documentation: add dry-run and resource reuse docs to existing RST pages

docs/model_customization/deploy_sagemaker_endpoint.rst

@@ -75,3 +75,65 @@ production deployments.
    model_builder = ModelBuilder(model=model_package)
    model = model_builder.build(model_name="my-registered-model")
    endpoint = model_builder.deploy(endpoint_name="my-endpoint")
+
+
+Reuse Deployed Resources
+--------------------------
+
+Pass ``reuse_resources=True`` to ``build()`` and ``deploy()`` to avoid creating duplicate
+endpoints when deploying the same model source multiple times.
+
+On first deploy, the SDK tags the endpoint with ``sagemaker.amazonaws.com/model-source``
+derived from the model's stable source identifier (model package ARN, escrow URI, S3 path,
+or JumpStart model ID). On subsequent deploys with ``reuse_resources=True``, the SDK discovers
+the existing endpoint by that tag and returns it instead of creating a new one.
+
+.. list-table::
+   :header-rows: 1
+
+   * - Model input style
+     - Source ID used for tag
+   * - ``ModelPackage``
+     - Model package ARN
+   * - ``BaseTrainer`` (with completed training job)
+     - Model package ARN if available, otherwise escrow resolution
+   * - ``TrainingJob``
+     - Via model package ARN or escrow resolution
+   * - Raw S3 URI string
+     - The S3 path itself
+   * - JumpStart model ID string
+     - The model ID string
+
+.. code-block:: python
+
+   from sagemaker.serve import ModelBuilder
+
+   builder = ModelBuilder(
+       model=my_trainer,  # or ModelPackage, TrainingJob, S3 URI, etc.
+       role_arn="arn:aws:iam::123456789012:role/MySageMakerRole",
+       instance_type="ml.p4d.24xlarge",
+       image_uri="my-inference-image:latest",
+   )
+
+   # build() checks for an existing Model with matching source tag
+   builder.build(region="us-east-1", reuse_resources=True)
+
+   # deploy() checks for an existing Endpoint with matching source tag
+   endpoint = builder.deploy(
+       endpoint_name="my-endpoint",
+       instance_type="ml.p4d.24xlarge",
+       reuse_resources=True,
+   )
+   # If a match is found: returns the existing endpoint
+   # If no match: creates a new endpoint as normal
+
+Without ``reuse_resources=True`` (the default), every deploy creates a new endpoint. The
+model-source tag is still applied so that future deploys with reuse enabled can discover it.
+
+.. note::
+
+   The ``reuse_resources`` flag must be passed to each call independently — it is not
+   inherited between ``build()`` and ``deploy()``.
+
+   Inference component builds (``modelbuilder_list``) manage their own reuse by component
+   name and bypass the endpoint-return reuse gate.

docs/model_customization/evaluation.rst

@@ -17,3 +17,44 @@ Launch evaluation jobs with the following options:
    ../../v3-examples/model-customization-examples/custom_scorer_demo
    ../../v3-examples/model-customization-examples/benchmark_demo
    ../../v3-examples/nova-examples/evaluation-benchmark-and-custom-scorer
+
+
+Dry-Run Validation
+-------------------
+
+Pass ``dry_run=True`` to ``evaluate()`` to validate your evaluation configuration without
+submitting a job or consuming compute. The SDK runs all validation (IAM role resolution,
+model resolution, dataset path existence) and then stops before launching the evaluation
+pipeline. Returns ``None`` on success, raises ``ValueError`` on validation failure.
+
+Supported on all evaluators: ``BenchMarkEvaluator``, ``CustomScorerEvaluator``, and
+``LLMAsJudgeEvaluator``.
+
+.. code-block:: python
+
+   from sagemaker.train.evaluate import BenchMarkEvaluator, get_benchmarks
+
+   Benchmark = get_benchmarks()
+
+   evaluator = BenchMarkEvaluator(
+       benchmark=Benchmark.MMLU,
+       model="arn:aws:sagemaker:us-east-1:123456789012:model-package/my-models/3",
+       s3_output_path="s3://my-bucket/eval-output/",
+   )
+
+   # Validate without launching — returns None on success
+   evaluator.evaluate(dry_run=True)
+
+.. code-block:: python
+
+   from sagemaker.train.evaluate import CustomScorerEvaluator
+
+   evaluator = CustomScorerEvaluator(
+       model="my-model-package-arn",
+       evaluation_dataset="s3://my-bucket/eval-data.jsonl",
+       s3_output_path="s3://my-bucket/custom-eval-output/",
+       scorer_function=my_scorer,
+   )
+
+   # Raises ValueError if dataset path does not exist
+   evaluator.evaluate(dry_run=True)

