Source code for ray.tune.trainable.util

import glob
import inspect
import logging
import os
import shutil
import types
from typing import Any, Callable, Dict, Optional, Type, Union, TYPE_CHECKING

import pandas as pd

import ray
import ray.cloudpickle as pickle
from ray.tune.execution.placement_groups import (
    PlacementGroupFactory,
    resource_dict_to_pg_factory,
)
from ray.tune.registry import _ParameterRegistry
from ray.tune.resources import Resources
from ray.tune.utils import _detect_checkpoint_function
from ray.util import placement_group
from ray.util.annotations import DeveloperAPI, PublicAPI

if TYPE_CHECKING:
    from ray.tune.trainable import Trainable

logger = logging.getLogger(__name__)


_TUNE_METADATA_FILENAME = ".tune_metadata"


@DeveloperAPI
class TrainableUtil:
    @staticmethod
    def write_metadata(checkpoint_dir: str, metadata: Dict) -> None:
        with open(os.path.join(checkpoint_dir, _TUNE_METADATA_FILENAME), "wb") as f:
            pickle.dump(metadata, f)

    @staticmethod
    def load_metadata(checkpoint_dir: str) -> Dict:
        with open(os.path.join(checkpoint_dir, _TUNE_METADATA_FILENAME), "rb") as f:
            return pickle.load(f)

    @staticmethod
    def pickle_checkpoint(checkpoint_path: str):
        """Pickles checkpoint data."""
        checkpoint_dir = TrainableUtil.find_checkpoint_dir(checkpoint_path)
        data = {}
        for basedir, _, file_names in os.walk(checkpoint_dir):
            for file_name in file_names:
                path = os.path.join(basedir, file_name)
                with open(path, "rb") as f:
                    data[os.path.relpath(path, checkpoint_dir)] = f.read()
        # Use normpath so that a directory path isn't mapped to empty string.
        name = os.path.relpath(os.path.normpath(checkpoint_path), checkpoint_dir)
        name += os.path.sep if os.path.isdir(checkpoint_path) else ""
        data_dict = pickle.dumps(
            {
                "checkpoint_name": name,
                "data": data,
            }
        )
        return data_dict

    @staticmethod
    def find_checkpoint_dir(checkpoint_path):
        """Returns the directory containing the checkpoint path.

        Raises:
            FileNotFoundError if the directory is not found.
        """
        if not os.path.exists(checkpoint_path):
            raise FileNotFoundError("Path does not exist", checkpoint_path)
        if os.path.isdir(checkpoint_path):
            checkpoint_dir = checkpoint_path
        else:
            checkpoint_dir = os.path.dirname(checkpoint_path)
        while checkpoint_dir != os.path.dirname(checkpoint_dir):
            if os.path.exists(os.path.join(checkpoint_dir, ".is_checkpoint")):
                break
            checkpoint_dir = os.path.dirname(checkpoint_dir)
        else:
            raise FileNotFoundError(
                "Checkpoint directory not found for {}".format(checkpoint_path)
            )
        return os.path.normpath(checkpoint_dir)

    @staticmethod
    def find_rel_checkpoint_dir(logdir, checkpoint_path):
        """Returns the (relative) directory name of the checkpoint.

        Note, the assumption here is `logdir` should be the prefix of
        `checkpoint_path`.
        For example, returns `checkpoint00000`.
        """
        assert checkpoint_path.startswith(
            logdir
        ), "expecting `logdir` to be a prefix of `checkpoint_path`"
        rel_path = os.path.relpath(checkpoint_path, logdir)
        tokens = rel_path.split(os.sep)
        return os.path.join(tokens[0])

    @staticmethod
    def make_checkpoint_dir(
        checkpoint_dir: str, index: Union[int, str], override: bool = False
    ):
        """Creates a checkpoint directory within the provided path.

        Args:
            checkpoint_dir: Path to checkpoint directory.
            index: A subdirectory will be created
                at the checkpoint directory named 'checkpoint_{index}'.
            override: Deletes checkpoint_dir before creating
                a new one.
        """
        suffix = "checkpoint"
        if index is not None:
            suffix += f"_{index:06d}" if isinstance(index, int) else f"_{index}"
        checkpoint_dir = os.path.join(checkpoint_dir, suffix)

        if override and os.path.exists(checkpoint_dir):
            shutil.rmtree(checkpoint_dir)
        os.makedirs(checkpoint_dir, exist_ok=True)

        TrainableUtil.mark_as_checkpoint_dir(checkpoint_dir)

        return checkpoint_dir

    @staticmethod
    def mark_as_checkpoint_dir(checkpoint_dir: str):
        """Drop marker in directory to identify it as a checkpoint dir."""
        open(os.path.join(checkpoint_dir, ".is_checkpoint"), "a").close()

    @staticmethod
    def get_checkpoints_paths(logdir):
        """Finds the checkpoints within a specific folder.

