Source code for ray.tune.trial_runner

import click
from datetime import datetime
import json
import logging
import os
import time
import traceback
import types

import ray.cloudpickle as cloudpickle
from ray.tune import TuneError
from ray.tune.stopper import NoopStopper
from ray.tune.progress_reporter import trial_progress_str
from ray.tune.ray_trial_executor import RayTrialExecutor
from ray.tune.result import (TIME_THIS_ITER_S, RESULT_DUPLICATE,
                             SHOULD_CHECKPOINT)
from ray.tune.syncer import get_cloud_syncer
from ray.tune.trial import Checkpoint, Trial
from ray.tune.schedulers import FIFOScheduler, TrialScheduler
from ray.tune.suggest import BasicVariantGenerator
from ray.tune.utils import warn_if_slow, flatten_dict
from ray.tune.web_server import TuneServer
from ray.utils import binary_to_hex, hex_to_binary

MAX_DEBUG_TRIALS = 20

logger = logging.getLogger(__name__)


def _find_newest_ckpt(ckpt_dir):
    """Returns path to most recently modified checkpoint."""
    full_paths = [
        os.path.join(ckpt_dir, fname) for fname in os.listdir(ckpt_dir)
        if fname.startswith("experiment_state") and fname.endswith(".json")
    ]
    return max(full_paths)


class _TuneFunctionEncoder(json.JSONEncoder):
    def default(self, obj):
        if isinstance(obj, types.FunctionType):
            return self._to_cloudpickle(obj)
        try:
            return super(_TuneFunctionEncoder, self).default(obj)
        except Exception:
            logger.debug("Unable to encode. Falling back to cloudpickle.")
            return self._to_cloudpickle(obj)

    def _to_cloudpickle(self, obj):
        return {
            "_type": "CLOUDPICKLE_FALLBACK",
            "value": binary_to_hex(cloudpickle.dumps(obj))
        }


class _TuneFunctionDecoder(json.JSONDecoder):
    def __init__(self, *args, **kwargs):
        json.JSONDecoder.__init__(
            self, object_hook=self.object_hook, *args, **kwargs)

    def object_hook(self, obj):
        if obj.get("_type") == "CLOUDPICKLE_FALLBACK":
            return self._from_cloudpickle(obj)
        return obj

    def _from_cloudpickle(self, obj):
        return cloudpickle.loads(hex_to_binary(obj["value"]))


