Source code for ray.train.base_trainer

import abc
import copy
import inspect
import json
import logging
import os
import warnings
from functools import partial
from pathlib import Path
from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Type, Union

import pyarrow.fs

import ray
import ray.cloudpickle as pickle
from ray._private.dict import deep_update
from ray.air._internal import usage as air_usage
from ray.air._internal.config import ensure_only_allowed_dataclass_keys_updated
from ray.air._internal.usage import AirEntrypoint
from ray.air.config import RunConfig, ScalingConfig
from ray.air.result import Result
from ray.train import Checkpoint
from ray.train._internal.session import _get_session
from import (
from ray.util import PublicAPI
from ray.util.annotations import DeveloperAPI

    from import Dataset
    from ray.tune import Trainable

_TRAINER_PKL = "trainer.pkl"

# A type representing either a or a function that returns a
# and accepts no arguments.
GenDataset = Union["Dataset", Callable[[], "Dataset"]]

logger = logging.getLogger(__name__)

    "The `preprocessor` argument to Trainers is deprecated as of Ray 2.7. "
    "Instead, use the Preprocessor `fit` and `transform` APIs directly on the Ray "
    "Dataset. For any state that needs to be saved to the trained checkpoint, pass it "
    "in using the `metadata` argument of the `Trainer`. "
    "For a full example, see "
    " "  # noqa:E501

[docs]@PublicAPI(stability="beta") class TrainingFailedError(RuntimeError): """An error indicating that training has failed.""" _RESTORE_MSG = ( "The Ray Train run failed. Please inspect the previous error messages for a " "cause. After fixing the issue (assuming that the error is not caused by " "your own application logic, but rather an error such as OOM), you can restart " "the run from scratch or continue this run.\n" "To continue this run, you can use: " '`trainer = {trainer_cls_name}.restore("{path}")`.' ) _FAILURE_CONFIG_MSG = ( "To start a new run that will retry on training failures, set " "`train.RunConfig(failure_config=train.FailureConfig(max_failures))` " "in the Trainer's `run_config` with `max_failures > 0`, or `max_failures = -1` " "for unlimited retries." )
def _train_coordinator_fn( config: dict, trainer_cls: Type["BaseTrainer"], metadata: dict ): """This is the function that defines the logic of the Ray Train coordinator. This is responsible for setting up a remote instance of the `trainer_cls` (a different instance than the one calling `` on the driver!) and running the training loop. """ assert metadata is not None, metadata # Propagate user metadata from the Trainer constructor. _get_session().metadata = metadata # config already contains merged values. # Instantiate new Trainer in Trainable. trainer = trainer_cls(**config) # Get the checkpoint from Tune and pass it to workers later on. checkpoint = ray.train.get_checkpoint() if checkpoint: # Set `starting_checkpoint` for auto-recovery fault-tolerance # as well as manual restoration. trainer.starting_checkpoint = checkpoint # else: Train will restore from the user-provided # `resume_from_checkpoint` == `starting_checkpoint`. # Evaluate datasets if they are wrapped in a factory. trainer.datasets = { k: d() if callable(d) else d for k, d in trainer.datasets.items() } trainer.setup() trainer.training_loop()
[docs]@DeveloperAPI class BaseTrainer(abc.ABC): """Defines interface for distributed training on Ray. Note: The base ``BaseTrainer`` class cannot be instantiated directly. Only one of its subclasses can be used. Note to developers: If a new trainer is added, please update `air/_internal/`. **How does a trainer work?** - First, initialize the Trainer. The initialization runs locally, so heavyweight setup should not be done in ``__init__``. - Then, when you call ````, the Trainer is serialized and copied to a remote Ray actor. The following methods are then called in sequence on the remote actor. - ``trainer.setup()``: Any heavyweight Trainer setup should be specified here. - ``trainer.training_loop()``: Executes the main training logic. - Calling ```` will return a ``ray.result.Result`` object where you can access metrics from your training run, as well as any checkpoints that may have been saved. **How do I create a new Trainer?