Clean up Ray AIR references in code (ray-project#39490)

doc/source/cluster/kubernetes/configs/xgboost-benchmark-autoscaler.yaml

@@ -1,4 +1,4 @@
-# This is a RayCluster configuration for exploration of the 100Gi Ray AIR XGBoostTrainer benchmark.
+# This is a RayCluster configuration for exploration of the 100Gi Ray XGBoostTrainer benchmark.
 
 # This configuration here modifies the file xgboost-benchmark.yaml in this directory
 # to demonstrate autoscaling.

doc/source/cluster/kubernetes/configs/xgboost-benchmark.yaml

@@ -1,4 +1,4 @@
-# This is a RayCluster configuration for exploration of the 100Gi Ray AIR XGBoostTrainer benchmark.
+# This is a RayCluster configuration for exploration of the 100Gi Ray XGBoostTrainer benchmark.
 
 # The configuration includes 1 Ray head pod and 9 Ray worker pods.
 # Each Ray container requests 54 Gi memory and 14 CPU.

doc/source/cluster/vms/configs/xgboost-benchmark.yaml

@@ -1,4 +1,4 @@
-# This is a Ray cluster configuration for exploration of the 100Gi Ray AIR XGBoostTrainer benchmark.
+# This is a Ray cluster configuration for exploration of the 100Gi Ray XGBoostTrainer benchmark.
 
 # The configuration includes 1 Ray head node and 9 worker nodes.
 

doc/source/ray-overview/getting-started.md

@@ -9,7 +9,7 @@ Use Ray to scale applications on your laptop or the cloud. Choose the right guid
 
 
 (libraries-quickstart)=
-## Ray AI Runtime Libraries Quickstart
+## Ray AI Libraries Quickstart
 
 Use individual libraries for ML workloads. Click on the dropdowns for your workload below.
 

doc/source/ray-overview/use-cases.rst

@@ -165,7 +165,7 @@ Learn more about reinforcement learning with the following resources.
 ML Platform
 -----------
 
-Ray and its AI Runtime libraries provide unified compute runtime for teams looking to simplify their ML platform.
+Ray and its AI libraries provide unified compute runtime for teams looking to simplify their ML platform.
 Ray's libraries such as Ray Train, Ray Data, and Ray Serve can be used to compose end-to-end ML workflows, providing features and APIs for
 data preprocessing as part of training, and transitioning from training to serving.
 

doc/source/templates/02_many_model_training/start.ipynb

@@ -331,7 +331,7 @@
    "source": [
     "## Summary\n",
     "\n",
-    "This template is a quickstart to using [Ray Tune](https://docs.ray.io/en/latest/tune/index.html) for many model training. Ray Tune is one of many libraries under the [Ray AI Runtime](https://docs.ray.io/en/latest/ray-air/getting-started.html). See [this blog post](https://www.anyscale.com/blog/training-one-million-machine-learning-models-in-record-time-with-ray) for more information on the benefits of performing many model training with Ray!\n",
+    "This template is a quickstart to using [Ray Tune](https://docs.ray.io/en/latest/tune/index.html) for many model training. See [this blog post](https://www.anyscale.com/blog/training-one-million-machine-learning-models-in-record-time-with-ray) for more information on the benefits of performing many model training with Ray!\n",
     "\n",
     "At a high level, this template showed how to do the following:\n",
     "\n",

doc/source/templates/03_serving_stable_diffusion/start.ipynb

@@ -410,7 +410,7 @@
    "source": [
     "## Summary\n",
     "\n",
-    "This template used [Ray Serve](https://docs.ray.io/en/latest/serve/index.html) to serve many replicas of a stable diffusion model. Ray Serve is one of many libraries under the [Ray AI Runtime](https://docs.ray.io/en/latest/ray-air/getting-started.html).\n",
+    "This template used [Ray Serve](https://docs.ray.io/en/latest/serve/index.html) to serve many replicas of a stable diffusion model. \n",
     "\n",
     "At a high level, this template showed how to:\n",
     "1. Define a Ray Serve deployment to load a HuggingFace model and perform inference.\n",

doc/source/templates/05_dreambooth_finetuning/dreambooth/train.py

@@ -24,7 +24,7 @@
 
 # LoRA related imports end ##
 from diffusers.utils.import_utils import is_xformers_available
-from ray.air import ScalingConfig
+from ray.train import ScalingConfig
 from ray import train
 from ray.train.torch import TorchTrainer
 import torch

doc/source/train/examples/pytorch/torch_detection.ipynb

@@ -8,7 +8,7 @@
             "source": [
                 "# Fine-tuning a Torch object detection model\n",
                 "\n",
-                "This tutorial explains how to fine-tune `fasterrcnn_resnet50_fpn` using the Ray AI Runtime libraries for parallel data ingest and training.\n",
+                "This tutorial explains how to fine-tune `fasterrcnn_resnet50_fpn` using the Ray AI libraries for parallel data ingest and training.\n",
                 "\n",
                 "Here's what you'll do:\n",
                 "1. Load raw images and [VOC-style](http://host.robots.ox.ac.uk/pascal/VOC/) annotations into a Dataset\n",
@@ -29,7 +29,7 @@
             "id": "e9a6d043",
             "metadata": {},
             "source": [
-                "* Install the dependencies for Ray AI Runtime libraries."
+                "* Install the dependencies for Ray Data and Ray Train."
             ]
         },
         {

doc/source/tune/examples/pbt_ppo_example.ipynb

@@ -44,7 +44,7 @@
     "import random\n",
     "\n",
     "import ray\n",
-    "from ray import air, tune\n",
+    "from ray import train, tune\n",
     "from ray.tune.schedulers import PopulationBasedTraining\n",
     "\n",
     "if __name__ == \"__main__\":\n",
@@ -111,7 +111,7 @@
     "            \"sgd_minibatch_size\": tune.choice([128, 512, 2048]),\n",
     "            \"train_batch_size\": tune.choice([10000, 20000, 40000]),\n",
     "        },\n",
-    "        run_config=air.RunConfig(stop=stopping_criteria),\n",
+    "        run_config=train.RunConfig(stop=stopping_criteria),\n",
     "    )\n",
     "    results = tuner.fit()\n"
    ]

doc/source/tune/tutorials/tune-distributed.rst

@@ -108,12 +108,12 @@ reduce synchronization overhead. For this, you just have to specify a remote ``s
 
 .. code-block:: python
 
-    from ray import air, tune
+    from ray import train, tune
     from my_module import my_trainable
 
     tuner = tune.Tuner(
         my_trainable,
-        run_config=air.RunConfig(
+        run_config=train.RunConfig(
             name="experiment_name",
             storage_path="s3://bucket-name/sub-path/",
         )

doc/source/tune/tutorials/tune-storage.rst

@@ -88,11 +88,11 @@ All we need to do is **set the shared network filesystem as the path to save res
 
 .. code-block:: python
 
-    from ray import air, tune
+    from ray import train, tune
 
     tuner = tune.Tuner(
         trainable,
-        run_config=air.RunConfig(
+        run_config=train.RunConfig(
             name="experiment_name",
             storage_path="/mnt/path/to/shared/storage/",
         )

python/ray/air/integrations/keras.py

@@ -103,7 +103,7 @@ def on_predict_end(self, logs=None):
 
 @PublicAPI(stability="alpha")
 class ReportCheckpointCallback(_Callback):
-    """Keras callback for Ray AIR reporting and checkpointing.
+    """Keras callback for Ray Train reporting and checkpointing.
 
     .. note::
         Metrics are always reported with checkpoints, even if the event isn't specified

python/ray/air/util/check_ingest.py

@@ -153,7 +153,7 @@ def make_local_dataset_iterator(
         raise RuntimeError(
             "make_local_dataset_iterator should only be used by the driver "
             "for development and debugging. To consume a dataset from a "
-            "worker or AIR trainer, see "
+            "worker or Ray Train Trainer, see "
             "https://docs.ray.io/en/latest/ray-air/check-ingest.html."
         )
 

python/ray/data/iterator.py

@@ -68,7 +68,7 @@ class DataIterator(abc.ABC):
     over the base Dataset. Note that for DatasetPipelines, each pass iterates over
     the original Dataset, instead of a window (if ``.window()`` was used).
 
-    If using Ray AIR, each trainer actor should get its own iterator by calling
+    If using Ray Train, each trainer actor should get its own iterator by calling
     :meth:`ray.train.get_dataset_shard("train")
     <ray.train.get_dataset_shard>`.
 