[docs]class TrialRunner: """A TrialRunner implements the event loop for scheduling trials on Ray. .. code-block: python runner = TrialRunner() runner.add_trial(Trial(...)) runner.add_trial(Trial(...)) while not runner.is_finished(): runner.step() print(runner.debug_string()) The main job of TrialRunner is scheduling trials to efficiently use cluster resources, without overloading the cluster. While Ray itself provides resource management for tasks and actors, this is not sufficient when scheduling trials that may instantiate multiple actors. This is because if insufficient resources are available, concurrent trials could deadlock waiting for new resources to become available. Furthermore, oversubscribing the cluster could degrade training performance, leading to misleading benchmark results. Args: search_alg (SearchAlgorithm): SearchAlgorithm for generating Trial objects. scheduler (TrialScheduler): Defaults to FIFOScheduler. launch_web_server (bool): Flag for starting TuneServer local_checkpoint_dir (str): Path where global checkpoints are stored and restored from. remote_checkpoint_dir (str): Remote path where global checkpoints are stored and restored from. Used if `resume` == REMOTE. stopper: Custom class for stopping whole experiments. See ``Stopper``. resume (str|False): see `tune.py:run`. sync_to_cloud (func|str): See `tune.py:run`. server_port (int): Port number for launching TuneServer. fail_fast (bool): Finishes as soon as a trial fails if True. verbose (bool): Flag for verbosity. If False, trial results will not be output. checkpoint_period (int): Trial runner checkpoint periodicity in seconds. Defaults to 10. trial_executor (TrialExecutor): Defaults to RayTrialExecutor. """ CKPT_FILE_TMPL = "experiment_state-{}.json" VALID_RESUME_TYPES = [True, "LOCAL", "REMOTE", "PROMPT"] def __init__(self, search_alg=None, scheduler=None, launch_web_server=False, local_checkpoint_dir=None, remote_checkpoint_dir=None, sync_to_cloud=None, stopper=None, resume=False, server_port=TuneServer.DEFAULT_PORT, fail_fast=False, verbose=True, checkpoint_period=10, trial_executor=None): self._search_alg = search_alg or BasicVariantGenerator() self._scheduler_alg = scheduler or FIFOScheduler() self.trial_executor = trial_executor or RayTrialExecutor() # For debugging, it may be useful to halt trials after some time has # elapsed. TODO(ekl) consider exposing this in the API. self._global_time_limit = float( os.environ.get("TRIALRUNNER_WALLTIME_LIMIT", float("inf"))) self._total_time = 0 self._iteration = 0 self._has_errored = False self._fail_fast = fail_fast self._verbose = verbose self._server = None self._server_port = server_port if launch_web_server: self._server = TuneServer(self, self._server_port) self._trials = [] self._cached_trial_decisions = {} self._stop_queue = [] self._should_stop_experiment = False # used by TuneServer self._local_checkpoint_dir = local_checkpoint_dir if self._local_checkpoint_dir: os.makedirs(self._local_checkpoint_dir, exist_ok=True) self._remote_checkpoint_dir = remote_checkpoint_dir self._syncer = get_cloud_syncer(local_checkpoint_dir, remote_checkpoint_dir, sync_to_cloud) self._stopper = stopper or NoopStopper() self._resumed = False if self._validate_resume(resume_type=resume): try: self.resume() logger.info("Resuming trial.") self._resumed = True except Exception: logger.exception( "Runner restore failed. Restarting experiment.") else: logger.debug("Starting a new experiment.") self._start_time = time.time() self._last_checkpoint_time = -float("inf") self._checkpoint_period = checkpoint_period self._session_str = datetime.fromtimestamp( self._start_time).strftime("%Y-%m-%d_%H-%M-%S") self.checkpoint_file = None if self._local_checkpoint_dir: self.checkpoint_file = os.path.join( self._local_checkpoint_dir, TrialRunner.CKPT_FILE_TMPL.format(self._session_str)) @property def scheduler_alg(self): return self._scheduler_alg def _validate_resume(self, resume_type): """Checks whether to resume experiment. Args: resume_type: One of True, "REMOTE", "LOCAL", "PROMPT". """ if not resume_type: return False assert resume_type in self.VALID_RESUME_TYPES, ( "resume_type {} is not one of {}".format(resume_type, self.VALID_RESUME_TYPES)) # Not clear if we need this assertion, since we should always have a # local checkpoint dir. assert self._local_checkpoint_dir or self._remote_checkpoint_dir if resume_type in [True, "LOCAL", "PROMPT"]: if not self.checkpoint_exists(self._local_checkpoint_dir): raise ValueError("Called resume when no checkpoint exists " "in local directory.") elif resume_type == "PROMPT": if click.confirm("Resume from local directory?"): return True if resume_type in ["REMOTE", "PROMPT"]: if resume_type == "PROMPT" and not click.confirm( "Try downloading from remote directory?"): return False if not self._remote_checkpoint_dir: raise ValueError( "Called resume from remote without remote directory.") # Try syncing down the upload directory. logger.info("Downloading from %s", self._remote_checkpoint_dir) # TODO(ujvl): Note that this syncs down the entire directory, # which may also contain trial checkpoints. We should selectively # sync the necessary files instead. self._syncer.sync_down_if_needed() if not self.checkpoint_exists(self._local_checkpoint_dir): raise ValueError("Called resume when no checkpoint exists " "in remote or local directory.") return True @classmethod def checkpoint_exists(cls, directory): if not os.path.exists(directory): return False return any( (fname.startswith("experiment_state") and fname.endswith(".json")) for fname in os.listdir(directory)) def add_experiment(self, experiment): if not self._resumed: self._search_alg.add_configurations([experiment]) else: logger.info("TrialRunner resumed, ignoring new add_experiment.") def checkpoint(self, force=False): """Saves execution state to `self._local_checkpoint_dir`. Overwrites the current session checkpoint, which starts when self is instantiated. Throttle depends on self._checkpoint_period. Args: force (bool): Forces a checkpoint despite checkpoint_period. """ if not self._local_checkpoint_dir: return now = time.time() if now - self._last_checkpoint_time < self._checkpoint_period and ( not force): return self._last_checkpoint_time = now runner_state = { "checkpoints": list( self.trial_executor.get_checkpoints().values()), "runner_data": self.__getstate__(), "stats": { "start_time": self._start_time, "timestamp": self._last_checkpoint_time } } tmp_file_name = os.path.join(self._local_checkpoint_dir, ".tmp_checkpoint") with open(tmp_file_name, "w") as f: json.dump(runner_state, f, indent=2, cls=_TuneFunctionEncoder) os.rename(tmp_file_name, self.checkpoint_file) if force: self._syncer.sync_up() else: self._syncer.sync_up_if_needed() return self._local_checkpoint_dir def resume(self): """Resumes all checkpointed trials from previous run. Requires user to manually re-register their objects. Also stops all ongoing trials. """ newest_ckpt_path = _find_newest_ckpt(self._local_checkpoint_dir) with open(newest_ckpt_path, "r") as f: runner_state = json.load(f, cls=_TuneFunctionDecoder) self.checkpoint_file = newest_ckpt_path logger.warning("".join([ "Attempting to resume experiment from {}. ".format( self._local_checkpoint_dir), "This feature is experimental, " "and may not work with all search algorithms. ", "This will ignore any new changes to the specification." ])) self.__setstate__(runner_state["runner_data"]) trials = [] for trial_cp in runner_state["checkpoints"]: new_trial = Trial(trial_cp["trainable_name"]) new_trial.__setstate__(trial_cp) trials += [new_trial] for trial in sorted( trials, key=lambda t: t.last_update_time, reverse=True): self.add_trial(trial) def is_finished(self): """Returns whether all trials have finished running.""" if self._total_time > self._global_time_limit: logger.warning("Exceeded global time limit {} / {}".format( self._total_time, self._global_time_limit)) return True trials_done = all(trial.is_finished() for trial in self._trials) return trials_done and self._search_alg.is_finished() def step(self): """Runs one step of the trial event loop. Callers should typically run this method repeatedly in a loop. They may inspect or modify the runner's state in between calls to step(). """ if self.is_finished(): raise TuneError("Called step when all trials finished?") with warn_if_slow("on_step_begin"): self.trial_executor.on_step_begin(self) next_trial = self._get_next_trial() # blocking if next_trial is not None: with warn_if_slow("start_trial"): self.trial_executor.start_trial(next_trial) elif self.trial_executor.get_running_trials(): self._process_events() # blocking else: self.trial_executor.on_no_available_trials(self) self._stop_experiment_if_needed() try: with warn_if_slow("experiment_checkpoint"): self.checkpoint() except Exception: