[air] Deprecate some fields/classes that are supposed to be gone in 2.6.

Signed-off-by: xwjiang2010 <xwjiang2010@gmail.com>

python/ray/air/integrations/keras.py

@@ -5,7 +5,7 @@
 import ray
 from ray.train._internal.storage import _use_storage_context
 from ray.train.tensorflow import TensorflowCheckpoint, LegacyTensorflowCheckpoint
-from ray.util.annotations import PublicAPI, Deprecated
+from ray.util.annotations import PublicAPI
 
 
 class _Callback(KerasCallback):
@@ -183,51 +183,3 @@ def _get_reported_metrics(self, logs: Dict) -> Dict:
 
         assert isinstance(reported_metrics, dict)
         return reported_metrics
-
-
-@Deprecated
-class Callback(_Callback):
-    """
-    Keras callback for Ray AIR reporting and checkpointing.
-
-    You can use this in both TuneSession and TrainSession.
-
-    Example:
-        .. code-block: python
-
-            ############# Using it in TrainSession ###############
-            from ray.air.integrations.keras import Callback
-            def train_loop_per_worker():
-                strategy = tf.distribute.MultiWorkerMirroredStrategy()
-                with strategy.scope():
-                    model = build_model()
-                    #model.compile(...)
-                model.fit(dataset_shard, callbacks=[Callback()])
-
-    Args:
-        metrics: Metrics to report. If this is a list, each item describes
-            the metric key reported to Keras, and it will reported under the
-            same name. If this is a dict, each key will be the name reported
-            and the respective value will be the metric key reported to Keras.
-            If this is None, all Keras logs will be reported.
-        on: When to report metrics. Must be one of
-            the Keras event hooks (less the ``on_``), e.g.
-            "train_start", or "predict_end". Defaults to "epoch_end".
-        frequency: Checkpoint frequency. If this is an integer `n`,
-            checkpoints are saved every `n` times each hook was called. If
-            this is a list, it specifies the checkpoint frequencies for each
-            hook individually.
-
-    """
-
-    def __init__(
-        self,
-        metrics: Optional[Union[str, List[str], Dict[str, str]]] = None,
-        on: Union[str, List[str]] = "epoch_end",
-        frequency: Union[int, List[int]] = 1,
-    ):
-        # TODO: Remove this class in 2.6.
-        raise DeprecationWarning(
-            "`ray.air.integrations.keras.Callback` is deprecated. Use "
-            "`ray.air.integrations.keras.ReportCheckpointCallback` instead.",
-        )

python/ray/air/result.py

@@ -1,7 +1,6 @@
 import os
 import json
 import pandas as pd
-import warnings
 from dataclasses import dataclass
 from pathlib import Path
 from typing import Any, Dict, List, Optional, Tuple, Union
@@ -13,7 +12,6 @@
     EXPR_PROGRESS_FILE,
     EXPR_ERROR_PICKLE_FILE,
 )
-from ray.util import log_once
 from ray.util.annotations import PublicAPI
 
 import logging
@@ -57,22 +55,6 @@ class Result:
     _local_path: Optional[str] = None
     _remote_path: Optional[str] = None
     _items_to_repr = ["error", "metrics", "path", "checkpoint"]
-    # Deprecate: raise in 2.5, remove in 2.6
-    log_dir: Optional[Path] = None
-
-    def __post_init__(self):
-        if self.log_dir and log_once("result_log_dir_deprecated"):
-            warnings.warn(
-                "The `Result.log_dir` property is deprecated. "
-                "Use `local_path` instead."
-            )
-            self._local_path = str(self.log_dir)
-
-        # Duplicate for retrieval
-        self.log_dir = Path(self._local_path) if self._local_path else None
-        # Backwards compatibility: Make sure to cast Path to string
-        # Deprecate: Remove this line after 2.6
-        self._local_path = str(self._local_path) if self._local_path else None
 
     @property
     def config(self) -> Optional[Dict[str, Any]]:

python/ray/train/tensorflow/tensorflow_checkpoint.py

@@ -296,7 +296,6 @@ def from_saved_model(
     def get_model(
         self,
         model: Optional[Union[tf.keras.Model, Callable[[], tf.keras.Model]]] = None,
-        model_definition: Optional[Callable[[], tf.keras.Model]] = None,
     ) -> tf.keras.Model:
         """Retrieve the model stored in this checkpoint.
 
@@ -308,13 +307,6 @@ def get_model(
         Returns:
             The Tensorflow Keras model stored in the checkpoint.
         """
-        # TODO: Remove `model_definition` in 2.6.
-        if model_definition is not None:
-            raise DeprecationWarning(
-                "The `model_definition` parameter is deprecated. Use the `model` "
-                "parameter instead."
-            )
-
         if model is not None and self._flavor is not self.Flavor.MODEL_WEIGHTS:
             warnings.warn(
                 "TensorflowCheckpoint was created from "