@@ -896,7 +896,7 @@ def generator():
 
     def iter_epochs(self, max_epoch: int = -1) -> None:
         raise DeprecationWarning(
-            "If you are using AIR, note that ray.train.get_dataset_shard() "
+            "If you are using Ray Train, ray.train.get_dataset_shard() "
             "returns a ray.data.DataIterator instead of a "
             "DatasetPipeline as of Ray 2.3. "
             "To iterate over one epoch of data, use iter_batches(), "

python/ray/train/base_trainer.py

@@ -91,7 +91,7 @@ class BaseTrainer(abc.ABC):
     Note: The base ``BaseTrainer`` class cannot be instantiated directly. Only
     one of its subclasses can be used.
 
-    Note to AIR developers: If a new AIR trainer is added, please update
+    Note to developers: If a new trainer is added, please update
     `air/_internal/usage.py`.
 
     **How does a trainer work?**

python/ray/train/data_parallel_trainer.py

@@ -705,7 +705,7 @@ def _load_checkpoint_dict(
             message.
 
     Returns:
-        The model or weights and AIR preprocessor contained within.
+        The model or weights and preprocessor contained within.
     """
     checkpoint_dict = checkpoint.to_dict()
     preprocessor = checkpoint_dict.get(PREPROCESSOR_KEY, None)

python/ray/train/lightning/lightning_checkpoint.py

@@ -44,7 +44,7 @@ def from_path(
             raise ValueError(
                 f"`from_path()` expects a file path, but `{path}` is a directory. "
                 "A valid checkpoint file name is normally with .ckpt extension."
-                "If you have an AIR checkpoint folder, you can also try to use "
+                "If you have a Ray checkpoint folder, you can also try to use "
                 "`LightningCheckpoint.from_directory()` instead."
             )
 
@@ -77,7 +77,7 @@ def __init__(self, input_dim, output_dim) -> None:
 
                     # ...
 
-                # After the training is finished, LightningTrainer saves AIR
+                # After the training is finished, LightningTrainer saves
                 # checkpoints in the result directory, for example:
                 # ckpt_dir = "{storage_path}/LightningTrainer_.*/checkpoint_000000"
 
@@ -196,7 +196,7 @@ def from_path(
             raise ValueError(
                 f"`from_path()` expects a file path, but `{path}` is a directory. "
                 "A valid checkpoint file name is normally with .ckpt extension."
-                "If you have an AIR checkpoint folder, you can also try to use "
+                "If you have a Ray checkpoint folder, you can also try to use "
                 "`LightningCheckpoint.from_directory()` instead."
             )
 

python/ray/train/lightning/lightning_predictor.py

@@ -105,7 +105,7 @@ def __init__(self, input_dim, output_dim) -> None:
 
                     # ...
 
-                # After the training is finished, LightningTrainer saves AIR
+                # After the training is finished, LightningTrainer saves
                 # checkpoints in the result directory, for example:
                 # ckpt_dir = "{storage_path}/LightningTrainer_.*/checkpoint_000000"
 

python/ray/train/lightning/lightning_trainer.py

@@ -200,7 +200,7 @@ def checkpointing(self, **kwargs) -> "LightningConfigBuilder":
 
         Note that this method is not a replacement for the
         ``ray.train.CheckpointConfig``. You still need to specify your
-        checkpointing strategy in ``CheckpointConfig``. Otherwise, AIR stores
+        checkpointing strategy in ``CheckpointConfig``. Otherwise, Ray stores
         all the reported checkpoints by default.
 
         Args:
@@ -459,14 +459,14 @@ def __init__(
     def _unify_checkpoint_configs(
         self, ptl_ckpt_config: Dict, air_ckpt_config: CheckpointConfig
     ) -> CheckpointConfig:
-        """Unify the Lightning checkpointing config and the AIR CheckpointConfig."""
+        """Unify the Lightning checkpointing config and the Ray CheckpointConfig."""
 