        Returns a pandas DataFrame of training iterations and checkpoint
        paths within a specific folder.

        Raises:
            FileNotFoundError if the directory is not found.
        """
        marker_paths = glob.glob(
            os.path.join(glob.escape(logdir), "checkpoint_*/.is_checkpoint")
        )
        iter_chkpt_pairs = []
        for marker_path in marker_paths:
            chkpt_dir = os.path.dirname(marker_path)

            # Skip temporary checkpoints
            if os.path.basename(chkpt_dir).startswith("checkpoint_tmp"):
                continue

            metadata_file = glob.glob(
                os.path.join(glob.escape(chkpt_dir), f"*{_TUNE_METADATA_FILENAME}")
            )
            # glob.glob: filenames starting with a dot are special cases
            # that are not matched by '*' and '?' patterns.
            metadata_file += glob.glob(
                os.path.join(glob.escape(chkpt_dir), _TUNE_METADATA_FILENAME)
            )
            metadata_file = list(set(metadata_file))  # avoid duplication
            if len(metadata_file) != 1:
                raise ValueError(
                    "{} has zero or more than one tune_metadata.".format(chkpt_dir)
                )

            metadata_file = metadata_file[0]

            try:
                with open(metadata_file, "rb") as f:
                    metadata = pickle.load(f)
            except Exception as e:
                logger.warning(f"Could not read metadata from checkpoint: {e}")
                metadata = {}

            chkpt_path = metadata_file[: -len(_TUNE_METADATA_FILENAME)]
            chkpt_iter = metadata.get("iteration", -1)
            iter_chkpt_pairs.append([chkpt_iter, chkpt_path])

        chkpt_df = pd.DataFrame(
            iter_chkpt_pairs, columns=["training_iteration", "chkpt_path"]
        )
        return chkpt_df


@DeveloperAPI
class PlacementGroupUtil:
    @staticmethod
    def get_remote_worker_options(
        num_workers: int,
        num_cpus_per_worker: int,
        num_gpus_per_worker: int,
        num_workers_per_host: Optional[int],
        timeout_s: Optional[int],
    ) -> (Dict[str, Any], placement_group):
        """Returns the option for remote workers.

        Args:
            num_workers: Number of training workers to include in
                world.
            num_cpus_per_worker: Number of CPU resources to reserve
                per training worker.
            num_gpus_per_worker: Number of GPU resources to reserve
                per training worker.
            num_workers_per_host: Optional[int]: Number of workers to
                colocate per host.
            timeout_s: Seconds before the torch process group
                times out. Useful when machines are unreliable. Defaults
                to 60 seconds. This value is also reused for triggering
                placement timeouts if forcing colocation.


        Returns:
            type: option that contains CPU/GPU count of
                the remote worker and the placement group information.
            pg: return a reference to the placement group
        """
        pg = None
        options = dict(num_cpus=num_cpus_per_worker, num_gpus=num_gpus_per_worker)
        if num_workers_per_host:
            num_hosts = int(num_workers / num_workers_per_host)
            cpus_per_node = num_cpus_per_worker * num_workers_per_host
            gpus_per_node = num_gpus_per_worker * num_workers_per_host
            bundle = {"CPU": cpus_per_node, "GPU": gpus_per_node}

            all_bundles = [bundle] * num_hosts
            pg = placement_group(all_bundles, strategy="STRICT_SPREAD")
            logger.debug("Waiting for placement_group to start.")
            ray.get(pg.ready(), timeout=timeout_s)
            logger.debug("Placement_group started.")
            options["placement_group"] = pg

