Source code for ray.tune.trainable.trainable

import copy
import logging
import os
import platform
import shutil
import subprocess
import sys
import tempfile
import time
import uuid
from contextlib import redirect_stderr, redirect_stdout
from datetime import datetime
from typing import Any, Callable, Dict, List, Optional, Union, TYPE_CHECKING

import ray
from ray.air._internal.remote_storage import list_at_uri
from ray.air._internal.util import skip_exceptions, exception_cause
from ray.air.checkpoint import (
from ray.tune.resources import Resources
from ray.tune.result import (
from ray.tune.syncer import Syncer
from ray.tune.utils import UtilMonitor
from ray.tune.utils.log import disable_ipython
from ray.tune.execution.placement_groups import PlacementGroupFactory
from ray.tune.trainable.util import TrainableUtil
from ray.tune.utils.util import (
from ray.util.annotations import PublicAPI

    from ray.tune.logger import Logger

logger = logging.getLogger(__name__)


[docs]@PublicAPI class Trainable: """Abstract class for trainable models, functions, etc. A call to ``train()`` on a trainable will execute one logical iteration of training. As a rule of thumb, the execution time of one train call should be large enough to avoid overheads (i.e. more than a few seconds), but short enough to report progress periodically (i.e. at most a few minutes). Calling ``save()`` should save the training state of a trainable to disk, and ``restore(path)`` should restore a trainable to the given state. Generally you only need to implement ``setup``, ``step``, ``save_checkpoint``, and ``load_checkpoint`` when subclassing Trainable. Other implementation methods that may be helpful to override are ``log_result``, ``reset_config``, ``cleanup``, and ``_export_model``. When using Tune, Tune will convert this class into a Ray actor, which runs on a separate process. Tune will also change the current working directory of this process to ``self.logdir``. This is designed so that different trials that run on the same physical node won't accidently write to the same location and overstep each other. If you want to know the orginal working directory path on the driver node, you can do so through env variable "TUNE_ORIG_WORKING_DIR". It is advised that you access this path for read only purposes and you need to make sure that the path exists on the remote nodes. This class supports checkpointing to and restoring from remote storage. """ def __init__( self, config: Dict[str, Any] = None, logger_creator: Callable[[Dict[str, Any]], "Logger"] = None, remote_checkpoint_dir: Optional[str] = None, custom_syncer: Optional[Syncer] = None, sync_timeout: Optional[int] = None, ): """Initialize a Trainable. Sets up logging and points ``self.logdir`` to a directory in which training outputs should be placed. Subclasses should prefer defining ``setup()`` instead of overriding ``__init__()`` directly. Args: config: Trainable-specific configuration data. By default will be saved as ``self.config``. logger_creator: Function that creates a ray.tune.Logger object. If unspecified, a default logger is created. remote_checkpoint_dir: Upload directory (S3 or GS path). This is **per trial** directory, which is different from **per checkpoint** directory. custom_syncer: Syncer used for synchronizing data from Ray nodes to external storage. sync_timeout: Timeout after which sync processes are aborted. """ self._experiment_id = uuid.uuid4().hex self.config = config or {} trial_info = self.config.pop(TRIAL_INFO, None) if self.is_actor(): disable_ipython() self._result_logger = self._logdir = None self._create_logger(self.config, logger_creator) self._stdout_context = self._stdout_fp = self._stdout_stream = None self._stderr_context = self._stderr_fp = self._stderr_stream = None self._stderr_logging_handler = None stdout_file = self.config.pop(STDOUT_FILE, None) stderr_file = self.config.pop(STDERR_FILE, None) self._open_logfiles(stdout_file, stderr_file) self._iteration = 0 self._time_total = 0.0 self._timesteps_total = None self._episodes_total = None self._time_since_restore = 0.0 self._timesteps_since_restore = 0 self._iterations_since_restore = 0 self._last_result = None self._restored = False self._trial_info = trial_info self._stdout_file = stdout_file self._stderr_file = stderr_file start_time = time.time() self._local_ip = ray.util.get_node_ip_address() self.setup(copy.deepcopy(self.config)) setup_time = time.time() - start_time if setup_time > SETUP_TIME_THRESHOLD: "Trainable.setup took {:.3f} seconds. If your " "trainable is slow to initialize, consider setting " "reuse_actors=True to reduce actor creation " "overheads.".format(setup_time) ) log_sys_usage = self.config.get("log_sys_usage", False) self._start_time = start_time self._warmup_time = None self._monitor = UtilMonitor(start=log_sys_usage) self.remote_checkpoint_dir = remote_checkpoint_dir self.custom_syncer = custom_syncer self.sync_timeout = sync_timeout @property def uses_cloud_checkpointing(self): return bool(self.remote_checkpoint_dir)
