Trial.RUNNING: logger.debug("Trial %s: Returning resources.", trial) if not trial.uses_placement_groups: self._return_resources(trial.resources) out = self._find_item(self._running, trial) for result_id in out: self._running.pop(result_id)
[docs] def continue_training(self, trial): """Continues the training of this trial.""" self._train(trial)
[docs] def pause_trial(self, trial): """Pauses the trial. If trial is in-flight, preserves return value in separate queue before pausing, which is restored when Trial is resumed. """ trial_future = self._find_item(self._running, trial) if trial_future: self._paused[trial_future[0]] = trial super(RayTrialExecutor, self).pause_trial(trial)
[docs] def reset_trial(self, trial, new_config, new_experiment_tag, logger_creator=None): """Tries to invoke `Trainable.reset()` to reset trial. Args: trial (Trial): Trial to be reset. new_config (dict): New configuration for Trial trainable. new_experiment_tag (str): New experiment name for trial. logger_creator (Optional[Callable[[Dict], Logger]]): Function that instantiates a logger on the actor process. Returns: True if `reset_config` is successful else False. """ trial.set_experiment_tag(new_experiment_tag) trial.set_config(new_config) trainable = trial.runner # Pass magic variables extra_config = copy.deepcopy(new_config) extra_config[TRIAL_INFO] = TrialInfo(trial) stdout_file, stderr_file = trial.log_to_file extra_config[STDOUT_FILE] = stdout_file extra_config[STDERR_FILE] = stderr_file with self._change_working_directory(trial): with warn_if_slow("reset"): try: reset_val = ray.get( trainable.reset.remote(extra_config, logger_creator), timeout=DEFAULT_GET_TIMEOUT) except GetTimeoutError: logger.exception("Trial %s: reset timed out.", trial) return False return reset_val
[docs] def get_running_trials(self): """Returns the running trials.""" return list(self._running.values())
def get_alive_node_ips(self): now = time.time() if now - self._last_ip_refresh < self._refresh_period: return self._last_ip_addresses logger.debug("Checking ips from Ray state.") self._last_ip_refresh = now nodes = ray.state.nodes() ip_addresses = set() for node in nodes: if node["alive"]: ip_addresses.add(node["NodeManagerAddress"]) self._last_ip_addresses = ip_addresses return ip_addresses def get_current_trial_ips(self): return {t.node_ip for t in self.get_running_trials()}
[docs] def get_next_failed_trial(self): """Gets the first trial found to be running on a node presumed dead. Returns: A Trial object that is ready for failure processing. None if no failure detected. """ if ray.worker._mode() != ray.worker.LOCAL_MODE: live_cluster_ips = self.get_alive_node_ips() if live_cluster_ips - self.get_current_trial_ips(): for trial in self.get_running_trials(): if trial.node_ip and trial.node_ip not in live_cluster_ips: return trial return None
[docs] def get_next_available_trial(self, timeout: Optional[float] = None): if not self._running: return None shuffled_results = list(self._running.keys()) random.shuffle(shuffled_results) # Note: We shuffle the results because `ray.wait` by default returns # the first available result, and we want to guarantee that slower # trials (i.e. trials that run remotely) also get fairly reported. # See for details. start = time.time() ready, _ = ray.wait(shuffled_results, timeout=timeout) if not ready: return None result_id = ready[0] wait_time = time.time() - start if wait_time > NONTRIVIAL_WAIT_TIME_THRESHOLD_S: self._last_nontrivial_wait = time.time() if time.time() - self._last_nontrivial_wait > BOTTLENECK_WARN_PERIOD_S: logger.warning( "Over the last {} seconds, the Tune event loop has been " "backlogged processing new results. Consider increasing your " "period of result reporting to improve performance.".format( BOTTLENECK_WARN_PERIOD_S)) self._last_nontrivial_wait = time.time() return self._running[result_id]
[docs] def fetch_result(self, trial): """Fetches result list of the running trials. Returns: Result of the most recent trial training run. """ trial_future = self._find_item(self._running, trial) if not trial_future: raise ValueError("Trial was not running.") self._running.pop(trial_future[0]) with warn_if_slow("fetch_result"): result = ray.get(trial_future[0], timeout=DEFAULT_GET_TIMEOUT) # For local mode if isinstance(result, _LocalWrapper): result = result.unwrap() if not isinstance(result, list): return [result] return result
