42411

Add SageMakerProcessingSensor which can be used to wait on a SageMaker processing job.

docs/apache-airflow-providers-amazon/operators/sagemaker.rst

@@ -366,6 +366,20 @@ you can use :class:`~airflow.providers.amazon.aws.sensors.sagemaker.SageMakerAut
     :start-after: [START howto_operator_sagemaker_auto_ml]
     :end-before: [END howto_operator_sagemaker_auto_ml]
 
+.. _howto/sensor:SageMakerProcessingSensor:
+
+Wait on an Amazon SageMaker processing job state
+================================================
+
+To check the state of an Amazon Sagemaker processing job until it reaches a terminal state
+you can use :class:`~airflow.providers.amazon.aws.sensors.sagemaker.SageMakerProcessingSensor`.
+
+.. exampleinclude:: /../../providers/tests/system/amazon/aws/example_sagemaker.py
+    :language: python
+    :dedent: 4
+    :start-after: [START howto_sensor_sagemaker_processing]
+    :end-before: [END howto_sensor_sagemaker_processing]
+
 Reference
 ---------
 

providers/src/airflow/providers/amazon/aws/hooks/sagemaker.py

@@ -153,7 +153,9 @@ class SageMakerHook(AwsBaseHook):
     non_terminal_states = {"InProgress", "Stopping"}
     endpoint_non_terminal_states = {"Creating", "Updating", "SystemUpdating", "RollingBack", "Deleting"}
     pipeline_non_terminal_states = {"Executing", "Stopping"}
+    processing_job_non_terminal_states = {"InProgress", "Stopping"}
     failed_states = {"Failed"}
+    processing_job_failed_states = {*failed_states, "Stopped"}
     training_failed_states = {*failed_states, "Stopped"}
 
     def __init__(self, *args, **kwargs):

providers/src/airflow/providers/amazon/aws/sensors/sagemaker.py

@@ -330,3 +330,35 @@ def get_sagemaker_response(self) -> dict:
 
     def state_from_response(self, response: dict) -> str:
         return response["AutoMLJobStatus"]
+
+
+class SageMakerProcessingSensor(SageMakerBaseSensor):
+    """
+    Poll the processing job until it reaches a terminal state; raise AirflowException with the failure reason.
+
+    .. seealso::
+        For more information on how to use this sensor, take a look at the guide:
+        :ref:`howto/sensor:SageMakerProcessingSensor`
+
+    :param job_name: Name of the processing job to watch.
+    """
+
+    template_fields: Sequence[str] = ("job_name",)
+    template_ext: Sequence[str] = ()
+
+    def __init__(self, *, job_name: str, **kwargs):
+        super().__init__(**kwargs)
+        self.job_name = job_name
+
+    def non_terminal_states(self) -> set[str]:
+        return SageMakerHook.processing_job_non_terminal_states
+
+    def failed_states(self) -> set[str]:
+        return SageMakerHook.processing_job_failed_states
+
+    def get_sagemaker_response(self) -> dict:
+        self.log.info("Poking Sagemaker ProcessingJob %s", self.job_name)
+        return self.hook.describe_processing_job(self.job_name)
+
+    def state_from_response(self, response: dict) -> str:
+        return response["ProcessingJobStatus"]