[docs] def _storage_path(self, local_path): """Converts a `local_path` to be based off of `self.remote_checkpoint_dir`.""" rel_local_path = os.path.relpath(local_path, self.logdir) return os.path.join(self.remote_checkpoint_dir, rel_local_path)
[docs] @classmethod def default_resource_request( cls, config: Dict[str, Any] ) -> Optional[Union[Resources, PlacementGroupFactory]]: """Provides a static resource requirement for the given configuration. This can be overridden by sub-classes to set the correct trial resource allocation, so the user does not need to. .. code-block:: python @classmethod def default_resource_request(cls, config): return PlacementGroupFactory([{"CPU": 1}, {"CPU": 1}]]) Args: config[Dict[str, Any]]: The Trainable's config dict. Returns: Union[Resources, PlacementGroupFactory]: A Resources object or PlacementGroupFactory consumed by Tune for queueing. """ return None
[docs] @classmethod def resource_help(cls, config: Dict): """Returns a help string for configuring this trainable's resources. Args: config: The Trainer's config dict. """ return ""
def get_current_ip_pid(self): return self._local_ip, os.getpid()
[docs] def get_auto_filled_metrics( self, now: Optional[datetime] = None, time_this_iter: Optional[float] = None, debug_metrics_only: bool = False, ) -> dict: """Return a dict with metrics auto-filled by the trainable. If ``debug_metrics_only`` is True, only metrics that don't require at least one iteration will be returned (``ray.tune.result.DEBUG_METRICS``). """ if now is None: now = autofilled = { TRIAL_ID: self.trial_id, "experiment_id": self._experiment_id, "date": now.strftime("%Y-%m-%d_%H-%M-%S"), "timestamp": int(time.mktime(now.timetuple())), TIME_THIS_ITER_S: time_this_iter, TIME_TOTAL_S: self._time_total, PID: os.getpid(), HOSTNAME: platform.node(), NODE_IP: self._local_ip, "config": self.config, "time_since_restore": self._time_since_restore, "timesteps_since_restore": self._timesteps_since_restore, "iterations_since_restore": self._iterations_since_restore, "warmup_time": self._warmup_time, } if debug_metrics_only: autofilled = {k: v for k, v in autofilled.items() if k in DEBUG_METRICS} return autofilled
def is_actor(self): try: actor_id = ray._private.worker.global_worker.actor_id return actor_id != actor_id.nil() except Exception: # If global_worker is not instantiated, we're not in an actor return False
[docs] def train_buffered(self, buffer_time_s: float, max_buffer_length: int = 1000): """Runs multiple iterations of training. Calls ``train()`` internally. Collects and combines multiple results. This function will run ``self.train()`` repeatedly until one of the following conditions is met: 1) the maximum buffer length is reached, 2) the maximum buffer time is reached, or 3) a checkpoint was created. Even if the maximum time is reached, it will always block until at least one result is received. Args: buffer_time_s: Maximum time to buffer. The next result received after this amount of time has passed will return the whole buffer. max_buffer_length: Maximum number of results to buffer. """ results = [] now = time.time() send_buffer_at = now + buffer_time_s while now < send_buffer_at or not results: # At least one result result = self.train() results.append(result) if result.get(DONE, False): # If the trial is done, return break elif result.get(SHOULD_CHECKPOINT, False): # If a checkpoint was created, return break elif result.get(RESULT_DUPLICATE): # If the function API trainable completed, return break elif len(results) >= max_buffer_length: # If the buffer is full, return break now = time.time() return results