         ptl_ckpt_metric = ptl_ckpt_config.get("monitor", None)
         air_ckpt_metric = air_ckpt_config.checkpoint_score_attribute
 
         if ptl_ckpt_metric and air_ckpt_metric and ptl_ckpt_metric != air_ckpt_metric:
             logger.warning(
-                "You have specified different metrics to track in AIR "
+                "You have specified different metrics to track in "
                 "`CheckpointConfig` and Lightning ModelCheckpoint. "
                 "Make sure that you have logged both metrics before "
                 "a checkpoint is created."
@@ -600,7 +600,7 @@ def _lightning_train_loop_per_worker(config):
 
     checkpoint = session.get_checkpoint()
     if checkpoint:
-        checkpoint_log_message = "Resuming training from an AIR checkpoint."
+        checkpoint_log_message = "Resuming training from a checkpoint."
         if "ckpt_path" in trainer_fit_params:
             checkpoint_log_message += " `ckpt_path` will be ignored."
         logger.info(checkpoint_log_message)

python/ray/train/mosaic/mosaic_trainer.py

@@ -227,7 +227,7 @@ def _validate_trainer_init_config(self, config) -> None:
         if config is not None and "loggers" in config:
             warnings.warn(
                 "Composer's Loggers (any subclass of LoggerDestination) are \
-                not supported for MosaicComposer. Use Ray AIR provided loggers instead"
+                not supported for MosaicComposer. Use Ray provided loggers instead"
             )
 
 

python/ray/tune/examples/cifar10_pytorch.py

@@ -154,7 +154,7 @@ def train_cifar(config):
         # Ray Tune and will potentially be accessed through in ``get_checkpoint()``
         # in future iterations.
         # Note to save a file like checkpoint, you still need to put it under a directory
-        # to construct an AIR checkpoint.
+        # to construct a checkpoint.
         os.makedirs("my_model", exist_ok=True)  # ok to overwrite the previous one.
         path = os.path.join("my_model", "checkpoint.pt")
         torch.save(

python/ray/tune/examples/pbt_convnet_function_example.py

@@ -47,7 +47,7 @@ def train_convnet(config):
             # Every 5 steps, checkpoint our current state.
             # First get the checkpoint directory from tune.
             # Need to create a directory under current working directory
-            # to construct an AIR Checkpoint object from.
+            # to construct checkpoint object from.
             os.makedirs("my_model", exist_ok=True)
             torch.save(
                 {

python/ray/tune/experiment/experiment.py

@@ -468,7 +468,7 @@ def get_experiment_checkpoint_dir(
 
         Args:
             run_obj: Trainable to run.
-            storage_path: The path to Ray AIR's result storage.
+            storage_path: The path to the specified result storage.
             name: The name of the experiment specified by user.
 
         Returns:

python/ray/tune/impl/tuner_internal.py

@@ -447,7 +447,7 @@ def _choose_run_config(
 
         Args:
             tuner_run_config: The run config passed into the Tuner constructor.
-            trainer: The AIR Trainer instance to use with Tune, which may have
+            trainer: The Trainer instance to use with Tune, which may have
                 a RunConfig specified by the user.
             param_space: The param space passed to the Tuner.
 
@@ -562,8 +562,8 @@ def param_space(self, param_space: Optional[Dict[str, Any]]):
             self._process_scaling_config()
 
     def _convert_trainable(self, trainable: TrainableTypeOrTrainer) -> TrainableType:
-        """Converts an AIR Trainer to a Tune trainable and saves the converted
-        trainable. If not using an AIR Trainer, this leaves the trainable as is."""
+        """Converts a Trainer to a Tune trainable and saves the converted
+        trainable. If not using a Trainer, this leaves the trainable as is."""
         from ray.train.trainer import BaseTrainer
 