** Subclass ``ray.train.trainer.BaseTrainer``, and override the ``training_loop`` method, and optionally ``setup``. .. testcode:: import torch from ray.train.trainer import BaseTrainer from ray import train, tune class MyPytorchTrainer(BaseTrainer): def setup(self): self.model = torch.nn.Linear(1, 1) self.optimizer = torch.optim.SGD( self.model.parameters(), lr=0.1) def training_loop(self): # You can access any Trainer attributes directly in this method. # self.datasets["train"] has already been dataset = self.datasets["train"] torch_ds = dataset.iter_torch_batches(dtypes=torch.float) loss_fn = torch.nn.MSELoss() for epoch_idx in range(10): loss = 0 num_batches = 0 torch_ds = dataset.iter_torch_batches( dtypes=torch.float, batch_size=2 ) for batch in torch_ds: X = torch.unsqueeze(batch["x"], 1) y = torch.unsqueeze(batch["y"], 1) # Compute prediction error pred = self.model(X) batch_loss = loss_fn(pred, y) # Backpropagation self.optimizer.zero_grad() batch_loss.backward() self.optimizer.step() loss += batch_loss.item() num_batches += 1 loss /= num_batches # Use Tune functions to report intermediate # results.{"loss": loss, "epoch": epoch_idx}) # Initialize the Trainer, and call import ray train_dataset = [{"x": i, "y": i} for i in range(10)]) my_trainer = MyPytorchTrainer(datasets={"train": train_dataset}) result = .. testoutput:: :hide: ... Args: scaling_config: Configuration for how to scale training. run_config: Configuration for the execution of the training run. datasets: Any Datasets to use for training. Use the key "train" to denote which dataset is the training dataset. metadata: Dict that should be made available via `train.get_context().get_metadata()` and in `checkpoint.get_metadata()` for checkpoints saved from this Trainer. Must be JSON-serializable. resume_from_checkpoint: A checkpoint to resume training from. """ _scaling_config_allowed_keys: List[str] = [ "trainer_resources", ] _handles_checkpoint_freq: bool = False _handles_checkpoint_at_end: bool = False # fields to propagate to Tuner param_space. # See `BaseTrainer._extract_fields_for_tuner_param_space` for more details. _fields_for_tuner_param_space = [] def __init__( self, *, scaling_config: Optional[ScalingConfig] = None, run_config: Optional[RunConfig] = None, datasets: Optional[Dict[str, GenDataset]] = None, metadata: Optional[Dict[str, Any]] = None, resume_from_checkpoint: Optional[Checkpoint] = None, ): self.scaling_config = ( scaling_config if scaling_config is not None else ScalingConfig() ) self.run_config = ( copy.copy(run_config) if run_config is not None else RunConfig() ) self.metadata = metadata self.datasets = datasets if datasets is not None else {} self.starting_checkpoint = resume_from_checkpoint # These attributes should only be set through `BaseTrainer.restore` self._restore_path = None self._restore_storage_filesystem = None self._validate_attributes() air_usage.tag_air_trainer(self)
[docs] @PublicAPI(stability="alpha") @classmethod def restore( cls: Type["BaseTrainer"], path: Union[str, os.PathLike], storage_filesystem: Optional[pyarrow.fs.FileSystem] = None, datasets: Optional[Dict[str, GenDataset]] = None, scaling_config: Optional[ScalingConfig] = None, **kwargs, ) -> "BaseTrainer": """Restores a Train experiment from a previously interrupted/failed run. Restore should be used for experiment-level fault tolerance in the event that the head node crashes (e.g., OOM or some other runtime error) or the entire cluster goes down (e.g., network error affecting all nodes). A run that has already completed successfully will not be resumed from this API. To continue training from a successful run, launch a new run with the ``<Framework>Trainer(resume_from_checkpoint)`` API instead, passing in a checkpoint from the previous run to start with. .. note:: Restoring an experiment from a path that's pointing to a *different* location than the original experiment path is supported. However, Ray Train assumes that the full experiment directory is available (including checkpoints) so that it's possible to resume trials from their latest state. For example, if the original experiment path was run locally, then the results are uploaded to cloud storage, Ray Train expects the full contents to be available in cloud storage if attempting to resume via ``<Framework>Trainer.restore("s3://...")