[docs] def train(self): """Runs one logical iteration of training. Calls ``step()`` internally. Subclasses should override ``step()`` instead to return results. This method automatically fills the following fields in the result: `done` (bool): training is terminated. Filled only if not provided. `time_this_iter_s` (float): Time in seconds this iteration took to run. This may be overridden in order to override the system-computed time difference. `time_total_s` (float): Accumulated time in seconds for this entire experiment. `experiment_id` (str): Unique string identifier for this experiment. This id is preserved across checkpoint / restore calls. `training_iteration` (int): The index of this training iteration, e.g. call to train(). This is incremented after `step()` is called. `pid` (str): The pid of the training process. `date` (str): A formatted date of when the result was processed. `timestamp` (str): A UNIX timestamp of when the result was processed. `hostname` (str): Hostname of the machine hosting the training process. `node_ip` (str): Node ip of the machine hosting the training process. Returns: A dict that describes training progress. """ if self._warmup_time is None: self._warmup_time = time.time() - self._start_time start = time.time() try: result = self.step() except Exception as e: skipped = skip_exceptions(e) raise skipped from exception_cause(skipped) assert isinstance(result, dict), "step() needs to return a dict." # We do not modify internal state nor update this result if duplicate. if RESULT_DUPLICATE in result: return result result = result.copy() self._iteration += 1 self._iterations_since_restore += 1 if result.get(TIME_THIS_ITER_S) is not None: time_this_iter = result[TIME_THIS_ITER_S] else: time_this_iter = time.time() - start self._time_total += time_this_iter self._time_since_restore += time_this_iter result.setdefault(DONE, False) # self._timesteps_total should only be tracked if increments provided if result.get(TIMESTEPS_THIS_ITER) is not None: if self._timesteps_total is None: self._timesteps_total = 0 self._timesteps_total += result[TIMESTEPS_THIS_ITER] self._timesteps_since_restore += result[TIMESTEPS_THIS_ITER] # self._episodes_total should only be tracked if increments provided if result.get(EPISODES_THIS_ITER) is not None: if self._episodes_total is None: self._episodes_total = 0 self._episodes_total += result[EPISODES_THIS_ITER] # self._timesteps_total should not override user-provided total result.setdefault(TIMESTEPS_TOTAL, self._timesteps_total) result.setdefault(EPISODES_TOTAL, self._episodes_total) result.setdefault(TRAINING_ITERATION, self._iteration) # Provides auto-filled neg_mean_loss for avoiding regressions if result.get("mean_loss"): result.setdefault("neg_mean_loss", -result["mean_loss"]) now = result.update(self.get_auto_filled_metrics(now, time_this_iter)) monitor_data = self._monitor.get_data() if monitor_data: result.update(monitor_data) self.log_result(result) if self._stdout_context: self._stdout_stream.flush() if self._stderr_context: self._stderr_stream.flush() self._last_result = result return result
def get_state(self): return { "experiment_id": self._experiment_id, "iteration": self._iteration, "timesteps_total": self._timesteps_total, "time_total": self._time_total, "episodes_total": self._episodes_total, "last_result": self._last_result, "ray_version": ray.__version__, } def _create_checkpoint_dir( self, checkpoint_dir: Optional[str] = None ) -> Optional[str]: # Create checkpoint_xxxxx directory and drop checkpoint marker checkpoint_dir = TrainableUtil.make_checkpoint_dir( checkpoint_dir or self.logdir, index=self.iteration, override=True ) return checkpoint_dir
[docs] def save( self, checkpoint_dir: Optional[str] = None, prevent_upload: bool = False ) -> str: """Saves the current model state to a checkpoint. Subclasses should override ``save_checkpoint()`` instead to save state. This method dumps additional metadata alongside the saved path. If a remote checkpoint dir is given, this will also sync up to remote storage. Args: checkpoint_dir: Optional dir to place the checkpoint. prevent_upload: If True, will not upload the saved checkpoint to cloud. Returns: The given or created checkpoint directory. Note the return path should match up with what is expected of `restore()`. """ checkpoint_dir = self._create_checkpoint_dir(checkpoint_dir=checkpoint_dir) # User saves checkpoint checkpoint_dict_or_path = self.save_checkpoint(checkpoint_dir) if checkpoint_dict_or_path is None: # checkpoint_dict_or_path can only be None in class trainables. # In that case the default is to use the root checkpoint directory. assert