[air] Refactor Docs for re-emphasizing libraries (3/n)

doc/source/_toc.yml

@@ -41,51 +41,6 @@ parts:
               - file: ray-core/examples/map_reduce
           - file: ray-core/api/index
 
-      - file: ray-air/getting-started
-        title: "Ray AI Runtime (AIR)"
-        sections:
-          - file: ray-air/key-concepts
-          - file: ray-air/user-guides
-            sections:
-              - file: ray-air/preprocessors
-              - file: ray-air/trainers
-              - file: ray-air/check-ingest
-              - file: ray-air/tuner
-              - file: ray-air/predictors
-              - file: ray-air/computer-vision
-              - file: ray-air/examples/serving_guide
-              - file: ray-air/deployment
-          - file: ray-air/examples/index
-            sections:
-              - file: ray-air/examples/torch_image_example
-              - file: ray-air/examples/torch_detection
-              - file: ray-air/examples/convert_existing_pytorch_code_to_ray_air
-              - file: ray-air/examples/convert_existing_tf_code_to_ray_air
-              - file: ray-air/examples/tfx_tabular_train_to_serve
-              - file: ray-air/examples/huggingface_text_classification
-              - file: ray-air/examples/sklearn_example
-              - file: ray-air/examples/xgboost_example
-              - file: ray-air/examples/analyze_tuning_results
-              - file: ray-air/examples/lightgbm_example
-              - file: ray-air/examples/torch_incremental_learning
-              - file: ray-air/examples/rl_serving_example
-              - file: ray-air/examples/rl_online_example
-              - file: ray-air/examples/rl_offline_example
-              - file: ray-air/examples/upload_to_comet_ml
-              - file: ray-air/examples/upload_to_wandb
-              - file: ray-air/examples/feast_example
-              - file: ray-air/examples/automl_with_ray_air
-              - file: ray-air/examples/batch_tuning
-              - file: ray-air/examples/batch_forecasting
-              - file: ray-air/examples/stablediffusion_batch_prediction
-              - file: ray-air/examples/gptj_deepspeed_fine_tuning
-              - file: ray-air/examples/gptj_batch_prediction
-              - file: ray-air/examples/gptj_serving
-              - file: ray-air/examples/dreambooth_finetuning
-              - file: ray-air/examples/dolly_lightning_fsdp_finetuning
-          - file: ray-air/api/api
-          - file: ray-air/benchmarks
-
       - file: data/data
         title: Ray Data
         sections:
@@ -111,10 +66,17 @@ parts:
                 title: "Configuring Ray Train"
               - file: train/dl_guide
                 title: "Deep Learning Guide"
+              - file: train/hf_trainers
+                title: "Hugging Face Trainers"
               - file: train/gbdt
                 title: "XGBoost/LightGBM guide"
               - file: train/architecture
                 title: "Ray Train Architecture"
+              - file: train/train-with-tune
+                title: "Using Ray Train with Ray Tune"
+              - file: train/check-ingest
+                title: "Configuring Training Datasets"
+              - file: train/predictors
           - file: train/examples
             title: "Examples"
             sections:
@@ -282,6 +244,7 @@ parts:
               - file: serve/advanced-guides/managing-java-deployments
               - file: serve/advanced-guides/migration
               - file: serve/advanced-guides/deploy-vm
+              - file: serve/advanced-guides/from-predictor
           - file: serve/architecture
           - file: serve/tutorials/index
             sections:
@@ -317,6 +280,44 @@ parts:
           - file: rllib/rllib-examples
           - file: rllib/package_ref/index
 
+      - file: ray-air/getting-started
+        title: "Ray AIR"
+        sections:
+          - file: ray-air/user-guides
+            sections:
+              - file: ray-air/computer-vision
+              - 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
+              - file: ray-air/examples/convert_existing_tf_code_to_ray_air
+              - file: ray-air/examples/tfx_tabular_train_to_serve
+              - file: ray-air/examples/huggingface_text_classification
+              - file: ray-air/examples/sklearn_example
+              - file: ray-air/examples/xgboost_example
+              - file: ray-air/examples/analyze_tuning_results
+              - file: ray-air/examples/lightgbm_example
+              - file: ray-air/examples/torch_incremental_learning
+              - file: ray-air/examples/rl_serving_example
+              - file: ray-air/examples/rl_online_example
+              - file: ray-air/examples/rl_offline_example
+              - file: ray-air/examples/upload_to_comet_ml
+              - file: ray-air/examples/upload_to_wandb
+              - file: ray-air/examples/feast_example
+              - file: ray-air/examples/automl_with_ray_air
+              - file: ray-air/examples/batch_tuning
+              - file: ray-air/examples/batch_forecasting
+              - file: ray-air/examples/stablediffusion_batch_prediction
+              - file: ray-air/examples/gptj_deepspeed_fine_tuning
+              - file: ray-air/examples/gptj_batch_prediction
+              - file: ray-air/examples/gptj_serving
+              - file: ray-air/examples/dreambooth_finetuning
+              - file: ray-air/examples/dolly_lightning_fsdp_finetuning
+          - file: ray-air/benchmarks
+
       - file: ray-more-libs/index
         title: More Libraries
         sections:

doc/source/ray-air/preprocessors.rst → doc/source/data/preprocessors.rst

@@ -15,7 +15,7 @@ Ray AIR provides several common preprocessors out of the box and interfaces to d
 Overview
 --------
 
