[air] Remove checkpoint user guide and update key concepts and docstr…

…ing (ray-project#27455)

doc/source/_toc.yml

@@ -15,7 +15,6 @@ parts:
     - file: ray-air/user-guides
       sections:
         - file: ray-air/preprocessors
-        - file: ray-air/checkpoints
         - file: ray-air/check-ingest
         - file: ray-air/trainer
         - file: ray-air/tuner

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

@@ -65,6 +65,37 @@
 print(best_result)
 # __air_tuner_end__
 
+# __air_checkpoints_start__
+checkpoint = result.checkpoint
+print(checkpoint)
+# Checkpoint(local_path=..../checkpoint_000005)
+
+tuned_checkpoint = result_grid.get_best_result().checkpoint
+print(tuned_checkpoint)
+# Checkpoint(local_path=..../checkpoint_000005)
+# __air_checkpoints_end__
+
+# __checkpoint_adhoc_start__
+from ray.train.tensorflow import TensorflowCheckpoint
+import tensorflow as tf
+
+# This can be a trained model.
+def build_model() -> tf.keras.Model:
+    model = tf.keras.Sequential(
+        [
+            tf.keras.layers.InputLayer(input_shape=(1,)),
+            tf.keras.layers.Dense(1),
+        ]
+    )
+    return model
+
+
+model = build_model()
+
+checkpoint = TensorflowCheckpoint.from_model(model)
+# __checkpoint_adhoc_end__
+
+
 # __air_batch_predictor_start__
 from ray.train.batch_predictor import BatchPredictor
 from ray.train.xgboost import XGBoostPredictor

doc/source/ray-air/key-concepts.rst

@@ -43,9 +43,8 @@ See the documentation on :ref:`Trainers <air-trainers>`.
     :start-after: __air_trainer_start__
     :end-before: __air_trainer_end__
 
-
-
-Trainer objects will produce a :ref:`Result <air-results-ref>` object after calling ``.fit()``.  These objects will contain training metrics as long as checkpoints to retrieve the best model.
+Trainer objects produce a :ref:`Result <air-results-ref>` object after calling ``.fit()``.
+These objects contain training metrics as well as checkpoints to retrieve the best model.
 
 .. literalinclude:: doc_code/air_key_concepts.py
     :language: python
@@ -65,11 +64,40 @@ Tuners can work seamlessly with any Trainer but also can support arbitrary train
     :start-after: __air_tuner_start__
     :end-before: __air_tuner_end__
 
+.. _air-checkpoints-doc:
+
+Checkpoints
+-----------
+
+The AIR trainers, tuners, and custom pretrained model generate :class:`a framework-specific Checkpoint <ray.air.Checkpoint>` object.
+Checkpoints are a common interface for models that are used across different AIR components and libraries.
+
+There are two main ways to generate a checkpoint.
+
+Checkpoint objects can be retrieved from the Result object returned by a Trainer or Tuner ``.fit()`` call.
+
+.. literalinclude:: doc_code/air_key_concepts.py
+    :language: python
+    :start-after: __air_checkpoints_start__
+    :end-before: __air_checkpoints_end__
+
+You can also generate a checkpoint from a pretrained model. Each AIR supported machine learning (ML) framework has
+a ``Checkpoint`` object that can be used to generate an AIR checkpoint:
+
+.. literalinclude:: doc_code/air_key_concepts.py
+    :language: python
+    :start-after: __checkpoint_adhoc_start__
+    :end-before: __checkpoint_adhoc_end__
+
+
+Checkpoints can be used to instantiate a :class:`Predictor`, :class:`BatchPredictor`, or :class:`PredictorDeployment` classes,
+as seen below.
+
 
 Batch Predictor
 ---------------
 
-You can take a trained model and do batch inference using the BatchPredictor object.
+You can take a checkpoint and do batch inference using the BatchPredictor object.
 
 .. literalinclude:: doc_code/air_key_concepts.py
     :language: python

doc/source/ray-air/user-guides.rst

@@ -23,17 +23,6 @@ AIR User Guides
         :text: Using Preprocessors
         :classes: btn-link btn-block stretched-link
 
-
-    ---
-    :img-top: /ray-overview/images/ray_svg_logo.svg
-
-    +++
-    .. link-button:: /ray-air/checkpoints
-        :type: ref
-        :text: Using Checkpoints
-        :classes: btn-link btn-block stretched-link
-
-
     ---
     :img-top: /ray-overview/images/ray_svg_logo.svg