checkpoint_dir checkpoint_dict_or_path = checkpoint_dir elif checkpoint_dir is None: # checkpoint_dir is only None in function trainables. In that case, # checkpoint_dict_or_path points to the already saved checkpoint dir. # This will be considered the root dir. assert isinstance(checkpoint_dict_or_path, str) checkpoint_dir = checkpoint_dict_or_path # Get trainable metadata metadata = self.get_state() if isinstance(checkpoint_dict_or_path, dict): metadata["relative_checkpoint_path"] = "" metadata["saved_as_dict"] = True Checkpoint.from_dict(checkpoint_dict_or_path).to_directory(checkpoint_dir) # Re-drop marker TrainableUtil.mark_as_checkpoint_dir(checkpoint_dir) else: # Make sure the checkpoint dir is contained if not checkpoint_dict_or_path.startswith(checkpoint_dir): raise ValueError( f"The returned checkpoint path must be within the given " f"checkpoint dir ({checkpoint_dir}): {checkpoint_dict_or_path}" ) # Get relative path to returned checkpoint relative_checkpoint_path = os.path.relpath( checkpoint_dict_or_path, checkpoint_dir ) metadata["relative_checkpoint_path"] = relative_checkpoint_path metadata["saved_as_dict"] = False TrainableUtil.write_metadata(checkpoint_dir, metadata) # Maybe sync to cloud if not prevent_upload: self._maybe_save_to_cloud(checkpoint_dir) return checkpoint_dir
def _get_latest_available_checkpoint(self) -> Optional[str]: latest_local_checkpoint = self._get_latest_local_available_checkpoint() latest_remote_checkpoint = self._get_latest_remote_available_checkpoint() if not latest_local_checkpoint: return latest_remote_checkpoint elif not latest_remote_checkpoint: return latest_local_checkpoint # Else, both are available return max([latest_local_checkpoint, latest_remote_checkpoint]) def _get_latest_local_available_checkpoint(self) -> Optional[str]: checkpoint_candidates = [] for name in os.listdir(self._logdir): if not name.startswith("checkpoint_"): continue candidate_path = os.path.join(self._logdir, name) if not os.path.isdir(candidate_path): continue # On local storage it is cheap to check for valid checkpoints try: TrainableUtil.find_checkpoint_dir(candidate_path) except Exception: continue checkpoint_candidates.append(candidate_path) if not checkpoint_candidates: return None return max(checkpoint_candidates) def _get_latest_remote_available_checkpoint(self) -> Optional[str]: if not self.remote_checkpoint_dir: return None checkpoint_candidates = [] for name in list_at_uri(self.remote_checkpoint_dir): if not name.startswith("checkpoint_"): continue candidate_path = os.path.join(self._logdir, name) checkpoint_candidates.append(candidate_path) if not checkpoint_candidates: return None return max(checkpoint_candidates) def _maybe_save_to_cloud(self, checkpoint_dir: str) -> bool: if not self.uses_cloud_checkpointing: return False if self.custom_syncer: self.custom_syncer.sync_up( checkpoint_dir, self._storage_path(checkpoint_dir) ) self.custom_syncer.wait_or_retry() return True checkpoint = Checkpoint.from_directory(checkpoint_dir) checkpoint_uri = self._storage_path(checkpoint_dir) if not retry_fn( lambda: checkpoint.to_uri(checkpoint_uri), subprocess.CalledProcessError, num_retries=3, sleep_time=1, timeout=self.sync_timeout, ): logger.error( f"Could not upload checkpoint even after 3 retries." f"Please check if the credentials expired and that the remote " f"filesystem is supported.. For large checkpoints, consider " f"increasing `SyncConfig(sync_timeout)` " f"(current value: {self.sync_timeout} seconds). Checkpoint URI: " f"{checkpoint_uri}" ) return True def _maybe_load_from_cloud(self, checkpoint_path: str) -> bool: if os.path.exists(checkpoint_path): try: TrainableUtil.find_checkpoint_dir(checkpoint_path) except Exception: pass else: # If the path exists locally, we don't have to download return True if not self.uses_cloud_checkpointing: return False rel_checkpoint_dir = TrainableUtil.find_rel_checkpoint_dir( self.logdir, checkpoint_path ) external_uri = os.path.join(self.remote_checkpoint_dir, rel_checkpoint_dir) local_dir = os.path.join(self.logdir, rel_checkpoint_dir) path_existed_before = os.path.exists(local_dir) if self.custom_syncer: # Only keep for backwards compatibility self.custom_syncer.sync_down(remote_dir=external_uri, local_dir=local_dir) self.custom_syncer.wait_or_retry() return True checkpoint = Checkpoint.from_uri(external_uri) if not retry_fn( lambda: checkpoint.to_directory(local_dir), (subprocess.CalledProcessError, FileNotFoundError), num_retries=3, sleep_time=1, timeout=self.sync_timeout, ): logger.error( f"Could not download checkpoint even after 3 retries: " f"{external_uri}" ) # We may have created this dir when we tried to sync, so clean up if not path_existed_before and os.path.exists(local_dir): shutil.rmtree(local_dir) return False return True
