[tune] Remove checkpoint_dir and reporter deprecation notices

python/ray/train/base_trainer.py

@@ -733,7 +733,7 @@ def train_func(config):
         # stdout messages and the results directory.
         train_func.__name__ = trainer_cls.__name__
 
-        trainable_cls = wrap_function(train_func, warn=False)
+        trainable_cls = wrap_function(train_func)
         has_base_dataset = bool(self.datasets)
         if has_base_dataset:
             from ray.data.context import DataContext

python/ray/tune/registry.py

@@ -102,10 +102,10 @@ def register_trainable(name: str, trainable: Union[Callable, Type], warn: bool =
         logger.debug("Detected class for trainable.")
     elif isinstance(trainable, FunctionType) or isinstance(trainable, partial):
         logger.debug("Detected function for trainable.")
-        trainable = wrap_function(trainable, warn=warn)
+        trainable = wrap_function(trainable)
     elif callable(trainable):
         logger.info("Detected unknown callable for trainable. Converting to class.")
-        trainable = wrap_function(trainable, warn=warn)
+        trainable = wrap_function(trainable)
 
     if not issubclass(trainable, Trainable):
         raise TypeError("Second argument must be convertable to Trainable", trainable)
@@ -246,7 +246,7 @@ def unregister(self, category, key):
 
     def unregister_all(self, category: Optional[str] = None):
         remaining = set()
-        for (cat, key) in self._registered:
+        for cat, key in self._registered:
             if category and category == cat:
                 self.unregister(cat, key)
             else:

python/ray/tune/trainable/function_trainable.py

@@ -27,11 +27,7 @@
     SHOULD_CHECKPOINT,
 )
 from ray.tune.trainable import Trainable
-from ray.tune.utils import (
-    _detect_checkpoint_function,
-    _detect_config_single,
-    _detect_reporter,
-)
+from ray.tune.utils import _detect_config_single
 from ray.util.annotations import DeveloperAPI
 
 
@@ -44,64 +40,6 @@
 TEMP_MARKER = ".temp_marker"
 
 
-_CHECKPOINT_DIR_ARG_DEPRECATION_MSG = """Accepting a `checkpoint_dir` argument in your training function is deprecated.
-Please use `ray.train.get_checkpoint()` to access your checkpoint as a
-`ray.train.Checkpoint` object instead. See below for an example:
-
-Before
-------
-
-from ray import tune
-
-def train_fn(config, checkpoint_dir=None):
-    if checkpoint_dir:
-        torch.load(os.path.join(checkpoint_dir, "checkpoint.pt"))
-    ...
-
-tuner = tune.Tuner(train_fn)
-tuner.fit()
-
-After
------
-
-from ray import train, tune
-
-def train_fn(config):
-    checkpoint: train.Checkpoint = train.get_checkpoint()
-    if checkpoint:
-        with checkpoint.as_directory() as checkpoint_dir:
-            torch.load(os.path.join(checkpoint_dir, "checkpoint.pt"))
-    ...
-
-tuner = tune.Tuner(train_fn)
-tuner.fit()"""  # noqa: E501
-
-_REPORTER_ARG_DEPRECATION_MSG = """Accepting a `reporter` in your training function is deprecated.
-Please use `ray.train.report()` to report results instead. See below for an example:
-
-Before
-------
-
-from ray import tune
-
-def train_fn(config, reporter):
-    reporter(metric=1)
-
-tuner = tune.Tuner(train_fn)
-tuner.fit()
-
-After
------
-
-from ray import train, tune
-
-def train_fn(config):
-    train.report({"metric": 1})
-
-tuner = tune.Tuner(train_fn)
-tuner.fit()"""  # noqa: E501
-
-
 @DeveloperAPI
 class FunctionTrainable(Trainable):
     """Trainable that runs a user function reporting results.
@@ -271,29 +209,20 @@ def _report_thread_runner_error(self, block=False):
 