logger.exception("Trial Runner checkpointing failed.") self._iteration += 1 if self._server: with warn_if_slow("server"): self._process_stop_requests() if self.is_finished(): self._server.shutdown() with warn_if_slow("on_step_end"): self.trial_executor.on_step_end(self) def get_trial(self, tid): trial = [t for t in self._trials if t.trial_id == tid] return trial[0] if trial else None def get_trials(self): """Returns the list of trials managed by this TrialRunner. Note that the caller usually should not mutate trial state directly. """ return self._trials def add_trial(self, trial): """Adds a new trial to this TrialRunner. Trials may be added at any time. Args: trial (Trial): Trial to queue. """ trial.set_verbose(self._verbose) self._trials.append(trial) with warn_if_slow("scheduler.on_trial_add"): self._scheduler_alg.on_trial_add(self, trial) self.trial_executor.try_checkpoint_metadata(trial) def debug_string(self, delim="\n"): messages = [ self._scheduler_alg.debug_string(), self.trial_executor.debug_string(), trial_progress_str(self.get_trials()), ] return delim.join(messages) def has_resources(self, resources): """Returns whether this runner has at least the specified resources.""" return self.trial_executor.has_resources(resources) def _stop_experiment_if_needed(self): """Stops all trials.""" fail_fast = self._fail_fast and self._has_errored if (self._stopper.stop_all() or fail_fast or self._should_stop_experiment): self._search_alg.set_finished() [ self.trial_executor.stop_trial(t) for t in self._trials if t.status is not Trial.ERROR ] def _get_next_trial(self): """Replenishes queue. Blocks if all trials queued have finished, but search algorithm is still not finished. """ trials_done = all(trial.is_finished() for trial in self._trials) wait_for_trial = trials_done and not self._search_alg.is_finished() self._update_trial_queue(blocking=wait_for_trial) with warn_if_slow("choose_trial_to_run"): trial = self._scheduler_alg.choose_trial_to_run(self) return trial def _process_events(self): failed_trial = self.trial_executor.get_next_failed_trial() if failed_trial: error_msg = ( "{} (IP: {}) detected as stale. This is likely because the " "node was lost").format(failed_trial, failed_trial.node_ip) logger.info(error_msg) with warn_if_slow("process_failed_trial"): self._process_trial_failure(failed_trial, error_msg=error_msg) else: # TODO(ujvl): Consider combining get_next_available_trial and # fetch_result functionality so that we don't timeout on fetch. trial = self.trial_executor.get_next_available_trial() # blocking if trial.is_restoring: with warn_if_slow("process_trial_restore"): self._process_trial_restore(trial) elif trial.is_saving: with warn_if_slow("process_trial_save") as profile: self._process_trial_save(trial) if profile.too_slow and trial.sync_on_checkpoint: # TODO(ujvl): Suggest using DurableTrainable once # API has converged. logger.warning( "Consider turning off forced head-worker trial " "checkpoint syncs by setting sync_on_checkpoint=False" ". Note that this may result in faulty trial " "restoration if a failure occurs while the checkpoint " "is being synced from the worker to the head node.") else: with warn_if_slow("process_trial"): self._process_trial(trial) def _process_trial(self, trial): """Processes a trial result. Fetches the trial's latest result and makes a scheduling decision regarding its next action. If a checkpoint is taken, the decided action is cached and acted on only after the checkpoint is later processed (see `_process_trial_save`). Otherwise the decision is acted on immediately. Args: trial (Trial): Trial with a result ready to be processed. """ try: result = self.trial_executor.fetch_result(trial) is_duplicate = RESULT_DUPLICATE in result # TrialScheduler and SearchAlgorithm still receive a # notification because there may be special handling for # the `on_trial_complete` hook. if is_duplicate: logger.debug("Trial finished without logging 'done'.") result = trial.last_result result.update(done=True) self._total_time += result.get(TIME_THIS_ITER_S, 0) flat_result = flatten_dict(result) if self._stopper(trial.trial_id, result) or trial.should_stop(flat_result): # Hook into scheduler self._scheduler_alg.on_trial_complete(self, trial, flat_result) self._search_alg.on_trial_complete( trial.trial_id, result=flat_result) decision = TrialScheduler.STOP else: with warn_if_slow("scheduler.on_trial_result"): decision = self._scheduler_alg.on_trial_result( self, trial, flat_result) with warn_if_slow("search_alg.on_trial_result"): self._search_alg.on_trial_result(trial.trial_id, flat_result) if decision == TrialScheduler.STOP: with warn_if_slow("search_alg.on_trial_complete"): self._search_alg.on_trial_complete( trial.trial_id, result=flat_result) if not is_duplicate: trial.update_last_result( result, terminate=(decision == TrialScheduler.STOP)) # Checkpoints to disk. This should be checked even if # the scheduler decision is STOP or PAUSE. Note that # PAUSE only checkpoints to memory and does not update # the global checkpoint state. self._checkpoint_trial_if_needed( trial, force=result.get(SHOULD_CHECKPOINT, False)) if trial.is_saving: # Cache decision to execute on after the save is processed. # This prevents changing the trial's state or kicking off # another training step prematurely. self._cached_trial_decisions[trial.trial_id] = decision else: self._execute_action(trial, decision) except Exception: logger.exception("Trial %s: Error processing event.", trial) self._process_trial_failure(trial, traceback.format_exc()) def _process_trial_save(self, trial): """Processes a trial save. Acts on the decision cached during the last `_process_trial` call. Args: trial (Trial): Trial being saved. """ logger.debug("Trial %s: Processing trial save.", trial) checkpoint_value = None try: checkpoint_value = self.trial_executor.fetch_result(trial) except Exception: logger.exception("Trial %s: Error processing result.", trial) self._process_trial_failure(trial, traceback.format_exc()) if checkpoint_value: try: trial.saving_to.value = checkpoint_value trial.on_checkpoint(trial.saving_to) self.trial_executor.try_checkpoint_metadata(trial) except Exception: logger.exception("Trial %s: Error handling checkpoint %s", trial, checkpoint_value) trial.saving_to = None decision = self._cached_trial_decisions.pop(trial.trial_id, None) if decision and checkpoint_value: self._execute_action(trial, decision) def _process_trial_restore(self, trial): """Processes a trial restore. Args: trial (Trial): Trial being restored. """ logger.debug("Trial %s: Processing trial restore.", trial) try: self.trial_executor.fetch_result(trial) trial.on_restore() logger.debug("Trial %s: Restore processed successfully", trial) self.trial_executor.set_status(trial, Trial.RUNNING) self.trial_executor.continue_training(trial) except Exception: logger.exception("Trial %s: Error processing restore.", trial) self._process_trial_failure(trial, traceback.format_exc()) def _process_trial_failure(self, trial, error_msg): """Handle trial failure. Attempt trial recovery if possible, clean up state otherwise. Args: trial (Trial): Failed trial. error_msg (str): Error message prior to invoking this method. """ self._has_errored = True if trial.status == Trial.RUNNING: if trial.should_recover(): self._try_recover(trial, error_msg) else: self._scheduler_alg.on_trial_error(self, trial) self._search_alg.on_trial_complete(trial.trial_id, error=True) self.trial_executor.stop_trial( trial, error=True, error_msg=error_msg) def _execute_action(self, trial, decision): """Executes action based on decision. Args: trial (Trial): Trial to act on. decision (str): Scheduling decision to undertake. """ if decision == TrialScheduler.CONTINUE: self.trial_executor.continue_training(trial) elif decision == TrialScheduler.PAUSE: self.trial_executor.pause_trial(trial) elif decision == TrialScheduler.STOP: self.trial_executor.export_trial_if_needed(trial) self.trial_executor.stop_trial(trial) else: raise ValueError("Invalid decision: {}".format(decision)) def _checkpoint_trial_if_needed(self, trial, force=False): """Checkpoints trial based off trial.last_result.""" if trial.should_checkpoint() or force: # Save trial runtime if possible. if trial.runner: self.trial_executor.save(trial, storage=Checkpoint.PERSISTENT) def _try_recover(self, trial, error_msg): """Tries to recover trial. Notifies SearchAlgorithm and Scheduler if failure to recover. Args: trial (Trial): Trial to recover. error_msg (str): Error message from prior to invoking this method. """ if trial.is_restoring: # Restore was unsuccessful, try again without checkpoint. trial.clear_checkpoint() self.trial_executor.stop_trial( trial, error=error_msg is not None, error_msg=error_msg, stop_logger=False) trial.result_logger.flush() if