[docs] def save_to_object(self): """Saves the current model state to a Python object. It also saves to disk but does not return the checkpoint path. It does not save the checkpoint to cloud storage. Returns: Object holding checkpoint data. """ temp_container_dir = tempfile.mkdtemp("save_to_object", dir=self.logdir) checkpoint_dir =, prevent_upload=True) obj_ref = Checkpoint.from_directory(checkpoint_dir).to_bytes() shutil.rmtree(temp_container_dir) return obj_ref
def _restore_from_checkpoint_obj(self, checkpoint: Checkpoint): with checkpoint.as_directory() as converted_checkpoint_path: return self.restore( checkpoint_path=converted_checkpoint_path, checkpoint_node_ip=None, )
[docs] def restore( self, checkpoint_path: Union[str, Checkpoint], checkpoint_node_ip: Optional[str] = None, fallback_to_latest: bool = False, ): """Restores training state from a given model checkpoint. These checkpoints are returned from calls to save(). Subclasses should override ``load_checkpoint()`` instead to restore state. This method restores additional metadata saved with the checkpoint. `checkpoint_path` should match with the return from ``save()``. `checkpoint_path` can be `~/ray_results/exp/MyTrainable_abc/ checkpoint_00000/checkpoint`. Or, `~/ray_results/exp/MyTrainable_abc/checkpoint_00000`. `self.logdir` should generally be corresponding to `checkpoint_path`, for example, `~/ray_results/exp/MyTrainable_abc`. `self.remote_checkpoint_dir` in this case, is something like, `REMOTE_CHECKPOINT_BUCKET/exp/MyTrainable_abc` Args: checkpoint_path: Path to restore checkpoint from. If this path does not exist on the local node, it will be fetched from external (cloud) storage if available, or restored from a remote node. checkpoint_node_ip: If given, try to restore checkpoint from this node if it doesn't exist locally or on cloud storage. fallback_to_latest: If True, will try to recover the latest available checkpoint if the given ``checkpoint_path`` could not be found. """ # Ensure Checkpoints are converted if isinstance(checkpoint_path, Checkpoint): return self._restore_from_checkpoint_obj(checkpoint_path) if not self._maybe_load_from_cloud(checkpoint_path) and ( # If a checkpoint source IP is given checkpoint_node_ip # And the checkpoint does not currently exist on the local node and not os.path.exists(checkpoint_path) # And the source IP is different to the current IP and checkpoint_node_ip != ray.util.get_node_ip_address() ): checkpoint = _get_checkpoint_from_remote_node( checkpoint_path, checkpoint_node_ip ) if checkpoint: checkpoint.to_directory(checkpoint_path) if not os.path.exists(checkpoint_path): if fallback_to_latest: f"Checkpoint path was not available, trying to recover from latest " f"available checkpoint instead. Unavailable checkpoint path: " f"{checkpoint_path}" ) checkpoint_path = self._get_latest_available_checkpoint() if checkpoint_path: f"Trying to recover from latest available checkpoint: " f"{checkpoint_path}" ) return self.restore(checkpoint_path, fallback_to_latest=False) # Else, raise raise ValueError( f"Could not recover from checkpoint as it does not exist on local " f"disk and was not available on cloud storage or another Ray node. " f"Got checkpoint path: {checkpoint_path} and IP {checkpoint_node_ip}" ) checkpoint_dir = TrainableUtil.find_checkpoint_dir(checkpoint_path) metadata = TrainableUtil.load_metadata(checkpoint_dir) if metadata["saved_as_dict"]: # If data was saved as a dict (e.g. from a class trainable), # also pass the dict to `load_checkpoint()`. checkpoint_dict = Checkpoint.from_directory(checkpoint_dir).to_dict() # If other files were added to the directory after converting from the # original dict (e.g. marker files), clean these up checkpoint_dict.pop(_DICT_CHECKPOINT_ADDITIONAL_FILE_KEY, None) to_load = checkpoint_dict else: # Otherwise, pass the relative checkpoint path relative_checkpoint_path = metadata["relative_checkpoint_path"] to_load = os.path.join(checkpoint_dir, relative_checkpoint_path) # Set metadata self._experiment_id = metadata["experiment_id"] self._iteration = metadata["iteration"] self._timesteps_total = metadata["timesteps_total"] self._time_total = metadata["time_total"] self._episodes_total = metadata["episodes_total"] # Actually load checkpoint self.load_checkpoint(to_load) self._time_since_restore = 0.0 self._timesteps_since_restore = 0 self._iterations_since_restore = 0 self._restored = True "Restored on %s from checkpoint: %s", self._local_ip, checkpoint_dir ) state = { "_iteration": self._iteration, "_timesteps_total": self._timesteps_total, "_time_total": self._time_total, "_episodes_total": self._episodes_total, }"Current state after restoring: %s", state)