``. The restored run will continue writing results to the same cloud storage location. The following example can be paired with implementing job retry using :ref:`Ray Jobs <jobs-overview>` to produce a Train experiment that will attempt to resume on both experiment-level and trial-level failures: .. testcode:: import os import ray from ray import train from ray.train.trainer import BaseTrainer experiment_name = "unique_experiment_name" storage_path = os.path.expanduser("~/ray_results") experiment_dir = os.path.join(storage_path, experiment_name) # Define some dummy inputs for demonstration purposes datasets = {"train":[{"a": i} for i in range(10)])} class CustomTrainer(BaseTrainer): def training_loop(self): pass if CustomTrainer.can_restore(experiment_dir): trainer = CustomTrainer.restore( experiment_dir, datasets=datasets ) else: trainer = CustomTrainer( datasets=datasets, run_config=train.RunConfig( name=experiment_name, storage_path=storage_path, # Tip: You can also enable retries on failure for # worker-level fault tolerance failure_config=train.FailureConfig(max_failures=3), ), ) result = .. testoutput:: :hide: ... Args: path: The path to the experiment directory of the training run to restore. This can be a local path or a remote URI if the experiment was uploaded to the cloud. storage_filesystem: Custom ``pyarrow.fs.FileSystem`` corresponding to the ``path``. This may be necessary if the original experiment passed in a custom filesystem. datasets: Re-specified datasets used in the original training run. This must include all the datasets that were passed in the original trainer constructor. scaling_config: Optionally re-specified scaling config. This can be modified to be different from the original spec. **kwargs: Other optionally re-specified arguments, passed in by subclasses. Raises: ValueError: If all datasets were not re-supplied on restore. Returns: BaseTrainer: A restored instance of the class that is calling this method. """ if not cls.can_restore(path, storage_filesystem): raise ValueError( f"Invalid restore path: {path}. Make sure that this path exists and " "is the experiment directory that results from a call to " "``." ) fs, fs_path = get_fs_and_path(path, storage_filesystem) trainer_pkl_path = Path(fs_path, _TRAINER_PKL).as_posix() with fs.open_input_file(trainer_pkl_path) as f: trainer_cls, param_dict = pickle.loads(f.readall()) if trainer_cls is not cls: warnings.warn( f"Invalid trainer type. You are attempting to restore a trainer of type" f" {trainer_cls} with `{cls.__name__}.restore`, " "which will most likely fail. " f"Use `{trainer_cls.__name__}.restore` instead." ) original_datasets = param_dict.pop("datasets", {}) if original_datasets and not datasets: raise ValueError( "The following datasets need to be provided again on restore: " f"{list(original_datasets.keys())}\n" f"Use {cls.__name__}.restore(..., datasets=datasets) " "with the datasets that were provided to the original trainer." ) datasets = datasets or {} if set(original_datasets) != set(datasets): raise ValueError( "The provided datasets don't match the original dataset keys.\n" f" Expected datasets for the keys: {list(original_datasets.keys())}\n" f" Actual datasets provided: {list(datasets.keys())}" ) param_dict["datasets"] = datasets if scaling_config: param_dict["scaling_config"] = scaling_config for param_name, val in kwargs.items(): # Overwrite the old value if something is passed into restore if val is not None: param_dict[param_name] = val try: trainer = cls(**param_dict) except Exception as e: raise ValueError( "Trainer restoration failed (see above for the stack trace). " "Make sure that you use the right trainer class to restore: " f"`{cls.__name__}.restore`\n" ) from e trainer._restore_path = path trainer._restore_storage_filesystem = storage_filesystem return trainer
