chore: improve comments

Author: evakravi

src/sagemaker/jumpstart/estimator.py

@@ -489,6 +489,9 @@ def __init__(
                 private Docker registry where your training image is hosted (Default: None).
                 When it's set to None, SageMaker will not do authentication before pulling the image
                 in the private Docker registry. (Default: None).
+
+        Raises:
+            ValueError: If the model ID is not recognized by JumpStart.
         """
 
         if not is_valid_model_id(

src/sagemaker/jumpstart/model.py

@@ -254,6 +254,9 @@ def __init__(
                 the SageMaker Python SDK attempts to use either the CodeCommit
                 credential helper or local credential storage for authentication.
                 (Default: None).
+
+        Raises:
+            ValueError: If the model ID is not recognized by JumpStart.
         """
 
         if not is_valid_model_id(

tests/integ/sagemaker/jumpstart/estimator/test_jumpstart_estimator.py

@@ -43,13 +43,15 @@ def test_jumpstart_estimator(setup):
         max_run=259200,  # avoid exceeding resource limits
     )
 
+    # uses ml.p3.2xlarge instance
     estimator.fit(
         {
             "training": f"s3://{get_jumpstart_content_bucket(JUMPSTART_DEFAULT_REGION_NAME)}/"
             f"{get_training_dataset_for_model_and_version(model_id, model_version)}",
         }
     )
 
+    # uses ml.p3.2xlarge instance
     predictor = estimator.deploy(
         tags=[{"Key": JUMPSTART_TAG, "Value": os.environ[ENV_VAR_JUMPSTART_SDK_TEST_SUITE_ID]}],
         role=get_sm_session().get_caller_identity_arn(),

tests/integ/sagemaker/jumpstart/model/test_jumpstart_model.py

@@ -40,6 +40,7 @@ def test_non_prepacked_jumpstart_model(setup):
         sagemaker_session=get_sm_session(),
     )
 
+    # uses ml.m5.4xlarge instance
     predictor = model.deploy(
         tags=[{"Key": JUMPSTART_TAG, "Value": os.environ[ENV_VAR_JUMPSTART_SDK_TEST_SUITE_ID]}],
     )
@@ -62,6 +63,7 @@ def test_prepacked_jumpstart_model(setup):
         sagemaker_session=get_sm_session(),
     )
 
+    # uses ml.p3.2xlarge instance
     predictor = model.deploy(
         tags=[{"Key": JUMPSTART_TAG, "Value": os.environ[ENV_VAR_JUMPSTART_SDK_TEST_SUITE_ID]}],
     )