[docs] def restore_from_object(self, obj): """Restores training state from a checkpoint object. These checkpoints are returned from calls to save_to_object(). """ checkpoint = Checkpoint.from_bytes(obj) with checkpoint.as_directory() as checkpoint_path: self.restore(checkpoint_path)
[docs] def delete_checkpoint(self, checkpoint_path: Union[str, Checkpoint]): """Deletes local copy of checkpoint. Args: checkpoint_path: Path to checkpoint. """ # Ensure Checkpoints are converted if isinstance(checkpoint_path, Checkpoint) and checkpoint_path._local_path: checkpoint_path = checkpoint_path._local_path try: checkpoint_dir = TrainableUtil.find_checkpoint_dir(checkpoint_path) except FileNotFoundError: # The checkpoint won't exist locally if the # trial was rescheduled to another worker. logger.debug( f"Local checkpoint not found during garbage collection: " f"{self.trial_id} - {checkpoint_path}" ) return else: if self.uses_cloud_checkpointing: if self.custom_syncer: # Keep for backwards compatibility self.custom_syncer.delete(self._storage_path(checkpoint_dir)) self.custom_syncer.wait_or_retry() else: checkpoint_uri = self._storage_path(checkpoint_dir) if not retry_fn( lambda: _delete_external_checkpoint(checkpoint_uri), subprocess.CalledProcessError, num_retries=3, sleep_time=1, timeout=self.sync_timeout, ): logger.error( f"Could not delete checkpoint even after 3 retries: " f"{checkpoint_uri}" ) if os.path.exists(checkpoint_dir): shutil.rmtree(checkpoint_dir)
[docs] def export_model( self, export_formats: Union[List[str], str], export_dir: Optional[str] = None ): """Exports model based on export_formats. Subclasses should override _export_model() to actually export model to local directory. Args: export_formats: Format or list of (str) formats that should be exported. export_dir: Optional dir to place the exported model. Defaults to self.logdir. Returns: A dict that maps ExportFormats to successfully exported models. """ if isinstance(export_formats, str): export_formats = [export_formats] export_dir = export_dir or self.logdir return self._export_model(export_formats, export_dir)
[docs] def reset(self, new_config, logger_creator=None): """Resets trial for use with new config. Subclasses should override reset_config() to actually reset actor behavior for the new config.""" self.config = new_config trial_info = new_config.pop(TRIAL_INFO, None) if trial_info: self._trial_info = trial_info self._result_logger.flush() self._result_logger.close() if logger_creator: logger.debug("Logger reset.") self._create_logger(new_config.copy(), logger_creator) else: logger.debug( "Did not reset logger. Got: " f"trainable.reset(logger_creator={logger_creator})." ) stdout_file = new_config.pop(STDOUT_FILE, None) stderr_file = new_config.pop(STDERR_FILE, None) self._close_logfiles() self._open_logfiles(stdout_file, stderr_file) success = self.reset_config(new_config) if not success: return False # Reset attributes. Will be overwritten by `restore` if a checkpoint # is provided. self._iteration = 0 self._time_total = 0.0 self._timesteps_total = None self._episodes_total = None self._time_since_restore = 0.0 self._timesteps_since_restore = 0 self._iterations_since_restore = 0 self._restored = False return True
[docs] def reset_config(self, new_config: Dict): """Resets configuration without restarting the trial. This method is optional, but can be implemented to speed up algorithms such as PBT, and to allow performance optimizations such as running experiments with reuse_actors=True. Args: new_config: Updated hyperparameter configuration for the trainable. Returns: True if reset was successful else False. """ return False