[docs] @PublicAPI(stability="alpha") @classmethod def can_restore( cls: Type["BaseTrainer"], path: Union[str, os.PathLike], storage_filesystem: Optional[pyarrow.fs.FileSystem] = None, ) -> bool: """Checks whether a given directory contains a restorable Train experiment. Args: path: The path to the experiment directory of the Train experiment. This can be either a local directory (e.g., ~/ray_results/exp_name) or a remote URI (e.g., s3://bucket/exp_name). Returns: bool: Whether this path exists and contains the trainer state to resume from """ fs, fs_path = get_fs_and_path(path, storage_filesystem) trainer_pkl_path = Path(fs_path, _TRAINER_PKL).as_posix() return _exists_at_fs_path(fs, trainer_pkl_path)
def __repr__(self): # A dictionary that maps parameters to their default values. default_values: Dict[str, Any] = { "scaling_config": ScalingConfig(), "run_config": RunConfig(), "datasets": {}, "starting_checkpoint": None, } non_default_arguments = [] for parameter, default_value in default_values.items(): value = getattr(self, parameter) if value != default_value: non_default_arguments.append(f"{parameter}={value!r}") if non_default_arguments: return f"<{self.__class__.__name__} {' '.join(non_default_arguments)}>" return f"<{self.__class__.__name__}>" def __new__(cls, *args, **kwargs): # Store the init args as attributes so this can be merged with Tune hparams. trainer = super(BaseTrainer, cls).__new__(cls) parameters = inspect.signature(cls.__init__).parameters parameters = list(parameters.keys()) # Remove self. parameters = parameters[1:] arg_dict = dict(zip(parameters, args)) trainer._param_dict = {**arg_dict, **kwargs} return trainer def _validate_attributes(self): """Called on __init()__ to validate trainer attributes.""" # Run config if not isinstance(self.run_config, RunConfig): raise ValueError( f"`run_config` should be an instance of `ray.train.RunConfig`, " f"found {type(self.run_config)} with value `{self.run_config}`." ) # Scaling config if not isinstance(self.scaling_config, ScalingConfig): raise ValueError( "`scaling_config` should be an instance of `ScalingConfig`, " f"found {type(self.scaling_config)} with value `{self.scaling_config}`." ) # Datasets if not isinstance(self.datasets, dict): raise ValueError( f"`datasets` should be a dict mapping from a string to " f"`` objects, " f"found {type(self.datasets)} with value `{self.datasets}`." ) else: for key, dataset in self.datasets.items(): if not isinstance(dataset, and not callable(dataset): raise ValueError( f"The Dataset under '{key}' key is not a " "``. " f"Received {dataset} instead." ) # Metadata. self.metadata = self.metadata or {} if not isinstance(self.metadata, dict): raise TypeError( f"The provided metadata must be a dict, was {type(self.metadata)}." ) try: self.metadata = json.loads(json.dumps(self.metadata)) except Exception as e: raise ValueError( "The provided metadata must be JSON-serializable: " f"{self.metadata}: {e}" ) if self.starting_checkpoint is not None and not isinstance( self.starting_checkpoint, Checkpoint ): raise ValueError( f"`resume_from_checkpoint` should be an instance of " f"`ray.train.Checkpoint`, found {type(self.starting_checkpoint)} " f"with value `{self.starting_checkpoint}`." ) @classmethod def _validate_scaling_config(cls, scaling_config: ScalingConfig) -> ScalingConfig: """Returns scaling config dataclass after validating updated keys.""" ensure_only_allowed_dataclass_keys_updated( dataclass=scaling_config, allowed_keys=cls._scaling_config_allowed_keys, ) return scaling_config
[docs] def setup(self) -> None: """Called during fit() to perform initial setup on the Trainer. .. note:: This method is run on a remote process. This method will not be called on the driver, so any expensive setup operations should be placed here and not in ``__init__``. This method is called prior to ``preprocess_datasets`` and ``training_loop``. """ pass
[docs] def preprocess_datasets(self) -> None: """Deprecated.""" raise DeprecationWarning( "`preprocess_datasets` is no longer used, since preprocessors " f"are no longer accepted by Trainers.\n{PREPROCESSOR_DEPRECATION_MESSAGE}" )
[docs] @abc.abstractmethod def training_loop(self) -> None: """Loop called by fit() to run training and report results to Tune. .. note:: This method runs on a remote process. ``self.datasets`` have already been evaluated if they were wrapped in a factory. You can use the :ref:`Ray Train utilities <train-loop-api>` (:func:` <>` and :func:`train.get_checkpoint() <ray.train.get_checkpoint>`) inside this training loop. Example: .. testcode:: from ray.train.trainer import BaseTrainer from ray import train class MyTrainer(BaseTrainer): def training_loop(self): for epoch_idx in range(5): ...{"epoch": epoch_idx}) """ raise NotImplementedError