def _commit_resources(self, resources): committed = self._committed_resources all_keys = set(resources.custom_resources).union( set(committed.custom_resources)) custom_resources = { k: committed.get(k) + resources.get_res_total(k) for k in all_keys } self._committed_resources = Resources( committed.cpu + resources.cpu_total(), committed.gpu + resources.gpu_total(), committed.memory + resources.memory_total(), committed.object_store_memory + resources.object_store_memory_total(), custom_resources=custom_resources) def _return_resources(self, resources): committed = self._committed_resources all_keys = set(resources.custom_resources).union( set(committed.custom_resources)) custom_resources = { k: committed.get(k) - resources.get_res_total(k) for k in all_keys } self._committed_resources = Resources( committed.cpu - resources.cpu_total(), committed.gpu - resources.gpu_total(), custom_resources=custom_resources) assert self._committed_resources.is_nonnegative(), ( "Resource invalid: {}".format(resources)) def _update_avail_resources(self, num_retries=5): if time.time() - self._last_resource_refresh < self._refresh_period: return logger.debug("Checking Ray cluster resources.") resources = None for i in range(num_retries): if i > 0: logger.warning( "Cluster resources not detected or are 0. Attempt #" "%s...", i + 1) time.sleep(0.5) try: resources = ray.cluster_resources() except Exception as exc: # TODO(rliaw): Remove this when local mode is fixed. # logger.debug(f"{exc}: Using resources for local machine.") resources = ResourceSpec().resolve(True).to_resource_dict() if resources: break if not resources: # NOTE: This hides the possibility that Ray may be waiting for # clients to connect. resources.setdefault("CPU", 0) resources.setdefault("GPU", 0) logger.warning("Cluster resources cannot be detected or are 0. " "You can resume this experiment by passing in " "`resume=True` to `run`.") resources = resources.copy() num_cpus = resources.pop("CPU", 0) num_gpus = resources.pop("GPU", 0) memory = ray_constants.from_memory_units(resources.pop("memory", 0)) object_store_memory = ray_constants.from_memory_units( resources.pop("object_store_memory", 0)) custom_resources = resources self._avail_resources = Resources( int(num_cpus), int(num_gpus), memory=int(memory), object_store_memory=int(object_store_memory), custom_resources=custom_resources) self._last_resource_refresh = time.time() self._resources_initialized = True
[docs] def has_resources_for_trial(self, trial: Trial): """Returns whether this runner has resources available for this trial. If using placement groups, this will return True as long as we didn't reach the maximum number of pending trials. It will also return True if the trial placement group is already staged. Args: trial: Trial object which should be scheduled. Returns: boolean """ if trial.uses_placement_groups: return trial in self._staged_trials or self._pg_manager.can_stage( ) or self._pg_manager.has_ready( trial, update=True) return self.has_resources(trial.resources)
[docs] def has_resources(self, resources): """Returns whether this runner has at least the specified resources. This refreshes the Ray cluster resources if the time since last update has exceeded self._refresh_period. This also assumes that the cluster is not resizing very frequently. """ if resources.has_placement_group: return self._pg_manager.can_stage() self._update_avail_resources() currently_available = Resources.subtract(self._avail_resources, self._committed_resources) have_space = ( resources.cpu_total() <= currently_available.cpu and resources.gpu_total() <= currently_available.gpu and resources.memory_total() <= currently_available.memory and resources.object_store_memory_total() <= currently_available.object_store_memory and all( resources.get_res_total(res) <= currently_available.get(res) for res in resources.custom_resources)) if have_space: # The assumption right now is that we block all trials if one # trial is queued. self._trial_queued = False return True can_overcommit = self._queue_trials and not self._trial_queued if can_overcommit: self._trial_queued = True logger.warning( "Allowing trial to start even though the " "cluster does not have enough free resources. Trial actors " "may appear to hang until enough resources are added to the " "cluster (e.g., via autoscaling). You can disable this " "behavior by specifying `queue_trials=False` in " "") return True return False
[docs] def debug_string(self): """Returns a human readable message for printing to the console.""" total_resources = self._pg_manager.total_used_resources( self._committed_resources) if self._resources_initialized: status = ("Resources requested: {}/{} CPUs, {}/{} GPUs, " "{}/{} GiB heap, {}/{} GiB objects".format( total_resources.pop("CPU", 0), self._avail_resources.cpu, total_resources.pop("GPU", 0), self._avail_resources.gpu, _to_gb(total_resources.pop("memory", 0.)), _to_gb(self._avail_resources.memory), _to_gb( total_resources.pop("object_store_memory", 0.)), _to_gb(self._avail_resources.object_store_memory))) customs = ", ".join([ "{}/{} {}".format( total_resources.get(name, 0.), self._avail_resources.get_res_total(name), name) for name in self._avail_resources.custom_resources if not name.startswith(ray.resource_spec.NODE_ID_PREFIX) ]) if customs: status += " ({})".format(customs) return status else: return "Resources requested: ?"
