deprecation: deprecate Serverless Lambda model-predictor

Author: mufaddal-rohawala

doc/api/inference/model.rst

@@ -16,7 +16,3 @@ Model
     :undoc-members:
     :show-inheritance:
 
-.. autoclass:: sagemaker.serverless.model.LambdaModel
-    :members:
-    :undoc-members:
-    :show-inheritance:

doc/api/inference/predictors.rst

@@ -7,8 +7,3 @@ Make real-time predictions against SageMaker endpoints with Python objects
     :members:
     :undoc-members:
     :show-inheritance:
-
-.. autoclass:: sagemaker.serverless.predictor.LambdaPredictor
-    :members:
-    :undoc-members:
-    :show-inheritance:

doc/overview.rst

@@ -1063,50 +1063,6 @@ You can also find these notebooks in the **Advanced Functionality** section of t
 For information about using sample notebooks in a SageMaker notebook instance, see `Use Example Notebooks <https://docs.aws.amazon.com/sagemaker/latest/dg/howitworks-nbexamples.html>`__
 in the AWS documentation.
 
-********************
-Serverless Inference
-********************
-
-You can use the SageMaker Python SDK to perform serverless inference on Lambda.
-
-To deploy models to Lambda, you must complete the following prerequisites:
-
-- `Package your model and inference code as a container image. <https://docs.aws.amazon.com/lambda/latest/dg/images-create.html>`_
-- `Create a role that lists Lambda as a trusted entity. <https://docs.aws.amazon.com/lambda/latest/dg/lambda-intro-execution-role.html#permissions-executionrole-console>`_
-
-After completing the prerequisites, you can deploy your model to Lambda using
-the `LambdaModel`_ class.
-
-.. code:: python
-
-   from sagemaker.serverless import LambdaModel
-
-   image_uri = "123456789012.dkr.ecr.us-west-2.amazonaws.com/my-lambda-repository:latest"
-   role = "arn:aws:iam::123456789012:role/MyLambdaExecutionRole"
-
-   model = LambdaModel(image_uri=image_uri, role=role)
-   predictor = model.deploy("my-lambda-function", timeout=20, memory_size=4092)
-
-The ``deploy`` method returns a `LambdaPredictor`_  instance. Use the
-`LambdaPredictor`_ ``predict`` method to perform inference on Lambda.
-
-.. code:: python
-
-   url = "https://example.com/cat.jpeg"
-   predictor.predict({"url": url})  # {'class': 'tabby'}
-
-Once you are done performing inference on Lambda, free the `LambdaModel`_ and
-`LambdaPredictor`_ resources using the ``delete_model`` and ``delete_predictor``
-methods.
-
-.. code:: python
-
-   model.delete_model()
-   predictor.delete_predictor()
-
-.. _LambdaModel : https://sagemaker.readthedocs.io/en/stable/api/inference/model.html#sagemaker.serverless.model.LambdaModel
-.. _LambdaPredictor : https://sagemaker.readthedocs.io/en/stable/api/inference/predictors.html#sagemaker.serverless.predictor.LambdaPredictor
-
 ******************
 SageMaker Workflow
 ******************

src/sagemaker/serverless/model.py

@@ -12,84 +12,12 @@
 # language governing permissions and limitations under the License.
 """Models that can be deployed to serverless compute."""
 from __future__ import absolute_import
+from sagemaker.deprecations import deprecated
 
-import time
-from typing import Optional
 
-import boto3
-import botocore
+@deprecated
+class LambdaModel:
+    """A model that can be deployed to Lambda.
 