[docs] @PublicAPI(stability="beta") def fit(self) -> Result: """Runs training. Returns: A Result object containing the training result. Raises: TrainingFailedError: If any failures during the execution of ``self.as_trainable()``, or during the Tune execution loop. """ from ray.tune import ResumeConfig, TuneError from ray.tune.tuner import Tuner trainable = self.as_trainable() param_space = self._extract_fields_for_tuner_param_space() = ( or StorageContext.get_experiment_dir_name(trainable) ) # The storage context here is only used to access the resolved # storage fs and experiment path, in order to avoid duplicating that logic. # This is NOT the storage context object that gets passed to remote workers. storage = StorageContext( storage_path=self.run_config.storage_path,, storage_filesystem=self.run_config.storage_filesystem, ) if self._restore_path: tuner = Tuner.restore( path=self._restore_path, trainable=trainable, param_space=param_space, _resume_config=ResumeConfig( finished=ResumeConfig.ResumeType.RESUME, unfinished=ResumeConfig.ResumeType.RESUME, errored=ResumeConfig.ResumeType.RESUME, ), storage_filesystem=self._restore_storage_filesystem, ) else: tuner = Tuner( trainable=trainable, param_space=param_space, run_config=self.run_config, _entrypoint=AirEntrypoint.TRAINER, ) self._save(storage.storage_filesystem, storage.experiment_fs_path) restore_msg = TrainingFailedError._RESTORE_MSG.format( trainer_cls_name=self.__class__.__name__, path=str(storage.experiment_fs_path), ) try: result_grid = except TuneError as e: # Catch any `TuneError`s raised by the `` call. # Unwrap the `TuneError` if needed. parent_error = e.__cause__ or e # Raise it to the user as a `TrainingFailedError` with a message to restore. raise TrainingFailedError(restore_msg) from parent_error # Other exceptions get passed through directly (ex: on `fail_fast='raise'`) assert len(result_grid) == 1 result = result_grid[0] if result.error: # Raise trainable errors to the user with a message to restore # or configure `FailureConfig` in a new run. raise TrainingFailedError( "\n".join([restore_msg, TrainingFailedError._FAILURE_CONFIG_MSG]) ) from result.error return result
def _save(self, fs: pyarrow.fs.FileSystem, experiment_path: str): """Saves the current trainer's class along with the `param_dict` of parameters passed to this trainer's constructor. This is used to recreate the trainer on restore. Unless a parameter is re-specified during restoration (only a subset of parameters can be passed in again), that parameter will be loaded from the saved copy. Datasets should not be saved as part of the state. Instead, we save the keys and replace the dataset values with dummy functions that will raise an error if invoked. The error only serves as a guardrail for misuse (e.g., manually unpickling and constructing the Trainer again) and is not typically surfaced, since datasets must be re-specified upon restoration. """ param_dict = self._param_dict.copy() datasets = param_dict.pop("datasets", {}) def raise_fn(): raise RuntimeError if datasets: param_dict["datasets"] = { dataset_name: raise_fn for dataset_name in datasets } cls_and_param_dict = (self.__class__, param_dict) fs.create_dir(experiment_path) with fs.open_output_stream(Path(experiment_path, _TRAINER_PKL).as_posix()) as f: f.write(pickle.dumps(cls_and_param_dict)) def _extract_fields_for_tuner_param_space(self) -> Dict: """Extracts fields to be included in `Tuner.param_space`. This is needed to leverage the full logging/integration offerings from Tune. For example, `param_space` is logged automatically to wandb integration. Currently only done for `train_loop_config`. Returns: A dictionary that should be passed to Tuner.param_space. """ result = {} for key in self._fields_for_tuner_param_space: if key in self._param_dict.keys(): result[key] = copy.deepcopy(self._param_dict[key]) return result def _generate_trainable_cls(self) -> Type["Trainable"]: """Generates the base Trainable class. Returns: A Trainable class to use for training. """ from ray.tune.execution.placement_groups import PlacementGroupFactory from ray.tune.trainable import wrap_function trainer_cls = self.