self.trial_executor.has_resources(trial.resources): logger.info( "Trial %s: Attempting to restore " "trial state from last checkpoint.", trial) self.trial_executor.start_trial(trial) if trial.status == Trial.ERROR: logger.exception( "Trial %s: Error restoring trial from checkpoint, abort.", trial) self._scheduler_alg.on_trial_error(self, trial) self._search_alg.on_trial_complete(trial.trial_id, error=True) else: logger.debug("Trial %s: Restore dispatched correctly.", trial) else: logger.debug("Trial %s: Notifying Scheduler and requeueing.", trial) self._requeue_trial(trial) def _requeue_trial(self, trial): """Notification to TrialScheduler and requeue trial. This does not notify the SearchAlgorithm because the function evaluation is still in progress. """ self._scheduler_alg.on_trial_error(self, trial) self.trial_executor.set_status(trial, Trial.PENDING) # TODO(rliaw): Right now, this pushes the trial to the end of queue # because restoration can be expensive. However, this is not # ideal since it just hides the issue - a better fix would # be to use an actor table to detect the IP of the Trainable # and rsync the files there. # See https://github.com/ray-project/ray/issues/5168 self._trials.pop(self._trials.index(trial)) self._trials.append(trial) with warn_if_slow("scheduler.on_trial_add"): self._scheduler_alg.on_trial_add(self, trial) def _update_trial_queue(self, blocking=False, timeout=600): """Adds next trials to queue if possible. Note that the timeout is currently unexposed to the user. Args: blocking (bool): Blocks until either a trial is available or is_finished (timeout or search algorithm finishes). timeout (int): Seconds before blocking times out. """ trials = self._search_alg.next_trials() if blocking and not trials: start = time.time() # Checking `is_finished` instead of _search_alg.is_finished # is fine because blocking only occurs if all trials are # finished and search_algorithm is not yet finished while (not trials and not self.is_finished() and time.time() - start < timeout): logger.info("Blocking for next trial...") trials = self._search_alg.next_trials() time.sleep(1) for trial in trials: self.add_trial(trial) def request_stop_trial(self, trial): self._stop_queue.append(trial) def request_stop_experiment(self): self._should_stop_experiment = True def _process_stop_requests(self): while self._stop_queue: t = self._stop_queue.pop() self.stop_trial(t) def stop_trial(self, trial): """Stops trial. Trials may be stopped at any time. If trial is in state PENDING or PAUSED, calls `on_trial_remove` for scheduler and `on_trial_complete() for search_alg. Otherwise waits for result for the trial and calls `on_trial_complete` for scheduler and search_alg if RUNNING. """ error = False error_msg = None if trial.status in [Trial.ERROR, Trial.TERMINATED]: return elif trial.status in [Trial.PENDING, Trial.PAUSED]: self._scheduler_alg.on_trial_remove(self, trial) self._search_alg.on_trial_complete(trial.trial_id) elif trial.status is Trial.RUNNING: try: result = self.trial_executor.fetch_result(trial) trial.update_last_result(result, terminate=True) self._scheduler_alg.on_trial_complete(self, trial, result) self._search_alg.on_trial_complete( trial.trial_id, result=result) except Exception: error_msg = traceback.format_exc() logger.exception("Error processing event.") self._scheduler_alg.on_trial_error(self, trial) self._search_alg.on_trial_complete(trial.trial_id, error=True) error = True self.trial_executor.stop_trial(trial, error=error, error_msg=error_msg) def cleanup_trials(self): self.trial_executor.cleanup() def __getstate__(self): """Gets state for trial. Note that this is not used as a pickling override as does not have all fields. """ state = self.__dict__.copy() for k in [ "_trials", "_stop_queue", "_server", "_search_alg", "_scheduler_alg", "trial_executor", "_syncer", ]: del state[k] state["launch_web_server"] = bool(self._server) return state def __setstate__(self, state): launch_web_server = state.pop("launch_web_server") # Use session_str from previous checkpoint if does not exist session_str = state.pop("_session_str") self.__dict__.setdefault("_session_str", session_str) # Use start_time from previous checkpoint if does not exist start_time = state.pop("_start_time") self.__dict__.setdefault("_start_time", start_time) self.__dict__.update(state) if launch_web_server: self._server = TuneServer(self, self._server_port)