[AIR][Docs] Remove BatchPredictor from examples (ray-project#37178) (…

…ray-project#37269)

The following examples already use updated APIs:
* Stable Diffusion Batch Prediction with Ray AIR
* GPT-J-6B Batch Prediction with Ray AIR (LLM)

The following examples have been updated to use updated APIs:
* Training a model with distributed XGBoost
* Training a model with distributed LightGBM

I've removed batch prediction sections from the other examples, and, where appropriate, linked to the batch inference user guide.

Signed-off-by: Balaji Veeramani <balaji@anyscale.com>

doc/source/_toc.yml

@@ -57,7 +57,6 @@ parts:
               - file: ray-air/deployment
           - file: ray-air/examples/index
             sections:
-              - file: ray-air/examples/opt_deepspeed_batch_inference
               - file: ray-air/examples/torch_image_example
               - file: ray-air/examples/torch_detection
               - file: ray-air/examples/convert_existing_pytorch_code_to_ray_air

doc/source/ray-air/doc_code/pytorch_starter.py

@@ -129,17 +129,3 @@ def train_func(config):
 result = trainer.fit()
 print(f"Last result: {result.metrics}")
 # __air_pytorch_train_end__
-
-
-# # __air_pytorch_batchpred_start__
-# import random
-# from ray.train.batch_predictor import BatchPredictor
-# from ray.train.torch import TorchPredictor
-
-# batch_predictor = BatchPredictor.from_checkpoint(result.checkpoint, TorchPredictor)
-
-# items = [{"x": random.uniform(0, 1) for _ in range(10)}]
-# prediction_dataset = ray.data.from_items(items)
-
-# predictions = batch_predictor.predict(prediction_dataset, dtype=torch.float)
-# # __air_pytorch_batchpred_end__

doc/source/ray-air/doc_code/tf_starter.py

@@ -75,24 +75,3 @@ def train_func(config: dict):
 result = trainer.fit()
 print(result.metrics)
 # __air_tf_train_end__
-
-# __air_tf_batchpred_start__
-import numpy as np
-
-from ray.train.batch_predictor import BatchPredictor
-from ray.train.tensorflow import TensorflowPredictor
-
-
-batch_predictor = BatchPredictor.from_checkpoint(
-    result.checkpoint, TensorflowPredictor, model_definition=build_model
-)
-
-items = [{"x": np.random.uniform(0, 1)} for _ in range(10)]
-prediction_dataset = ray.data.from_items(items)
-
-predictions = batch_predictor.predict(prediction_dataset, dtype=tf.float32)
-
-print("PREDICTIONS")
-predictions.show()
-
-# __air_tf_batchpred_end__

doc/source/ray-air/examples/BUILD

@@ -15,22 +15,12 @@ filegroup(
 py_test_run_all_subdirectory(
     size = "medium",
     include = ["*.py"],
-    exclude = ["torch_image_batch_pretrained.py"],
-    extra_srcs = [],
-    data = ["//doc/source/ray-air/examples:air_examples"],
-    tags = ["exclusive", "team:ml", "ray_air"],
-)
-
-py_test_run_all_subdirectory(
-    size = "medium",
-    include = ["torch_image_batch_pretrained.py"],
     exclude = [],
     extra_srcs = [],
     data = ["//doc/source/ray-air/examples:air_examples"],
-    tags = ["exclusive", "team:ml", "ray_air", "gpu"],
+    tags = ["exclusive", "team:ml", "ray_air"],
 )
 
-
 # --------------------------------------------------------------------
 # Test all doc/source/ray-air/examples notebooks.
 # --------------------------------------------------------------------
@@ -50,7 +40,6 @@ py_test_run_all_notebooks(
         "gptj_serving.ipynb",  # Requires GPUs
         "stablediffusion_batch_prediction.ipynb",  # Requires GPUs
         "gptj_deepspeed_fine_tuning.ipynb",  # Requires release test
-        "opt_deepspeed_batch_inference.ipynb", # Requires release test
         "dolly_lightning_fsdp_finetuning.ipynb", # Requires release test
     ],
     data = ["//doc/source/ray-air/examples:air_examples"],