__class__ scaling_config = self.scaling_config metadata = self.metadata train_coordinator_fn = partial( _train_coordinator_fn, trainer_cls=trainer_cls, metadata=metadata ) # Change the name of the training function to match the name of the Trainer # class. This will mean the Tune trial name will match the name of Trainer on # stdout messages and the results directory. train_coordinator_fn.__name__ = trainer_cls.__name__ trainable_cls = wrap_function(train_coordinator_fn) has_base_dataset = bool(self.datasets) if has_base_dataset: from import DataContext dataset_context = DataContext.get_current() else: dataset_context = None class TrainTrainable(trainable_cls): """Adds default resources to the Trainable.""" _handles_checkpoint_freq = trainer_cls._handles_checkpoint_freq _handles_checkpoint_at_end = trainer_cls._handles_checkpoint_at_end @classmethod def has_base_dataset(cls) -> bool: """Whether a dataset is provided through the Trainer.""" return has_base_dataset @classmethod def base_scaling_config(cls) -> ScalingConfig: """Returns the unchanged scaling config provided through the Trainer.""" return scaling_config def setup(self, config, **kwargs): base_config = dict(kwargs) # Merge Tuner param space hyperparameters in `config` into the # base config passed to the Trainer constructor, which is `base_config`. # `base_config` is pulled from the object store from the usage of # tune.with_parameters in `BaseTrainer.as_trainable`. # run_config is not a tunable hyperparameter so it does not need to be # merged. run_config = base_config.pop("run_config", None) self._merged_config = deep_update( base_config, self.config, new_keys_allowed=True ) self._merged_config["run_config"] = run_config merged_scaling_config = self._merged_config.get( "scaling_config", ScalingConfig() ) if isinstance(merged_scaling_config, dict): merged_scaling_config = ScalingConfig(**merged_scaling_config) self._merged_config[ "scaling_config" ] = self._reconcile_scaling_config_with_trial_resources( merged_scaling_config ) if self.has_base_dataset(): # Set the DataContext on the Trainer actor to the DataContext # specified on the driver. DataContext._set_current(dataset_context) super(TrainTrainable, self).setup(config) def _reconcile_scaling_config_with_trial_resources( self, scaling_config: ScalingConfig ) -> ScalingConfig: """ ResourceChangingScheduler workaround. Ensures that the scaling config matches trial resources. This should be replaced with RCS returning a ScalingConfig in the future. """ trial_resources = self.trial_resources # This will be false if the resources are default if not isinstance(trial_resources, PlacementGroupFactory): return scaling_config # Ignore ResourceChangingScheduler workaround when resource bundles # are unchanged if self.trial_resources == scaling_config.as_placement_group_factory(): return scaling_config trainer_cls._validate_scaling_config(scaling_config) return ScalingConfig.from_placement_group_factory(trial_resources) def _trainable_func(self, config): # We ignore the config passed by Tune and instead use the merged # config which includes the initial Trainer args. super()._trainable_func(self._merged_config) @classmethod def default_resource_request(cls, config): # `config["scaling_config"] is a dataclass when passed via the # `scaling_config` argument in `Trainer` and is a dict when passed # via the `scaling_config` key of `param_spec`. # Conversion logic must be duplicated in `TrainTrainable.__init__` # because this is a class method. updated_scaling_config = config.get("scaling_config", scaling_config) if isinstance(updated_scaling_config, dict): updated_scaling_config = ScalingConfig(**updated_scaling_config) validated_scaling_config = trainer_cls._validate_scaling_config( updated_scaling_config ) return validated_scaling_config.as_placement_group_factory() return TrainTrainable
[docs] def as_trainable(self) -> Type["Trainable"]: """Converts self to a ``tune.Trainable`` class.""" from ray import tune base_config = self._param_dict trainable_cls = self._generate_trainable_cls() # Wrap with `tune.with_parameters` to handle very large values in base_config return tune.with_parameters(trainable_cls, **base_config)