Updated doc.

Author: lu-ohai

docs/source/user_guide/model_deployment/deploy.rst

@@ -67,6 +67,8 @@ The Data Science Model Deployment supports service managed conda runtime and cus
 ModelDeploymentContainerRuntime
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
+To use the ``ModelDeploymentContainerRuntime``, you need to first push the image to `OCI container registry <https://docs.oracle.com/en-us/iaas/Content/Registry/Concepts/registryoverview.htm>`_. See `Creating a Repository <https://docs.oracle.com/en-us/iaas/Content/Registry/Tasks/registrycreatingarepository.htm>`_ and `Pushing Images Using the Docker CLI <https://docs.oracle.com/en-us/iaas/Content/Registry/Tasks/registrycreatingarepository.htm>`_ for more details.
+ 
 You can define the model deployment container runtime by passing the following properties to ``ModelDeploymentContainerRuntime`` object:
 
   * ``model_uri``: The model ocid or path to model artifacts directory that is used in the model deployment.
@@ -179,7 +181,7 @@ A ``ModelDeployment`` object can be serialized to a YAML file by calling ``to_ya
         kind: infrastructure
             ...
     """")
-    
+
     deployment.deploy(wait_for_completion=False)
 
 Here is an example of a YAML file representing the ``ModelDeployment`` with docker container runtime defined in the preceding examples:

docs/source/user_guide/model_deployment/update.rst

@@ -15,6 +15,6 @@ Or, you could update the instance shape with:
 
 .. code-block:: python3
 
-    deployment.infrastructure.with_shape_name("VM.Standard.E4.Flex")
+    deployment.infrastructure.with_shape_name("VM.Standard.E4.Flex").with_shape_config_details(ocpus=2, memory_in_gbs=32)
     deployment.update()