[docs] def _create_logger( self, config: Dict[str, Any], logger_creator: Callable[[Dict[str, Any]], "Logger"] = None, ): """Create logger from logger creator. Sets _logdir and _result_logger. `_logdir` is the **per trial** directory for the Trainable. """ if logger_creator: self._result_logger = logger_creator(config) self._logdir = self._result_logger.logdir else: from ray.tune.logger import UnifiedLogger logdir_prefix ="%Y-%m-%d_%H-%M-%S") ray._private.utils.try_to_create_directory(DEFAULT_RESULTS_DIR) self._logdir = tempfile.mkdtemp( prefix=logdir_prefix, dir=DEFAULT_RESULTS_DIR ) self._result_logger = UnifiedLogger(config, self._logdir, loggers=None)
[docs] def _open_logfiles(self, stdout_file, stderr_file): """Create loggers. Open stdout and stderr logfiles.""" if stdout_file: stdout_path = os.path.expanduser(os.path.join(self._logdir, stdout_file)) self._stdout_fp = open(stdout_path, "a+") self._stdout_stream = Tee(sys.stdout, self._stdout_fp) self._stdout_context = redirect_stdout(self._stdout_stream) self._stdout_context.__enter__() if stderr_file: stderr_path = os.path.expanduser(os.path.join(self._logdir, stderr_file)) self._stderr_fp = open(stderr_path, "a+") self._stderr_stream = Tee(sys.stderr, self._stderr_fp) self._stderr_context = redirect_stderr(self._stderr_stream) self._stderr_context.__enter__() # Add logging handler to root ray logger formatter = logging.Formatter( "[%(levelname)s %(asctime)s] " "%(filename)s: %(lineno)d " "%(message)s" ) self._stderr_logging_handler = logging.StreamHandler(self._stderr_fp) self._stderr_logging_handler.setFormatter(formatter) ray.logger.addHandler(self._stderr_logging_handler)
[docs] def _close_logfiles(self): """Close stdout and stderr logfiles.""" if self._stderr_logging_handler: ray.logger.removeHandler(self._stderr_logging_handler) if self._stdout_context: self._stdout_stream.flush() self._stdout_context.__exit__(None, None, None) self._stdout_fp.close() self._stdout_context = None if self._stderr_context: self._stderr_stream.flush() self._stderr_context.__exit__(None, None, None) self._stderr_fp.close() self._stderr_context = None
[docs] def stop(self): """Releases all resources used by this trainable. Calls ``Trainable.cleanup`` internally. Subclasses should override ``Trainable.cleanup`` for custom cleanup procedures. """ self._result_logger.flush() self._result_logger.close() if self._monitor.is_alive(): self._monitor.stop() self._monitor.join() self.cleanup() self._close_logfiles()
@property def logdir(self): """Directory of the results and checkpoints for this Trainable. Tune will automatically sync this folder with the driver if execution is distributed. Note that the current working directory will also be changed to this. """ return os.path.join(self._logdir, "") @property def trial_name(self): """Trial name for the corresponding trial of this Trainable. This is not set if not using Tune. .. code-block:: python name = self.trial_name """ if self._trial_info: return self._trial_info.trial_name else: return "default" @property def trial_id(self): """Trial ID for the corresponding trial of this Trainable. This is not set if not using Tune. .. code-block:: python trial_id = self.trial_id """ if self._trial_info: return self._trial_info.trial_id else: return "default" @property def trial_resources(self) -> Union[Resources, PlacementGroupFactory]: """Resources currently assigned to the trial of this Trainable. This is not set if not using Tune. .. code-block:: python trial_resources = self.trial_resources """ if self._trial_info: return self._trial_info.trial_resources else: return "default" @property def iteration(self): """Current training iteration. This value is automatically incremented every time `train()` is called and is automatically inserted into the training result dict. """ return self._iteration @property def training_iteration(self): """Current training iteration (same as `self.iteration`). This value is automatically incremented every time `train()` is called and is automatically inserted into the training result dict. """ return self._iteration
[docs] def get_config(self): """Returns configuration passed in by Tune.""" return self.config
[docs] def step(self): """Subclasses should override this to implement train(). The return value will be automatically passed to the loggers. Users can also return `tune.result.DONE` or `tune.result.SHOULD_CHECKPOINT` as a key to manually trigger termination or checkpointing of this trial. Note that manual checkpointing only works when subclassing Trainables. .. versionadded:: 0.8.7 Returns: A dict that describes training progress. """ raise NotImplementedError
