[air] Update to beta (#27393)

Update API references to beta. Needed as we are going to beta in 2.0.

I left out RL/Scikit-Learn/HuggingFace.

python/ray/air/checkpoint.py

@@ -38,7 +38,7 @@ _CHECKPOINT_DIR_PREFIX = "checkpoint_tmp_"
 logger = logging.getLogger(__name__)
 
 
-@PublicAPI(stability="alpha")
+@PublicAPI(stability="beta")
 class Checkpoint:
     """Ray AIR Checkpoint.
 

python/ray/air/config.py

@@ -77,7 +77,7 @@ def _repr_dataclass(obj, *, default_values: Optional[Dict[str, Any]] = None) ->
 
 
 @dataclass
-@PublicAPI(stability="alpha")
+@PublicAPI(stability="beta")
 class ScalingConfig:
     """Configuration for scaling training.
 
@@ -264,7 +264,7 @@ class ScalingConfig:
 
 
 @dataclass
-@PublicAPI(stability="alpha")
+@PublicAPI(stability="beta")
 class DatasetConfig:
     """Configuration for ingest of a single Dataset.
 
@@ -428,7 +428,7 @@ class DatasetConfig:
 
 
 @dataclass
-@PublicAPI(stability="alpha")
+@PublicAPI(stability="beta")
 class FailureConfig:
     """Configuration related to failure handling of each run/trial.
 
@@ -463,7 +463,7 @@ class FailureConfig:
 
 
 @dataclass
-@PublicAPI(stability="alpha")
+@PublicAPI(stability="beta")
 class CheckpointConfig:
     """Configurable parameters for defining the checkpointing strategy.
 
@@ -543,7 +543,7 @@ class CheckpointConfig:
 
 
 @dataclass
-@PublicAPI(stability="alpha")
+@PublicAPI(stability="beta")
 class RunConfig:
     """Runtime configuration for individual trials that are run.
 

python/ray/air/result.py

@@ -9,7 +9,7 @@ import pandas as pd
 
 
 @dataclass
-@PublicAPI(stability="alpha")
+@PublicAPI(stability="beta")
 class Result:
     """The final result of a ML training run or a Tune trial.
 

python/ray/data/preprocessor.py

@@ -13,14 +13,14 @@ if TYPE_CHECKING:
     from ray.air.data_batch_type import DataBatchType
 
 
-@PublicAPI(stability="alpha")
+@PublicAPI(stability="beta")
 class PreprocessorNotFittedException(RuntimeError):
     """Error raised when the preprocessor needs to be fitted first."""
 
     pass
 
 
-@PublicAPI(stability="alpha")
+@PublicAPI(stability="beta")
 class Preprocessor(abc.ABC):
     """Implements an ML preprocessing operation.
 

python/ray/train/base_trainer.py

@@ -27,7 +27,7 @@ GenDataset = Union["Dataset", Callable[[], "Dataset"]]
 logger = logging.getLogger(__name__)
 
 
-@PublicAPI(stability="alpha")
+@PublicAPI(stability="beta")
 class TrainingFailedError(RuntimeError):
     """An error indicating that training has failed."""
 
@@ -322,7 +322,7 @@ class BaseTrainer(abc.ABC):
         """
         raise NotImplementedError
 
-    @PublicAPI(stability="alpha")
+    @PublicAPI(stability="beta")
     def fit(self) -> Result:
         """Runs training.
 

python/ray/train/batch_predictor.py

@@ -12,7 +12,7 @@ from ray.train.predictor import Predictor
 from ray.util.annotations import PublicAPI
 
 
-@PublicAPI(stability="alpha")
+@PublicAPI(stability="beta")
 class BatchPredictor:
     """Batch predictor class.
 

python/ray/train/horovod/horovod_trainer.py

@@ -13,7 +13,7 @@ if TYPE_CHECKING:
     from ray.data.preprocessor import Preprocessor
 
 
-@PublicAPI(stability="alpha")
+@PublicAPI(stability="beta")
 class HorovodTrainer(DataParallelTrainer):
     """A Trainer for data parallel Horovod training.
 

python/ray/train/lightgbm/lightgbm_checkpoint.py

@@ -13,7 +13,7 @@ if TYPE_CHECKING:
     from ray.data.preprocessor import Preprocessor
 
 
-@PublicAPI(stability="alpha")
+@PublicAPI(stability="beta")
 class LightGBMCheckpoint(Checkpoint):
     """A :py:class:`~ray.air.checkpoint.Checkpoint` with LightGBM-specific
     functionality.

python/ray/train/lightgbm/lightgbm_predictor.py

@@ -15,7 +15,7 @@ if TYPE_CHECKING:
     from ray.data.preprocessor import Preprocessor
 
 
-@PublicAPI(stability="alpha")
+@PublicAPI(stability="beta")
 class LightGBMPredictor(Predictor):
     """A predictor for LightGBM models.
 

python/ray/train/lightgbm/lightgbm_trainer.py

@@ -13,7 +13,7 @@ if TYPE_CHECKING:
     from ray.data.preprocessor import Preprocessor
 
 
-@PublicAPI(stability="alpha")
+@PublicAPI(stability="beta")
 class LightGBMTrainer(GBDTTrainer):
     """A Trainer for data parallel LightGBM training.
 