        return options, pg


[docs]@PublicAPI(stability="beta") def with_parameters(trainable: Union[Type["Trainable"], Callable], **kwargs): """Wrapper for trainables to pass arbitrary large data objects. This wrapper function will store all passed parameters in the Ray object store and retrieve them when calling the function. It can thus be used to pass arbitrary data, even datasets, to Tune trainables. This can also be used as an alternative to ``functools.partial`` to pass default arguments to trainables. When used with the function API, the trainable function is called with the passed parameters as keyword arguments. When used with the class API, the ``Trainable.setup()`` method is called with the respective kwargs. If the data already exists in the object store (are instances of ObjectRef), using ``tune.with_parameters()`` is not necessary. You can instead pass the object refs to the training function via the ``config`` or use Python partials. Args: trainable: Trainable to wrap. **kwargs: parameters to store in object store. Function API example: .. code-block:: python from ray import tune from ray.air import session def train(config, data=None): for sample in data: loss = update_model(sample) session.report(loss=loss) data = HugeDataset(download=True) tuner = Tuner( tune.with_parameters(train, data=data), # ... ) tuner.fit() Class API example: .. code-block:: python from ray import tune class MyTrainable(tune.Trainable): def setup(self, config, data=None): self.data = data self.iter = iter(self.data) self.next_sample = next(self.iter) def step(self): loss = update_model(self.next_sample) try: self.next_sample = next(self.iter) except StopIteration: return {"loss": loss, done: True} return {"loss": loss} data = HugeDataset(download=True) tuner = Tuner( tune.with_parameters(MyTrainable, data=data), # ... ) """ from ray.tune.trainable import Trainable if not callable(trainable) or ( inspect.isclass(trainable) and not issubclass(trainable, Trainable) ): raise ValueError( f"`tune.with_parameters() only works with function trainables " f"or classes that inherit from `tune.Trainable()`. Got type: " f"{type(trainable)}." ) parameter_registry = _ParameterRegistry() ray._private.worker._post_init_hooks.append(parameter_registry.flush) # Objects are moved into the object store prefix = f"{str(trainable)}_" for k, v in kwargs.items(): parameter_registry.put(prefix + k, v) trainable_name = getattr(trainable, "__name__", "tune_with_parameters") if inspect.isclass(trainable): # Class trainable keys = list(kwargs.keys()) class _Inner(trainable): def setup(self, config): setup_kwargs = {} for k in keys: setup_kwargs[k] = parameter_registry.get(prefix + k) super(_Inner, self).setup(config, **setup_kwargs) # Workaround for actor name not being logged correctly # if __repr__ is not directly defined in a class. def __repr__(self): return super().__repr__() _Inner.__name__ = trainable_name return _Inner else: # Function trainable use_checkpoint = _detect_checkpoint_function(trainable, partial=True) keys = list(kwargs.keys()) def inner(config, checkpoint_dir=None): fn_kwargs = {} if use_checkpoint: default = checkpoint_dir sig = inspect.signature(trainable) if "checkpoint_dir" in sig.parameters: default = sig.parameters["checkpoint_dir"].default or default fn_kwargs["checkpoint_dir"] = default for k in keys: fn_kwargs[k] = parameter_registry.get(prefix + k) trainable(config, **fn_kwargs) inner.__name__ = trainable_name # Use correct function signature if no `checkpoint_dir` parameter # is set if not use_checkpoint: def _inner(config): inner(config, checkpoint_dir=None) _inner.__name__ = trainable_name if hasattr(trainable, "__mixins__"): _inner.__mixins__ = trainable.__mixins__ return _inner if hasattr(trainable, "__mixins__"): inner.__mixins__ = trainable.__mixins__ return inner
@PublicAPI(stability="beta") def with_resources( trainable: Union[Type["Trainable"], Callable], resources: Union[ Dict[str, float], PlacementGroupFactory, Callable[[dict], PlacementGroupFactory] ], ): """Wrapper for trainables to specify resource requests. This wrapper allows specification of resource requirements for a specific trainable. It will override potential existing resource requests (use with caution!). The main use case is to request resources for function trainables when used with the Tuner() API. Class trainables should usually just implement the ``default_resource_request()`` method. Args: trainable: Trainable to wrap. resources: Resource dict, placement group factory, or callable that takes in a config dict and returns a placement group factory. Example: .. code-block:: python from ray import tune from ray.tune.tuner import Tuner def train(config): return len(ray.get_gpu_ids()) # Returns 2 tuner = Tuner( tune.with_resources(train, resources={"gpu": 2}), # ... ) results = tuner.fit() """ from ray.tune.trainable import Trainable if not callable(trainable) or ( inspect.isclass(trainable) and not issubclass(trainable, Trainable) ): raise ValueError( f"`tune.with_parameters() only works with function trainables " f"or classes that inherit from `tune.Trainable()`. Got type: " f"{type(trainable)}." ) if isinstance(resources, PlacementGroupFactory): pgf = resources elif isinstance(resources, dict): pgf = resource_dict_to_pg_factory(resources) elif callable(resources): pgf = resources else: raise ValueError( f"Invalid resource type for `with_resources()`: {type(resources)}" ) if not inspect.isclass(trainable): if isinstance(trainable, types.MethodType): # Methods cannot set arbitrary attributes, so we have to wrap them use_checkpoint = _detect_checkpoint_function(trainable, partial=True) if use_checkpoint: def _trainable(config, checkpoint_dir): return trainable(config, checkpoint_dir=checkpoint_dir) else: def _trainable(config): return trainable(config) _trainable._resources = pgf return _trainable # Just set an attribute. This will be resolved later in `wrap_function()`. try: trainable._resources = pgf except AttributeError as e: raise RuntimeError( "Could not use `tune.with_resources()` on the supplied trainable. " "Wrap your trainable in a regular function before passing it " "to Ray Tune." ) from e else: class ResourceTrainable(trainable): @classmethod def default_resource_request( cls, config: Dict[str, Any] ) -> Optional[Union[Resources, PlacementGroupFactory]]: if not isinstance(pgf, PlacementGroupFactory) and callable(pgf): return pgf(config) return pgf ResourceTrainable.__name__ = trainable.__name__ trainable = ResourceTrainable return trainable