[docs/air] Refactor AIR documentation -- orphan examples (1/n) (ray-p…

…roject#37741) * orphan all examples * orphan examples * add getting-started-for-serve * update-reference
lmco · Jul 25, 2023 · 5f1e274 · 5f1e274
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diff --git a/doc/source/_toc.yml b/doc/source/_toc.yml
@@ -54,32 +54,6 @@ parts:
               - file: ray-air/predictors
               - file: ray-air/computer-vision
               - file: ray-air/deployment
-          - file: ray-air/examples/index
-            sections:
-              - 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_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
 

diff --git a/doc/source/ray-air/api/checkpoint.rst b/doc/source/ray-air/api/checkpoint.rst
@@ -1,4 +1,5 @@
 .. _air-checkpoint-ref:
+.. _checkpoint-api-ref:
 
 Ray AIR Checkpoint
 ==================

diff --git a/doc/source/ray-air/examples/analyze_tuning_results.ipynb b/doc/source/ray-air/examples/analyze_tuning_results.ipynb
@@ -1,6 +1,7 @@
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diff --git a/doc/source/ray-air/examples/automl_with_ray_air.ipynb b/doc/source/ray-air/examples/automl_with_ray_air.ipynb
@@ -1,6 +1,7 @@
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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",
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diff --git a/doc/source/ray-air/examples/batch_forecasting.ipynb b/doc/source/ray-air/examples/batch_forecasting.ipynb
@@ -1,6 +1,7 @@
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@@ -482,7 +491,7 @@
     "Inside this trainable function:\n",
     "- 📖 The input must include a `config` argument. \n",
     "- 📈 Inside the function, the tuning metric (a model's loss or error) must be calculated and reported using `session.report()`.\n",
-    "- ✔️ Optionally [checkpoint](air-checkpoints-doc) (save) the model for fault tolerance and easy deployment later.\n",
+    "- ✔️ Optionally [checkpoint](checkpoint-api-ref) (save) the model for fault tolerance and easy deployment later.\n",
     "\n",
     "```{tip}\n",
     "Ray Tune has two ways of [defining a trainable](tune_60_seconds_trainables), namely the Function API and the Class API. Both are valid ways of defining a trainable, but *the Function API is generally recommended*.\n",
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