python/ray/train/predictor.py

@@ -29,14 +29,14 @@ TYPE_TO_ENUM: Dict[Type[DataBatchType], DataType] = {
 }
 
 
-@PublicAPI(stability="alpha")
+@PublicAPI(stability="beta")
 class PredictorNotSerializableException(RuntimeError):
     """Error raised when trying to serialize a Predictor instance."""
 
     pass
 
 
-@PublicAPI(stability="alpha")
+@PublicAPI(stability="beta")
 class Predictor(abc.ABC):
     """Predictors load models from checkpoints to perform inference.
 

python/ray/train/tensorflow/tensorflow_checkpoint.py

@@ -12,7 +12,7 @@ if TYPE_CHECKING:
     from ray.data.preprocessor import Preprocessor
 
 
-@PublicAPI(stability="alpha")
+@PublicAPI(stability="beta")
 class TensorflowCheckpoint(Checkpoint):
     """A :py:class:`~ray.air.checkpoint.Checkpoint` with TensorFlow-specific
     functionality.

python/ray/train/tensorflow/tensorflow_predictor.py

@@ -19,7 +19,7 @@ if TYPE_CHECKING:
 logger = logging.getLogger(__name__)
 
 
-@PublicAPI(stability="alpha")
+@PublicAPI(stability="beta")
 class TensorflowPredictor(DLPredictor):
     """A predictor for TensorFlow models.
 

python/ray/train/tensorflow/tensorflow_trainer.py

@@ -11,7 +11,7 @@ if TYPE_CHECKING:
     from ray.data.preprocessor import Preprocessor
 
 
-@PublicAPI(stability="alpha")
+@PublicAPI(stability="beta")
 class TensorflowTrainer(DataParallelTrainer):
     """A Trainer for data parallel Tensorflow training.
 

python/ray/train/torch/torch_checkpoint.py

@@ -12,7 +12,7 @@ if TYPE_CHECKING:
     from ray.data.preprocessor import Preprocessor
 
 
-@PublicAPI(stability="alpha")
+@PublicAPI(stability="beta")
 class TorchCheckpoint(Checkpoint):
     """A :py:class:`~ray.air.checkpoint.Checkpoint` with Torch-specific
     functionality.

python/ray/train/torch/torch_predictor.py

@@ -18,7 +18,7 @@ if TYPE_CHECKING:
 logger = logging.getLogger(__name__)
 
 
-@PublicAPI(stability="alpha")
+@PublicAPI(stability="beta")
 class TorchPredictor(DLPredictor):
     """A predictor for PyTorch models.
 

python/ray/train/torch/torch_trainer.py

@@ -11,7 +11,7 @@ if TYPE_CHECKING:
     from ray.data.preprocessor import Preprocessor
 
 
-@PublicAPI(stability="alpha")
+@PublicAPI(stability="beta")
 class TorchTrainer(DataParallelTrainer):
     """A Trainer for data parallel PyTorch training.
 

python/ray/train/xgboost/xgboost_checkpoint.py

@@ -13,7 +13,7 @@ if TYPE_CHECKING:
     from ray.data.preprocessor import Preprocessor
 
 
-@PublicAPI(stability="alpha")
+@PublicAPI(stability="beta")
 class XGBoostCheckpoint(Checkpoint):
     """A :py:class:`~ray.air.checkpoint.Checkpoint` with XGBoost-specific
     functionality.

python/ray/train/xgboost/xgboost_predictor.py

@@ -14,7 +14,7 @@ if TYPE_CHECKING:
     from ray.data.preprocessor import Preprocessor
 
 
-@PublicAPI(stability="alpha")
+@PublicAPI(stability="beta")
 class XGBoostPredictor(Predictor):
     """A predictor for XGBoost models.
 

python/ray/train/xgboost/xgboost_trainer.py

@@ -13,7 +13,7 @@ if TYPE_CHECKING:
     from ray.data.preprocessor import Preprocessor
 
 
-@PublicAPI(stability="alpha")
+@PublicAPI(stability="beta")
 class XGBoostTrainer(GBDTTrainer):
     """A Trainer for data parallel XGBoost training.
 

python/ray/tune/search/variant_generator.py

@@ -58,7 +58,7 @@ def generate_variants(
         yield resolved_vars, spec
 
 
-@PublicAPI(stability="alpha")
+@PublicAPI(stability="beta")
 def grid_search(values: Iterable) -> Dict[str, List]:
     """Convenience method for specifying grid search over a value.
 

python/ray/tune/tune_config.py

@@ -8,7 +8,7 @@ from ray.util import PublicAPI
 
 
 @dataclass
-@PublicAPI(stability="alpha")
+@PublicAPI(stability="beta")
 class TuneConfig:
     """Tune specific configs.
 

python/ray/tune/tuner.py

@@ -28,7 +28,7 @@ _TUNER_INTERNAL = "_tuner_internal"
 _SELF = "self"
 
 
-@PublicAPI(stability="alpha")
+@PublicAPI(stability="beta")
 class Tuner:
     """Tuner is the recommended way of launching hyperparameter tuning jobs with Ray Tune.