         return (
@@ -610,7 +610,7 @@ def _get_tune_run_arguments(self, trainable: TrainableType) -> Dict[str, Any]:
                     "You passed `checkpoint_frequency="
                     f"{checkpoint_config.checkpoint_frequency}` to your "
                     "CheckpointConfig, but this trainer does not support "
-                    "this argument. If you passed in an AIR trainer that takes in a "
+                    "this argument. If you passed in a Trainer that takes in a "
                     "custom training loop, you will need to "
                     "report a checkpoint every `checkpoint_frequency` iterations "
                     "within your training loop using "
@@ -621,7 +621,7 @@ def _get_tune_run_arguments(self, trainable: TrainableType) -> Dict[str, Any]:
                 # If we specifically support it, it's handled in the training loop,
                 # so we disable tune's bookkeeping.
                 checkpoint_config.checkpoint_frequency = 0
-            # Otherwise, the trainable is not an AIR trainer and we just keep the
+            # Otherwise, the trainable is not a Trainer and we just keep the
             # user-supplied value.
             # Function trainables will raise a runtime error later if set > 0
         if checkpoint_config.checkpoint_at_end is not None:
@@ -633,7 +633,7 @@ def _get_tune_run_arguments(self, trainable: TrainableType) -> Dict[str, Any]:
                     "You passed `checkpoint_at_end="
                     f"{checkpoint_config.checkpoint_at_end}` "
                     "to your CheckpointConfig, but this trainer does not support "
-                    "this argument. If you passed in an AIR trainer that takes in a "
+                    "this argument. If you passed in a Trainer that takes in a "
                     "custom training loop, you should include one last call to "
                     "`ray.train.report(metrics=..., checkpoint=...)` "
                     "at the end of your training loop to get this behavior."

python/ray/tune/trainable/util.py

@@ -358,7 +358,7 @@ def with_resources(
 
     Args:
         trainable: Trainable to wrap.
-        resources: Resource dict, placement group factory, AIR ``ScalingConfig``
+        resources: Resource dict, placement group factory, ``ScalingConfig``
             or callable that takes in a config dict and returns a placement
             group factory.
 

python/ray/tune/tune.py

@@ -984,7 +984,7 @@ class and registered trainables.
                 "To enable trials to use GPUs, wrap `train_func` with "
                 "`tune.with_resources(train_func, resources_per_trial={'gpu': 1})` "
                 "which allows Tune to expose 1 GPU to each trial. "
-                "For Ray AIR Trainers, you can specify GPU resources "
+                "For Ray Train Trainers, you can specify GPU resources "
                 "through `ScalingConfig(use_gpu=True)`. "
                 "You can also override "
                 "`Trainable.default_resource_request` if using the "

python/ray/tune/tune_config.py

@@ -46,7 +46,7 @@ class TuneConfig:
             and stop actors often (e.g., PBT in time-multiplexing mode). This
             requires trials to have the same resource requirements.
             Defaults to ``True`` for function trainables (including most
-            Ray AIR trainers) and ``False`` for class and registered trainables
+            Ray Train Trainers) and ``False`` for class and registered trainables
             (e.g. RLlib).
         trial_name_creator: Optional function that takes in a Trial and returns
             its name (i.e. its string representation). Be sure to include some unique

python/ray/util/horovod/__init__.py

@@ -1,5 +1,4 @@
 raise DeprecationWarning(
     "ray.util.horovod has been removed as of Ray 2.0. Instead, use the `horovod` "
-    "library directly or the `HorovodTrainer` in Ray AIR ("
-    "https://docs.ray.io/en/master/ray-air/getting-started.html)"
+    "library directly or the `HorovodTrainer` in Ray Train."
 )

python/ray/util/lightgbm/__init__.py

@@ -1,5 +1,4 @@
 raise DeprecationWarning(
     "ray.util.lightgbm has been removed as of Ray 2.0. Instead, use the `lightgbm-ray` "
-    "library directly or the `LightGBMTrainer` in Ray AIR ("
-    "https://docs.ray.io/en/master/ray-air/getting-started.html)"
+    "library directly or the `LightGBMTrainer` in Ray Train."
 )

python/ray/util/sgd/__init__.py

@@ -1,6 +1,4 @@
 raise DeprecationWarning(
     "Ray SGD has been deprecated as of Ray 1.13. For distributed "
-    "deep learning on Ray please use Ray AI Runtime (Ray AIR) "
-    "instead ("
-    "https://docs.ray.io/en/master/ray-air/getting-started.html)."
+    "deep learning on Ray please use Ray Train instead."
 )

python/ray/util/xgboost/__init__.py

@@ -1,5 +1,4 @@
 raise DeprecationWarning(
     "ray.util.xgboost has been removed as of Ray 2.0. Instead, use the `xgboost-ray` "
-    "library directly or the `XGBoostTrainer` in Ray AIR ("
-    "https://docs.ray.io/en/master/ray-air/getting-started.html)"
+    "library directly or the `XGBoostTrainer` in Ray Train."
 )