[1/n] Lightweight Ray AIR API refactor (ray-project#36706)

This PR removes some circularities in the Ray AIR import system so we can put the training related functions into `ray.train`. It introduces a training context and makes report, get_dataset_shard, Checkpoint, Result, and the following configs:

- CheckpointConfig
- DataConfig
- FailureConfig
- RunConfig
- ScalingConfig

available in `ray.train`. No user facing changes yet, the old APIs still work.

Going forward, it will be most consistent / symmetrical if these things are included in the following way:

```python
from ray import train, tune, serve # Pick the subset that is needed
# Include what you need from the following:
from ray.train import CheckpointConfig, DataConfig, FailureConfig, RunConfig, ScalingConfig

# ...

def train_func():
    dataset_shard = train.get_dataset_shard("train")
    world_size = train.get_context().get_world_size()
    # ...
    train.report(...)

trainer = train.torch.TorchTrainer(
    train_func,
    scaling_config=ScalingConfig(num_workers=2),
)
result = trainer.fit()
```

We have many examples in ray-project#37123 on how this looks like in actual code.

Signed-off-by: 久龙 <guyang.sgy@antfin.com>

doc/source/train/api/api.rst

@@ -52,6 +52,25 @@ Train Backend Base Classes
     ~train.backend.Backend
     ~train.backend.BackendConfig
 
+Ray Train Config
+----------------
+
+.. autosummary::
+
+    ~ray.train.DataConfig
+
+
+Ray Train Loop
+--------------
+
+.. autosummary::
+    :toctree: doc/
+
+    ~train.context.TrainContext
+    ~train.get_context
+    ~train.get_dataset_shard
+    ~train.report
+
 
 .. _train-integration-api:
 .. _train-framework-specific-ckpts:

python/ray/air/checkpoint.py

@@ -331,7 +331,7 @@ def from_bytes(cls, data: bytes) -> "Checkpoint":
             data: Data object containing pickled checkpoint data.
 
         Returns:
-            Checkpoint: checkpoint object.
+            ray.air.checkpoint.Checkpoint: checkpoint object.
         """
         bytes_data = pickle.loads(data)
         if isinstance(bytes_data, dict):
@@ -360,7 +360,7 @@ def from_dict(cls, data: dict) -> "Checkpoint":
             data: Dictionary containing checkpoint data.
 
         Returns:
-            Checkpoint: checkpoint object.
+            ray.air.checkpoint.Checkpoint: checkpoint object.
         """
         state = {}
         if _METADATA_KEY in data:
@@ -455,7 +455,7 @@ def from_directory(cls, path: Union[str, os.PathLike]) -> "Checkpoint":
                 Checkpoint).
 
         Returns:
-            Checkpoint: checkpoint object.
+            ray.air.checkpoint.Checkpoint: checkpoint object.
         """
         state = {}
 
@@ -474,7 +474,7 @@ def from_directory(cls, path: Union[str, os.PathLike]) -> "Checkpoint":
     @classmethod
     @DeveloperAPI
     def from_checkpoint(cls, other: "Checkpoint") -> "Checkpoint":
-        """Create a checkpoint from a generic :class:`Checkpoint`.
+        """Create a checkpoint from a generic :class:`ray.air.checkpoint.Checkpoint`.
 
         This method can be used to create a framework-specific checkpoint from a
         generic :class:`Checkpoint` object.
@@ -715,7 +715,7 @@ def from_uri(cls, uri: str) -> "Checkpoint":
             uri: Source location URI to read data from.
 
         Returns:
-            Checkpoint: checkpoint object.
+            ray.air.checkpoint.Checkpoint: checkpoint object.
         """
         state = {}
         try: