Merge branch 'master' into master

Author: zuoyuanh

doc/api/inference/deserializers.rst

@@ -1,8 +1,12 @@
 Deserializers
 -------------
 
-.. automodule:: sagemaker.deserializers
+.. automodule:: sagemaker.base_deserializers
     :members:
     :undoc-members:
     :show-inheritance:
 
+.. automodule:: sagemaker.deserializers
+    :members:
+    :undoc-members:
+    :show-inheritance:

doc/api/inference/serializers.rst

@@ -1,8 +1,12 @@
 Serializers
 -----------
 
-.. automodule:: sagemaker.serializers
+.. automodule:: sagemaker.base_serializers
     :members:
     :undoc-members:
     :show-inheritance:
 
+.. automodule:: sagemaker.serializers
+    :members:
+    :undoc-members:
+    :show-inheritance:

doc/workflows/airflow/using_workflow.rst

@@ -22,6 +22,16 @@ the configuration dictionary in the SageMaker Python SDK. Currently, the followi
 * ``SageMakerTransformOperator``
 * ``SageMakerEndpointConfigOperator``
 * ``SageMakerEndpointOperator``
+* ``SageMakerProcessingOperator``
+* ``SageMakerDeleteModelOperator``
+* ``SageMakerStartPipelineOperator``
+* ``SageMakerStopPipelineOperator``
+* ``SageMakerRegisterModelVersionOperator``
+* ``SageMakerAutoMLOperator``
+* ``SageMakerCreateExperimentOperator``
+
+You can look into `the Apache Airflow documentation <https://airflow.apache.org/docs/apache-airflow-providers-amazon/stable/operators/sagemaker.html>`_ for the full
+list of operators with examples.
 
 2. PythonOperator: Airflow built-in operator that executes Python callables. You can use the PythonOperator to execute
 operations in the SageMaker Python SDK to create a SageMaker workflow.

tests/integ/sagemaker/jumpstart/retrieve_uri/test_transfer_learning.py

@@ -28,6 +28,7 @@
 from sagemaker import script_uris
 from sagemaker import model_uris
 from sagemaker import hyperparameters
+import pytest
 
 
 def test_jumpstart_transfer_learning_retrieve_functions(setup):
@@ -71,7 +72,12 @@ def test_jumpstart_transfer_learning_retrieve_functions(setup):
         base_name="huggingface",
     )
 
-    training_job.create_training_job()
+    try:
+        training_job.create_training_job()
+    except Exception as e:
+        if "ResourceInUse" in str(e):
+            pytest.xfail("Known issue with conflicting resources.")
+        raise
     training_job.wait_until_training_job_complete()
 
     # inference