docs/model_customization/model_customization.rst

@@ -53,6 +53,33 @@ Key Features
   with clear precedence. Use ``get_resolved_recipe()`` to inspect the merged configuration
   before job submission. See :doc:`finetuning_serverful` and :doc:`finetuning_hyperpod` for examples.
 
+**Dry-Run Validation**
+  Pass ``dry_run=True`` to ``train()`` to run the validation steps without submitting a job 
+  or consuming compute. Returns ``None`` on success, raises ``ValueError`` on validation failure.
+
+  Supported on all trainers (SFT, DPO, RLVR, RLAIF, CPT) and ``ModelTrainer.train()``.
+  Works across serverless, serverful (``TrainingJobCompute``), and HyperPod
+  (``HyperPodCompute``) compute modes. Validates S3 URIs, DataSet ARNs, and ``DataSet``
+  objects.
+
+  Also available on evaluators — see :doc:`evaluation` for details.
+
+  .. code-block:: python
+
+     from sagemaker.train import SFTTrainer
+     from sagemaker.train.common import TrainingType
+
+     trainer = SFTTrainer(
+         model="meta-textgeneration-llama-3-2-1b-instruct",
+         training_type=TrainingType.LORA,
+         model_package_group="my-finetuned-models",
+         training_dataset="s3://my-bucket/train.jsonl",
+         accept_eula=True,
+     )
+
+     # Validate without submitting — returns None on success
+     trainer.train(dry_run=True)
+
 **Job Notifications**
   Receive SNS notifications when training jobs complete, fail, or stop. Uses EventBridge
   rules to route SageMaker Training Job status changes to your SNS topic. 

sagemaker-serve/src/sagemaker/serve/bedrock_model_builder.py

@@ -103,9 +103,8 @@ class BedrockModelBuilder:
         tags each custom model it creates with the model source and, on a
         subsequent deploy of the same source, reuses the existing custom model
         (and its active deployment if present) instead of creating duplicates,
-        logging a warning rather than raising. This matters because Bedrock
-        custom-model import is slow and consumes the limited imported-model
-        quota per account/region.
+        logging a warning rather than raising. To avoid redeploying the same
+        model artifacts, opt into resource reuse with ``reuse_resources=True``.
 
     Args:
         model: The model to deploy. Can be a ModelTrainer, MultiTurnRLTrainer,

v3-examples/model-customization-examples/bedrock-modelbuilder-deployment.ipynb

@@ -275,7 +275,7 @@
    "source": [
     "## Reusing an Existing Custom Model\n",
     "\n",
-    "Importing a custom model into Bedrock is slow and consumes the limited imported-model quota per account/region. If you redeploy the *same* model artifacts, you can avoid a duplicate import by opting into resource reuse with `reuse_resources=True`.\n",
+    "To avoid redeploying the *same* model artifacts, you can opt into resource reuse with `reuse_resources=True`.\n",
     "\n",
     "When enabled, `BedrockModelBuilder` tags each custom model it creates with the model source and, on a later deploy of the same source, reuses the existing custom model (and its active deployment if one exists) instead of creating duplicates. It logs a warning rather than raising, and the returned response includes the `modelArn` that was reused.\n",
     "\n",