providers/tests/amazon/aws/sensors/test_sagemaker_processing.py

@@ -0,0 +1,110 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+from __future__ import annotations
+
+from unittest import mock
+
+import pytest
+
+from airflow.exceptions import AirflowException
+from airflow.providers.amazon.aws.hooks.sagemaker import SageMakerHook
+from airflow.providers.amazon.aws.sensors.sagemaker import SageMakerProcessingSensor
+
+DESCRIBE_PROCESSING_INPROGRESS_RESPONSE = {
+    "ProcessingJobStatus": "InProgress",
+    "ResponseMetadata": {
+        "HTTPStatusCode": 200,
+    },
+}
+
+DESCRIBE_PROCESSING_COMPLETED_RESPONSE = {
+    "ProcessingJobStatus": "Completed",
+    "ResponseMetadata": {
+        "HTTPStatusCode": 200,
+    },
+}
+
+DESCRIBE_PROCESSING_FAILED_RESPONSE = {
+    "ProcessingJobStatus": "Failed",
+    "ResponseMetadata": {
+        "HTTPStatusCode": 200,
+    },
+    "FailureReason": "Unknown",
+}
+
+DESCRIBE_PROCESSING_STOPPING_RESPONSE = {
+    "ProcessingJobStatus": "Stopping",
+    "ResponseMetadata": {
+        "HTTPStatusCode": 200,
+    },
+}
+
+DESCRIBE_PROCESSING_STOPPED_RESPONSE = {
+    "ProcessingJobStatus": "Stopped",
+    "ResponseMetadata": {
+        "HTTPStatusCode": 200,
+    },
+}
+
+
+class TestSageMakerProcessingSensor:
+    @mock.patch.object(SageMakerHook, "get_conn")
+    @mock.patch.object(SageMakerHook, "describe_processing_job")
+    def test_sensor_with_failure(self, mock_describe_job, mock_client):
+        mock_describe_job.side_effect = [DESCRIBE_PROCESSING_FAILED_RESPONSE]
+        sensor = SageMakerProcessingSensor(
+            task_id="test_task", poke_interval=2, aws_conn_id="aws_test", job_name="test_job_name"
+        )
+        with pytest.raises(AirflowException):
+            sensor.execute(None)
+        mock_describe_job.assert_called_once_with("test_job_name")
+
+    @mock.patch.object(SageMakerHook, "get_conn")
+    @mock.patch.object(SageMakerHook, "describe_processing_job")
+    def test_sensor_with_stopped(self, mock_describe_job, mock_client):
+        mock_describe_job.side_effect = [DESCRIBE_PROCESSING_STOPPED_RESPONSE]
+        sensor = SageMakerProcessingSensor(
+            task_id="test_task", poke_interval=2, aws_conn_id="aws_test", job_name="test_job_name"
+        )
+        with pytest.raises(AirflowException):
+            sensor.execute(None)
+        mock_describe_job.assert_called_once_with("test_job_name")
+
+    @mock.patch.object(SageMakerHook, "get_conn")
+    @mock.patch.object(SageMakerHook, "__init__")
+    @mock.patch.object(SageMakerHook, "describe_processing_job")
+    def test_sensor(self, mock_describe_job, hook_init, mock_client):
+        hook_init.return_value = None
+
+        mock_describe_job.side_effect = [
+            DESCRIBE_PROCESSING_INPROGRESS_RESPONSE,
+            DESCRIBE_PROCESSING_STOPPING_RESPONSE,
+            DESCRIBE_PROCESSING_COMPLETED_RESPONSE,
+        ]
+        sensor = SageMakerProcessingSensor(
+            task_id="test_task", poke_interval=0, aws_conn_id="aws_test", job_name="test_job_name"
+        )
+
+        sensor.execute(None)
+
+        # make sure we called 3 times(terminated when its completed)
+        assert mock_describe_job.call_count == 3
+
+        # make sure the hook was initialized with the specific params
+        calls = [mock.call(aws_conn_id="aws_test")]
+        hook_init.assert_has_calls(calls)

providers/tests/system/amazon/aws/example_sagemaker.py

@@ -47,6 +47,7 @@
 )
 from airflow.providers.amazon.aws.sensors.sagemaker import (
     SageMakerAutoMLSensor,
+    SageMakerProcessingSensor,
     SageMakerTrainingSensor,
     SageMakerTransformSensor,
     SageMakerTuningSensor,
@@ -390,6 +391,7 @@ def set_up(env_id, role_arn):
     ti.xcom_push(key="raw_data_s3_key", value=raw_data_s3_key)
     ti.xcom_push(key="ecr_repository_name", value=ecr_repository_name)
     ti.xcom_push(key="processing_config", value=processing_config)
+    ti.xcom_push(key="processing_job_name", value=processing_job_name)
     ti.xcom_push(key="input_data_uri", value=input_data_uri)
     ti.xcom_push(key="output_data_uri", value=f"s3://{bucket_name}/{training_output_s3_key}")
     ti.xcom_push(key="training_config", value=training_config)
@@ -518,8 +520,18 @@ def delete_docker_image(image_name):
         task_id="preprocess_raw_data",
         config=test_setup["processing_config"],
     )
+
+    # SageMakerProcessingOperator waits by default, setting as False to test the Sensor below.
+    preprocess_raw_data.wait_for_completion = False
+
     # [END howto_operator_sagemaker_processing]
 
+    # [START howto_sensor_sagemaker_processing]
+    await_preprocess = SageMakerProcessingSensor(
+        task_id="await_preprocess", job_name=test_setup["processing_job_name"]
+    )
+    # [END howto_sensor_sagemaker_processing]
+
     # [START howto_operator_sagemaker_training]
     train_model = SageMakerTrainingOperator(
         task_id="train_model",
@@ -622,6 +634,7 @@ def delete_docker_image(image_name):
         await_automl,
         create_experiment,
         preprocess_raw_data,
+        await_preprocess,
         train_model,
         await_training,
         create_model,