[docs] def resource_string(self): """Returns a string describing the total resources available.""" if self._resources_initialized: res_str = ("{} CPUs, {} GPUs, " "{} GiB heap, {} GiB objects".format( self._avail_resources.cpu, self._avail_resources.gpu, _to_gb(self._avail_resources.memory), _to_gb(self._avail_resources.object_store_memory))) if self._avail_resources.custom_resources: custom = ", ".join( "{} {}".format( self._avail_resources.get_res_total(name), name) for name in self._avail_resources.custom_resources) res_str += " ({})".format(custom) return res_str else: return "? CPUs, ? GPUs"
[docs] def on_step_begin(self, trial_runner): """Before step() called, update the available resources.""" self._update_avail_resources() self._trial_just_finished_before = self._trial_just_finished self._trial_just_finished = False
[docs] def on_step_end(self, trial_runner): self._just_staged_trials.clear() if time.time() > self.last_pg_recon + self.pg_recon_interval: # Only do this every now and then - usually the placement groups # should not get out of sync, and calling this often is inefficient self._pg_manager.reconcile_placement_groups( trial_runner.get_trials()) self.last_pg_recon = time.time() self._pg_manager.cleanup()
[docs] def save(self, trial, storage=Checkpoint.PERSISTENT, result=None): """Saves the trial's state to a checkpoint asynchronously. Args: trial (Trial): The trial to be saved. storage (str): Where to store the checkpoint. Defaults to PERSISTENT. result (dict): The state of this trial as a dictionary to be saved. If result is None, the trial's last result will be used. Returns: Checkpoint object, or None if an Exception occurs. """ result = result or trial.last_result with self._change_working_directory(trial): if storage == Checkpoint.MEMORY: value = trial.runner.save_to_object.remote() checkpoint = Checkpoint(storage, value, result) trial.on_checkpoint(checkpoint) else: value = checkpoint = Checkpoint(storage, value, result) trial.saving_to = checkpoint self._running[value] = trial return checkpoint
[docs] def restore(self, trial, checkpoint=None, block=False): """Restores training state from a given model checkpoint. Args: trial (Trial): The trial to be restored. checkpoint (Checkpoint): The checkpoint to restore from. If None, the most recent PERSISTENT checkpoint is used. Defaults to None. block (bool): Whether or not to block on restore before returning. Raises: RuntimeError: This error is raised if no runner is found. AbortTrialExecution: This error is raised if the trial is ineligible for restoration, given the Tune input arguments. """ if checkpoint is None or checkpoint.value is None: checkpoint = trial.checkpoint if checkpoint.value is None: return if trial.runner is None: raise RuntimeError( "Trial {}: Unable to restore - no runner found.".format(trial)) value = checkpoint.value if == Checkpoint.MEMORY: logger.debug("Trial %s: Attempting restore from object", trial) # Note that we don't store the remote since in-memory checkpoints # don't guarantee fault tolerance and don't need to be waited on. with self._change_working_directory(trial): trial.runner.restore_from_object.remote(value) else: logger.debug("Trial %s: Attempting restore from %s", trial, value) if issubclass(trial.get_trainable_cls(), DurableTrainable) or not trial.sync_on_checkpoint: with self._change_working_directory(trial): remote = trial.runner.restore.remote(value) elif trial.sync_on_checkpoint: # This provides FT backwards compatibility in the # case where a DurableTrainable is not provided. logger.debug("Trial %s: Reading checkpoint into memory", trial) obj = TrainableUtil.checkpoint_to_object(value) with self._change_working_directory(trial): remote = trial.runner.restore_from_object.remote(obj) else: raise AbortTrialExecution( "Pass in `sync_on_checkpoint=True` for driver-based trial" "restoration. Pass in an `upload_dir` and a Trainable " "extending `DurableTrainable` for remote storage-based " "restoration") if block: ray.get(remote) else: self._running[remote] = trial trial.restoring_from = checkpoint
[docs] def export_trial_if_needed(self, trial): """Exports model of this trial based on trial.export_formats. Return: A dict that maps ExportFormats to successfully exported models. """ if trial.export_formats and len(trial.export_formats) > 0: with self._change_working_directory(trial): return ray.get( trial.runner.export_model.remote(trial.export_formats), timeout=DEFAULT_GET_TIMEOUT) return {}
[docs] def has_gpus(self): if self._resources_initialized: self._update_avail_resources() return self._avail_resources.gpu > 0
[docs] def cleanup(self, trial_runner): self._trial_cleanup.cleanup(partial=False) self._pg_manager.reconcile_placement_groups(trial_runner.get_trials()) self._pg_manager.cleanup(force=True) self._pg_manager.cleanup_existing_pg(block=True)
@contextmanager def _change_working_directory(self, trial): """Context manager changing working directory to trial logdir. Used in local mode. For non-local mode it is no-op. """ if ray.worker._mode() == ray.worker.LOCAL_MODE: old_dir = os.getcwd() try: os.chdir(trial.logdir) yield finally: os.chdir(old_dir) else: yield
def _to_gb(n_bytes): return round(n_bytes / (1024**3), 2)