[docs] def save_checkpoint(self, checkpoint_dir: str) -> Optional[Union[str, Dict]]: """Subclasses should override this to implement ``save()``. Warning: Do not rely on absolute paths in the implementation of ``Trainable.save_checkpoint`` and ``Trainable.load_checkpoint``. Use ``validate_save_restore`` to catch ``Trainable.save_checkpoint``/ ``Trainable.load_checkpoint`` errors before execution. >>> from ray.tune.utils import validate_save_restore >>> MyTrainableClass = ... # doctest: +SKIP >>> validate_save_restore(MyTrainableClass) # doctest: +SKIP >>> validate_save_restore( # doctest: +SKIP ... MyTrainableClass, use_object_store=True) .. versionadded:: 0.8.7 Args: checkpoint_dir: The directory where the checkpoint file must be stored. In a Tune run, if the trial is paused, the provided path may be temporary and moved. Returns: A dict or string. If string, the return value is expected to be prefixed by `tmp_checkpoint_dir`. If dict, the return value will be automatically serialized by Tune and passed to ``Trainable.load_checkpoint()``. Example: >>> trainable, trainable1, trainable2 = ... # doctest: +SKIP >>> print(trainable1.save_checkpoint("/tmp/checkpoint_1")) # doctest: +SKIP "/tmp/checkpoint_1" >>> print(trainable2.save_checkpoint("/tmp/checkpoint_2")) # doctest: +SKIP {"some": "data"} >>> trainable.save_checkpoint("/tmp/bad_example") # doctest: +SKIP "/tmp/NEW_CHECKPOINT_PATH/my_checkpoint_file" # This will error. """ raise NotImplementedError
[docs] def load_checkpoint(self, checkpoint: Union[Dict, str]): """Subclasses should override this to implement restore(). Warning: In this method, do not rely on absolute paths. The absolute path of the checkpoint_dir used in ``Trainable.save_checkpoint`` may be changed. If ``Trainable.save_checkpoint`` returned a prefixed string, the prefix of the checkpoint string returned by ``Trainable.save_checkpoint`` may be changed. This is because trial pausing depends on temporary directories. The directory structure under the checkpoint_dir provided to ``Trainable.save_checkpoint`` is preserved. See the example below. Example: >>> from ray.tune.trainable import Trainable >>> class Example(Trainable): ... def save_checkpoint(self, checkpoint_path): ... print(checkpoint_path) ... return os.path.join(checkpoint_path, "my/check/point") ... def load_checkpoint(self, checkpoint): ... print(checkpoint) >>> trainer = Example() >>> # This is used when PAUSED. >>> obj = trainer.save_to_object() # doctest: +SKIP <logdir>/tmpc8k_c_6hsave_to_object/checkpoint_0/my/check/point >>> # Note the different prefix. >>> trainer.restore_from_object(obj) # doctest: +SKIP <logdir>/tmpb87b5axfrestore_from_object/checkpoint_0/my/check/point .. versionadded:: 0.8.7 Args: checkpoint: If dict, the return value is as returned by `save_checkpoint`. If a string, then it is a checkpoint path that may have a different prefix than that returned by `save_checkpoint`. The directory structure underneath the `checkpoint_dir` `save_checkpoint` is preserved. """ raise NotImplementedError
[docs] def setup(self, config: Dict): """Subclasses should override this for custom initialization. .. versionadded:: 0.8.7 Args: config: Hyperparameters and other configs given. Copy of `self.config`. """ pass
[docs] def log_result(self, result: Dict): """Subclasses can optionally override this to customize logging. The logging here is done on the worker process rather than the driver. .. versionadded:: 0.8.7 Args: result: Training result returned by step(). """ self._result_logger.on_result(result)
[docs] def cleanup(self): """Subclasses should override this for any cleanup on stop. If any Ray actors are launched in the Trainable (i.e., with a RLlib trainer), be sure to kill the Ray actor process here. This process should be lightweight. Per default, You can kill a Ray actor by calling `ray.kill(actor)` on the actor or removing all references to it and waiting for garbage collection .. versionadded:: 0.8.7 """ pass
[docs] def _export_model(self, export_formats: List[str], export_dir: str): """Subclasses should override this to export model. Args: export_formats: List of formats that should be exported. export_dir: Directory to place exported models. Return: A dict that maps ExportFormats to successfully exported models. """ return {}
def _implements_method(self, key): return hasattr(self, key) and callable(getattr(self, key))