Source code for ray.tune.integration.ray_train
from typing import Any, Dict, List, Optional
from ray.train import Checkpoint as RayTrainCheckpoint
from ray.train._internal.session import get_session
from ray.train.v2._internal.execution.context import TrainRunContext
from ray.train.v2.api.callback import UserCallback
from ray.tune.trainable.trainable_fn_utils import _in_tune_session
from ray.util.annotations import DeveloperAPI
CHECKPOINT_PATH_KEY = "checkpoint_path"
[docs]
@DeveloperAPI
class TuneReportCallback(UserCallback):
"""Propagate metrics and checkpoint paths from Ray Train workers to Ray Tune."""
def __init__(self):
if not _in_tune_session():
raise RuntimeError("TuneReportCallback must be used in a Tune session.")
self._training_actor_item_queue = (
get_session()._get_or_create_inter_actor_queue()
)
def after_report(
self,
run_context: TrainRunContext,
metrics: List[Dict[str, Any]],
checkpoint: Optional[RayTrainCheckpoint],
):
# TODO: This can be changed to aggregate the metrics from all workers.
# For now, just achieve feature parity with the old Tune+Train integration.
metrics = metrics[0].copy()
# If a checkpoint is provided, add the checkpoint path to the metrics.
# Don't report the checkpoint again since it's already been uploaded
# to storage.
if checkpoint:
metrics[CHECKPOINT_PATH_KEY] = checkpoint.path
self._training_actor_item_queue.put(metrics)