 @DeveloperAPI
 def wrap_function(
-    train_func: Callable[[Any], Any], warn: bool = True, name: Optional[str] = None
+    train_func: Callable[[Any], Any], name: Optional[str] = None
 ) -> Type["FunctionTrainable"]:
     inherit_from = (FunctionTrainable,)
 
     if hasattr(train_func, "__mixins__"):
         inherit_from = train_func.__mixins__ + inherit_from
 
     func_args = inspect.getfullargspec(train_func).args
-    use_checkpoint = _detect_checkpoint_function(train_func)
     use_config_single = _detect_config_single(train_func)
-    use_reporter = _detect_reporter(train_func)
-
-    if use_checkpoint:
-        raise DeprecationWarning(_CHECKPOINT_DIR_ARG_DEPRECATION_MSG)
-
-    if use_reporter:
-        raise DeprecationWarning(_REPORTER_ARG_DEPRECATION_MSG)
 
     if not use_config_single:
-        # use_reporter is hidden
         raise ValueError(
             "Unknown argument found in the Trainable function. "
-            "The function args must include a 'config' positional parameter."
+            "The function args must include a single 'config' positional parameter.\n"
             "Found: {}".format(func_args)
         )
 

python/ray/tune/trainable/util.py

@@ -10,7 +10,6 @@
 )
 from ray.air.config import ScalingConfig
 from ray.tune.registry import _ParameterRegistry
-from ray.tune.utils import _detect_checkpoint_function
 from ray.util.annotations import PublicAPI
 
 if TYPE_CHECKING:
@@ -124,12 +123,6 @@ def setup(self, config):
         trainable_with_params = _Inner
     else:
         # Function trainable
-        if _detect_checkpoint_function(trainable, partial=True):
-            from ray.tune.trainable.function_trainable import (
-                _CHECKPOINT_DIR_ARG_DEPRECATION_MSG,
-            )
-
-            raise DeprecationWarning(_CHECKPOINT_DIR_ARG_DEPRECATION_MSG)
 
         def inner(config):
             fn_kwargs = {}
@@ -223,13 +216,6 @@ def train_fn(config):
     if not inspect.isclass(trainable):
         if isinstance(trainable, types.MethodType):
             # Methods cannot set arbitrary attributes, so we have to wrap them
-            if _detect_checkpoint_function(trainable, partial=True):
-                from ray.tune.trainable.function_trainable import (
-                    _CHECKPOINT_DIR_ARG_DEPRECATION_MSG,
-                )
-
-                raise DeprecationWarning(_CHECKPOINT_DIR_ARG_DEPRECATION_MSG)
-
             def _trainable(config):
                 return trainable(config)
 

python/ray/tune/utils/__init__.py

@@ -8,8 +8,6 @@
     validate_save_restore,
     warn_if_slow,
     diagnose_serialization,
-    _detect_checkpoint_function,
-    _detect_reporter,
     _detect_config_single,
     wait_for_gpu,
 )
@@ -24,8 +22,6 @@
     "validate_save_restore",
     "warn_if_slow",
     "diagnose_serialization",
-    "_detect_checkpoint_function",
-    "_detect_reporter",
     "_detect_config_single",
     "wait_for_gpu",
 ]

python/ray/tune/utils/util.py

@@ -584,40 +584,6 @@ def validate_save_restore(
     return True
 
 
-def _detect_checkpoint_function(train_func, abort=False, partial=False):
-    """Use checkpointing if any arg has "checkpoint_dir" and args = 2"""
-    func_sig = inspect.signature(train_func)
-    validated = True
-    try:
-        # check if signature is func(config, checkpoint_dir=None)
-        if partial:
-            func_sig.bind_partial({}, checkpoint_dir="tmp/path")
-        else:
-            func_sig.bind({}, checkpoint_dir="tmp/path")
-    except Exception as e:
-        logger.debug(str(e))
-        validated = False
-    if abort and not validated:
-        func_args = inspect.getfullargspec(train_func).args
-        raise ValueError(
-            "Provided training function must have 1 `config` argument "
-            "`func(config)`. Got {}".format(func_args)
-        )
-    return validated
-
-
-def _detect_reporter(func):
-    """Use reporter if any arg has "reporter" and args = 2"""
-    func_sig = inspect.signature(func)
-    use_reporter = True
-    try:
-        func_sig.bind({}, reporter=None)
-    except Exception as e:
-        logger.debug(str(e))
-        use_reporter = False
-    return use_reporter
-
-
 def _detect_config_single(func):
     """Check if func({}) works."""
     func_sig = inspect.signature(func)