import abc
import copy
import inspect
import json
import logging
import os
import warnings
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 merge_dicts
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 ray.train._internal.storage import _exists_at_fs_path, get_fs_and_path
from ray.train.constants import TRAIN_DATASET_KEY
from ray.util import PublicAPI
from ray.util.annotations import DeveloperAPI
if TYPE_CHECKING:
from ray.data import Dataset
from ray.data.preprocessor import Preprocessor
from ray.tune import Trainable
_TRAINER_PKL = "trainer.pkl"
# A type representing either a ray.data.Dataset or a function that returns a
# ray.data.Dataset and accepts no arguments.
GenDataset = Union["Dataset", Callable[[], "Dataset"]]
logger = logging.getLogger(__name__)
PREPROCESSOR_DEPRECATION_MESSAGE = (
"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 "
"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#preprocessing-structured-data " # noqa:E501
)
@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."
)
[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/usage.py`.
**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 ``trainer.fit()``, 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.preprocess_datasets()``: The datasets passed to the Trainer will be
setup here.
- ``trainer.train_loop()``: Executes the main training logic.
- Calling ``trainer.fit()`` 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.
train.report({"loss": loss, "epoch": epoch_idx})
# Initialize the Trainer, and call Trainer.fit()
import ray
train_dataset = ray.data.from_items(
[{"x": i, "y": i} for i in range(10)])
my_trainer = MyPytorchTrainer(datasets={"train": train_dataset})
result = my_trainer.fit()
.. 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,
# Deprecated.
preprocessor: Optional["Preprocessor"] = None,
):
self.scaling_config = (
scaling_config if scaling_config is not None else ScalingConfig()
)
self.run_config = run_config if run_config is not None else RunConfig()
self.metadata = metadata
self.datasets = datasets if datasets is not None else {}
self.preprocessor = preprocessor
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)
if preprocessor is not None:
raise DeprecationWarning(PREPROCESSOR_DEPRECATION_MESSAGE)
[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,
preprocessor: Optional["Preprocessor"] = 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": ray.data.from_items([{"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 = trainer.fit()
.. 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 "
"`trainer.fit()`."
)
fs, fs_path = get_fs_and_path(path, storage_filesystem)
with fs.open_input_file(os.path.join(fs_path, _TRAINER_PKL)) 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 no preprocessor is re-specified, then it will be set to None
# here and loaded from the latest checkpoint
param_dict["preprocessor"] = preprocessor
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)
return _exists_at_fs_path(fs, os.path.join(fs_path, _TRAINER_PKL))
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"`ray.data.Dataset` objects, "
f"found {type(self.datasets)} with value `{self.datasets}`."
)
else:
for key, dataset in self.datasets.items():
if not isinstance(dataset, ray.data.Dataset) and not callable(dataset):
raise ValueError(
f"The Dataset under '{key}' key is not a "
"`ray.data.Dataset`. "
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}"
)
# Preprocessor
if self.preprocessor is not None and not isinstance(
self.preprocessor, ray.data.Preprocessor
):
raise ValueError(
f"`preprocessor` should be an instance of `ray.data.Preprocessor`, "
f"found {type(self.preprocessor)} with value `{self.preprocessor}`."
)
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:
"""Called during fit() to preprocess dataset attributes with preprocessor.
.. note:: This method is run on a remote process.
This method is called prior to entering the training_loop.
If the ``Trainer`` has both a datasets dict and
a preprocessor, the datasets dict contains a training dataset (denoted by
the "train" key), and the preprocessor has not yet
been fit, then it will be fit on the train dataset.
Then, all Trainer's datasets will be transformed by the preprocessor.
The transformed datasets will be set back in the ``self.datasets`` attribute
of the Trainer to be used when overriding ``training_loop``.
"""
# Evaluate all datasets.
self.datasets = {k: d() if callable(d) else d for k, d in self.datasets.items()}
if self.preprocessor:
train_dataset = self.datasets.get(TRAIN_DATASET_KEY, None)
if train_dataset and self.preprocessor.fit_status() in (
ray.data.Preprocessor.FitStatus.NOT_FITTED,
ray.data.Preprocessor.FitStatus.PARTIALLY_FITTED,
):
self.preprocessor.fit(train_dataset)
# Execute dataset transformations serially for now.
# Cannot execute them in remote tasks due to dataset ownership model:
# if datasets are created on a remote node, then if that node fails,
# we cannot recover the dataset.
new_datasets = {}
for key, dataset in self.datasets.items():
new_datasets[key] = self.preprocessor.transform(dataset)
self.datasets = new_datasets
[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 preprocessed by ``self.preprocessor``.
You can use the :ref:`Ray Train utilities <train-loop-api>`
(:func:`train.report() <ray.train.report>` 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):
...
train.report({"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 TuneError
from ray.tune.tuner import Tuner, TunerInternal
trainable = self.as_trainable()
param_space = self._extract_fields_for_tuner_param_space()
if self._restore_path:
tuner = Tuner.restore(
path=self._restore_path,
trainable=trainable,
param_space=param_space,
resume_unfinished=True,
resume_errored=True,
storage_filesystem=self._restore_storage_filesystem,
)
else:
tuner = Tuner(
trainable=trainable,
param_space=param_space,
run_config=self.run_config,
_entrypoint=AirEntrypoint.TRAINER,
)
experiment_local_path, _ = TunerInternal.setup_create_experiment_checkpoint_dir(
trainable, self.run_config
)
experiment_local_path = Path(experiment_local_path)
self._save(experiment_local_path)
restore_msg = TrainingFailedError._RESTORE_MSG.format(
trainer_cls_name=self.__class__.__name__,
path=str(experiment_local_path),
)
try:
result_grid = tuner.fit()
except TuneError as e:
# Catch any `TuneError`s raised by the `Tuner.fit` 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, experiment_path: Union[str, Path]):
"""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)
experiment_path = Path(experiment_path)
with open(experiment_path / _TRAINER_PKL, "wb") as fp:
pickle.dump(cls_and_param_dict, fp)
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
def train_func(config):
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`.
trainer.setup()
trainer.preprocess_datasets()
trainer.training_loop()
# 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_func.__name__ = trainer_cls.__name__
trainable_cls = wrap_function(train_func, warn=False)
has_base_dataset = bool(self.datasets)
if has_base_dataset:
from ray.data.context 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)
# Create a new config by merging the dicts.
# 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 = merge_dicts(base_config, self.config)
self._merged_config["run_config"] = run_config
merged_scaling_config = self._merged_config.get("scaling_config")
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
if scaling_config:
scaling_config = trainer_cls._validate_scaling_config(
scaling_config
)
scaling_config_from_trial_resources = (
ScalingConfig.from_placement_group_factory(trial_resources)
)
# This check should always pass if ResourceChangingScheduler is not
# used.
if scaling_config_from_trial_resources != scaling_config:
scaling_config = trainer_cls._validate_scaling_config(
scaling_config_from_trial_resources
)
return scaling_config
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)