-The most common way of using a preprocessor is by passing it as an argument to the constructor of a :ref:`Trainer <air-trainers>` in conjunction with a :ref:`Ray Data <data>`.
+The most common way of using a preprocessor is by passing it as an argument to the constructor of a Ray Train :ref:`Trainer <train-getting-started>` in conjunction with a :ref:`Ray Data dataset <data>`.
 For example, the following code trains a model with a preprocessor that normalizes the data.
 
 .. literalinclude:: doc_code/preprocessors.py
@@ -119,7 +119,7 @@ Predictor
 A ``Predictor`` can be constructed from a saved ``Checkpoint``. If the ``Checkpoint`` contains a ``Preprocessor``,
 then the ``Preprocessor`` calls ``transform_batch`` on input batches prior to performing inference.
 
-In the following example, we show the Batch Predictor flow. The same logic applies to the :ref:`Online Inference flow <air-key-concepts-online-inference>`.
+In the following example, we show the Batch Predictor flow.
 
 .. literalinclude:: doc_code/preprocessors.py
     :language: python

doc/source/data/user-guide.rst

@@ -24,3 +24,4 @@ show you how achieve several tasks.
     batch_inference
     data-internals
     performance-tips
+    preprocessors

doc/source/ray-air/api/checkpoint.rst

@@ -1,4 +1,4 @@
-.. _air-checkpoint-ref:
+.. _checkpoint-api-ref:
 
 Ray AIR Checkpoint
 ==================

doc/source/ray-air/computer-vision.rst

@@ -184,7 +184,7 @@ Training vision models
             :dedent:
 
         For more in-depth examples, read :doc:`/ray-air/examples/torch_image_example` and
-        :ref:`Using Trainers <air-trainers>`.
+        :ref:`Using Trainers <train-getting-started>`.
 
     .. tab-item:: TensorFlow
 
@@ -203,7 +203,7 @@ Training vision models
             :end-before: __tensorflow_trainer_stop__
             :dedent:
 
-        For more information, read :ref:`Using Trainers <air-trainers>`.
+        For more information, check out :ref:`the Ray Train documentation <train-getting-started>`.
 
 Creating checkpoints
 --------------------
@@ -316,7 +316,7 @@ To NumPy ndarrays like this:
             :dedent:
 
         For more in-depth examples, read :doc:`/ray-air/examples/torch_image_example`
-        and :doc:`/ray-air/examples/serving_guide`.
+        and :ref:`Ray Serve <serve-getting-started>`
 
     .. tab-item:: TensorFlow
 
@@ -336,4 +336,4 @@ To NumPy ndarrays like this:
             :end-before: __tensorflow_online_predict_stop__
             :dedent:
 
-        For more information, read :doc:`/ray-air/examples/serving_guide`.
+        For more information, see :ref:`Ray Serve <serve-getting-started>`.

doc/source/ray-air/examples/automl_with_ray_air.ipynb

@@ -1,6 +1,7 @@
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  "cells": [
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    "id": "596e2463",
    "metadata": {},
@@ -9,6 +10,7 @@
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@@ -21,7 +23,7 @@
     "2. Perform temporal cross-validation on each model configuration in parallel.\n",
     "3. Pick the best performing model as the output of the AutoML system.\n",
     "\n",
-    "We see that these steps fit into the framework of a hyperparameter optimization problem that can be tackled with the [Ray AIR Tuner](air-tuner)!\n",
+    "We see that these steps fit into the framework of a hyperparameter optimization problem that can be tackled with [Ray Tune](tune-main)!\n",
     "\n",
     "In this notebook, we will show how to:\n",
     "1. **Create an AutoML system with Ray AIR** for time series forecasting.\n",
@@ -32,6 +34,7 @@
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@@ -53,6 +56,7 @@
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@@ -94,6 +98,7 @@
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@@ -104,6 +109,7 @@
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@@ -263,6 +269,7 @@
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@@ -387,6 +394,7 @@
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@@ -413,6 +421,7 @@
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@@ -458,6 +467,7 @@
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@@ -501,6 +511,7 @@
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@@ -519,6 +530,7 @@
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@@ -554,6 +566,7 @@
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@@ -583,6 +596,7 @@
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@@ -611,6 +625,7 @@
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@@ -677,6 +692,7 @@
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@@ -739,6 +755,7 @@
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