-from sagemaker.model import ModelBase
-from sagemaker.deprecations import deprecation_warning
-from .predictor import LambdaPredictor
-
-
-@deprecation_warning(
-    msg="Based on customer experience and feedback an"
-    " alternative support will be added in near future",
-    date="10/27/2021",
-)
-class LambdaModel(ModelBase):
-    """A model that can be deployed to Lambda."""
-
-    def __init__(
-        self, image_uri: str, role: str, client: Optional[botocore.client.BaseClient] = None
-    ) -> None:
-        """Initialize instance attributes.
-
-        Arguments:
-            image_uri: URI of a container image in the Amazon ECR registry. The image
-                should contain a handler that performs inference.
-            role: The Amazon Resource Name (ARN) of the IAM role that Lambda will assume
-                when it performs inference
-            client: The Lambda client used to interact with Lambda.
-        """
-        self._client = client or boto3.client("lambda")
-        self._image_uri = image_uri
-        self._role = role
-
-    def deploy(
-        self, function_name: str, timeout: int, memory_size: int, wait: bool = True
-    ) -> LambdaPredictor:
-        """Create a Lambda function using the image specified in the constructor.
-
-        Arguments:
-            function_name: The name of the function.
-            timeout: The number of seconds that the function can run for before being terminated.
-            memory_size: The amount of memory in MB that the function has access to.
-            wait: If true, wait until the deployment completes (default: True).
-
-        Returns:
-            A LambdaPredictor instance that performs inference using the specified image.
-        """
-        response = self._client.create_function(
-            FunctionName=function_name,
-            PackageType="Image",
-            Role=self._role,
-            Code={
-                "ImageUri": self._image_uri,
-            },
-            Timeout=timeout,
-            MemorySize=memory_size,
-        )
-
-        if not wait:
-            return LambdaPredictor(function_name, client=self._client)
-
-        # Poll function state.
-        polling_interval = 5
-        while response["State"] == "Pending":
-            time.sleep(polling_interval)
-            response = self._client.get_function_configuration(FunctionName=function_name)
-
-        if response["State"] != "Active":
-            raise RuntimeError("Failed to deploy model to Lambda: %s" % response["StateReason"])
-
-        return LambdaPredictor(function_name, client=self._client)
-
-    def delete_model(self) -> None:
-        """Destroy resources associated with this model.
-
-        This method does not delete the image specified in the constructor. As
-        a result, this method is a no-op.
-        """
+    note:: Deprecated in versions > v2.66.0. An alternative support will be added in near future.
+    """

src/sagemaker/serverless/predictor.py

@@ -12,76 +12,12 @@
 # language governing permissions and limitations under the License.
 """Predictors that are hosted on serverless compute."""
 from __future__ import absolute_import
+from sagemaker.deprecations import deprecated
 
-from typing import Optional, Tuple
 
-import boto3
-import botocore
+@deprecated
+class LambdaPredictor:
+    """A deployed model hosted on Lambda.
 
-from sagemaker import deserializers, serializers
-from sagemaker.predictor import PredictorBase
-from sagemaker.deprecations import deprecation_warning
-
-
-@deprecation_warning(
-    msg="Based on customer experience and feedback an"
-    " alternative support will be added in near future",
-    date="10/27/2021",
-)
-class LambdaPredictor(PredictorBase):
-    """A deployed model hosted on Lambda."""
-
-    def __init__(
-        self, function_name: str, client: Optional[botocore.client.BaseClient] = None
-    ) -> None:
-        """Initialize instance attributes.
-
-        Arguments:
-            function_name: The name of the function.
-            client: The Lambda client used to interact with Lambda.
-        """
-        self._client = client or boto3.client("lambda")
-        self._function_name = function_name
-        self._serializer = serializers.JSONSerializer()
-        self._deserializer = deserializers.JSONDeserializer()
-
-    def predict(self, data: dict) -> dict:
-        """Invoke the Lambda function specified in the constructor.
-
-        This function is synchronous. It will only return after the function
-        has produced a prediction.
-
-        Arguments:
-            data: The data sent to the Lambda function as input.
-
-        Returns:
-            The data returned by the Lambda function.
-        """
-        response = self._client.invoke(
-            FunctionName=self._function_name,
-            InvocationType="RequestResponse",
-            Payload=self._serializer.serialize(data),
-        )
-        return self._deserializer.deserialize(
-            response["Payload"],
-            response["ResponseMetadata"]["HTTPHeaders"]["content-type"],
-        )
-
-    def delete_predictor(self) -> None:
-        """Destroy the Lambda function specified in the constructor."""
-        self._client.delete_function(FunctionName=self._function_name)
-
-    @property
-    def content_type(self) -> str:
-        """The MIME type of the data sent to the Lambda function."""
-        return self._serializer.CONTENT_TYPE
-
-    @property
-    def accept(self) -> Tuple[str]:
-        """The content type(s) that are expected from the Lambda function."""
-        return self._deserializer.ACCEPT
-
-    @property
-    def function_name(self) -> str:
-        """The name of the Lambda function this predictor invokes."""
-        return self._function_name
+    note:: Deprecated in versions > v2.66.0. An alternative support will be added in near future.
+    """