Source code for ray.train.xgboost._xgboost_utils

import tempfile
from collections import OrderedDict
from contextlib import contextmanager
from pathlib import Path
from typing import Callable, Dict, List, Optional, Union

from xgboost.core import Booster

import ray.train
from ray.train import Checkpoint
from ray.tune.utils import flatten_dict
from ray.util.annotations import PublicAPI

    from xgboost.callback import TrainingCallback
except ImportError:

    class TrainingCallback:

class TuneCallback(TrainingCallback):
    # TODO(justinvyu): [code_removal] Remove this after enforcing min xgboost version.
    """Base class for Tune's XGBoost callbacks."""

    def __call__(self, env):
        """Compatibility with xgboost<1.3"""
        return self.after_iteration(
            env.model, env.iteration, env.evaluation_result_list

    def after_iteration(self, model: Booster, epoch: int, evals_log: Dict):
        raise NotImplementedError

[docs]@PublicAPI(stability="beta") class RayTrainReportCallback(TuneCallback): """XGBoost callback to save checkpoints and report metrics. Args: metrics: Metrics to report. If this is a list, each item describes the metric key reported to XGBoost, and it will be reported under the same name. This can also be a dict of {<key-to-report>: <xgboost-metric-key>}, which can be used to rename xgboost default metrics. filename: Customize the saved checkpoint file type by passing a filename. Defaults to "model.ubj". frequency: How often to save checkpoints, in terms of iterations. Defaults to 0 (no checkpoints are saved during training). checkpoint_at_end: Whether or not to save a checkpoint at the end of training. results_postprocessing_fn: An optional Callable that takes in the metrics dict that will be reported (after it has been flattened) and returns a modified dict. For example, this can be used to average results across CV fold when using ````. Examples -------- Reporting checkpoints and metrics to Ray Tune when running many independent xgboost trials (without data parallelism within a trial). .. testcode:: :skipif: True import xgboost from ray.tune import Tuner from ray.train.xgboost import RayTrainReportCallback def train_fn(config): # Report log loss to Ray Tune after each validation epoch. bst = xgboost.train( ..., callbacks=[ RayTrainReportCallback( metrics={"loss": "eval-logloss"}, frequency=1 ) ], ) tuner = Tuner(train_fn) results = Loading a model from a checkpoint reported by this callback. .. testcode:: :skipif: True from ray.train.xgboost import RayTrainReportCallback # Get a `Checkpoint` object that is saved by the callback during training. result = booster = RayTrainReportCallback.get_model(result.checkpoint) """ CHECKPOINT_NAME = "model.ubj" def __init__( self, metrics: Optional[Union[str, List[str], Dict[str, str]]] = None, filename: str = CHECKPOINT_NAME, frequency: int = 0, checkpoint_at_end: bool = True, results_postprocessing_fn: Optional[ Callable[[Dict[str, Union[float, List[float]]]], Dict[str, float]] ] = None, ): if isinstance(metrics, str): metrics = [metrics] self._metrics = metrics self._filename = filename self._frequency = frequency self._checkpoint_at_end = checkpoint_at_end self._results_postprocessing_fn = results_postprocessing_fn # Keeps track of the eval metrics from the last iteration, # so that the latest metrics can be reported with the checkpoint # at the end of training. self._evals_log = None # Keep track of the last checkpoint iteration to avoid double-checkpointing # when using `checkpoint_at_end=True`. self._last_checkpoint_iteration = None
[docs] @classmethod def get_model( cls, checkpoint: Checkpoint, filename: str = CHECKPOINT_NAME ) -> Booster: """Retrieve the model stored in a checkpoint reported by this callback. Args: checkpoint: The checkpoint object returned by a training run. The checkpoint should be saved by an instance of this callback. filename: The filename to load the model from, which should match the filename used when creating the callback. """ with checkpoint.as_directory() as checkpoint_path: booster = Booster() booster.load_model(Path(checkpoint_path, filename).as_posix()) return booster
def _get_report_dict(self, evals_log): if isinstance(evals_log, OrderedDict): # xgboost>=1.3 result_dict = flatten_dict(evals_log, delimiter="-") for k in list(result_dict): result_dict[k] = result_dict[k][-1] else: # xgboost<1.3 result_dict = dict(evals_log) if not self._metrics: report_dict = result_dict else: report_dict = {} for key in self._metrics: if isinstance(self._metrics, dict): metric = self._metrics[key] else: metric = key report_dict[key] = result_dict[metric] if self._results_postprocessing_fn: report_dict = self._results_postprocessing_fn(report_dict) return report_dict @contextmanager def _get_checkpoint(self, model: Booster) -> Optional[Checkpoint]: # NOTE: The world rank returns None for Tune usage without Train. if ray.train.get_context().get_world_rank() in (0, None): with tempfile.TemporaryDirectory() as temp_checkpoint_dir: model.save_model(Path(temp_checkpoint_dir, self._filename).as_posix()) yield Checkpoint(temp_checkpoint_dir) else: yield None def after_iteration(self, model: Booster, epoch: int, evals_log: Dict): self._evals_log = evals_log checkpointing_disabled = self._frequency == 0 # Ex: if frequency=2, checkpoint at epoch 1, 3, 5, ... (counting from 0) should_checkpoint = ( not checkpointing_disabled and (epoch + 1) % self._frequency == 0 ) report_dict = self._get_report_dict(evals_log) if should_checkpoint: self._last_checkpoint_iteration = epoch with self._get_checkpoint(model=model) as checkpoint:, checkpoint=checkpoint) else: def after_training(self, model: Booster) -> Booster: if not self._checkpoint_at_end: return model if ( self._last_checkpoint_iteration is not None and model.num_boosted_rounds() - 1 == self._last_checkpoint_iteration ): # Avoids a duplicate checkpoint if the checkpoint frequency happens # to align with the last iteration. return model report_dict = self._get_report_dict(self._evals_log) if self._evals_log else {} with self._get_checkpoint(model=model) as checkpoint:, checkpoint=checkpoint) return model