feature: create ProcessingJob from ARN and from name

src/sagemaker/model_monitor/model_monitoring.py

@@ -1761,7 +1761,7 @@ def _get_default_image_uri(region):
 class BaseliningJob(ProcessingJob):
     """Provides functionality to retrieve baseline-specific files output from baselining job."""
 
-    def __init__(self, sagemaker_session, job_name, inputs, outputs):
+    def __init__(self, sagemaker_session, job_name, inputs, outputs, output_kms_key=None):
         """Initializes a Baselining job that tracks a baselining job kicked off by the suggest
         workflow.
 
@@ -1773,12 +1773,18 @@ def __init__(self, sagemaker_session, job_name, inputs, outputs):
             job_name (str): Name of the Amazon SageMaker Model Monitoring Baselining Job.
             inputs ([sagemaker.processing.ProcessingInput]): A list of ProcessingInput objects.
             outputs ([sagemaker.processing.ProcessingOutput]): A list of ProcessingOutput objects.
+            output_kms_key (str): The output kms key associated with the job. Defaults to None
+                if not provided.
 
         """
         self.inputs = inputs
         self.outputs = outputs
         super(BaseliningJob, self).__init__(
-            sagemaker_session=sagemaker_session, job_name=job_name, inputs=inputs, outputs=outputs
+            sagemaker_session=sagemaker_session,
+            job_name=job_name,
+            inputs=inputs,
+            outputs=outputs,
+            output_kms_key=output_kms_key,
         )
 
     @classmethod
@@ -1799,6 +1805,7 @@ def from_processing_job(cls, processing_job):
             processing_job.job_name,
             processing_job.inputs,
             processing_job.outputs,
+            processing_job.output_kms_key,
         )
 
     def baseline_statistics(self, file_name=STATISTICS_JSON_DEFAULT_FILE_NAME, kms_key=None):
@@ -1881,7 +1888,7 @@ class MonitoringExecution(ProcessingJob):
     executions
     """
 
-    def __init__(self, sagemaker_session, job_name, inputs, output, output_kms_key):
+    def __init__(self, sagemaker_session, job_name, inputs, output, output_kms_key=None):
         """Initializes a MonitoringExecution job that tracks a monitoring execution kicked off by
         an Amazon SageMaker Model Monitoring Schedule.
 
@@ -1893,13 +1900,17 @@ def __init__(self, sagemaker_session, job_name, inputs, output, output_kms_key):
             job_name (str): The name of the monitoring execution job.
             output (sagemaker.Processing.ProcessingOutput): The output associated with the
                 monitoring execution.
-            output_kms_key (str): The output kms key associated with the job.
+            output_kms_key (str): The output kms key associated with the job. Defaults to None
+                if not provided.
 
         """
         self.output = output
-        self.output_kms_key = output_kms_key
         super(MonitoringExecution, self).__init__(
-            sagemaker_session=sagemaker_session, job_name=job_name, inputs=inputs, outputs=[output]
+            sagemaker_session=sagemaker_session,
+            job_name=job_name,
+            inputs=inputs,
+            outputs=[output],
+            output_kms_key=output_kms_key,
         )
 
     @classmethod

src/sagemaker/processing.py

@@ -502,9 +502,24 @@ def _set_entrypoint(self, command, user_script_name):
 class ProcessingJob(_Job):
     """Provides functionality to start, describe, and stop processing jobs."""
 
-    def __init__(self, sagemaker_session, job_name, inputs, outputs):
+    def __init__(self, sagemaker_session, job_name, inputs, outputs, output_kms_key=None):
+        """Initializes a Processing job.
+
+        Args:
+            sagemaker_session (sagemaker.session.Session): Session object which
+                manages interactions with Amazon SageMaker APIs and any other
+                AWS services needed. If not specified, one is created using
+                the default AWS configuration chain.
+            job_name (str): Name of the Processing job.
+            inputs ([sagemaker.processing.ProcessingInput]): A list of ProcessingInput objects.
+            outputs ([sagemaker.processing.ProcessingOutput]): A list of ProcessingOutput objects.
+            output_kms_key (str): The output kms key associated with the job. Defaults to None
+                if not provided.
+
+        """
         self.inputs = inputs
         self.outputs = outputs
+        self.output_kms_key = output_kms_key
         super(ProcessingJob, self).__init__(sagemaker_session=sagemaker_session, job_name=job_name)
 
     @classmethod
@@ -586,7 +601,83 @@ def start_new(cls, processor, inputs, outputs, experiment_config):
         # Call sagemaker_session.process using the arguments dictionary.
         processor.sagemaker_session.process(**process_request_args)
 
-        return cls(processor.sagemaker_session, processor._current_job_name, inputs, outputs)
+        return cls(
+            processor.sagemaker_session,
+            processor._current_job_name,
+            inputs,
+            outputs,
+            processor.output_kms_key,
+        )
+
+    @classmethod
+    def from_processing_name(cls, sagemaker_session, processing_job_name):
+        """Initializes a Processing job from a Processing job name.
+
+        Args:
+            processing_job_name (str): Name of the processing job.
+            sagemaker_session (sagemaker.session.Session): Session object which
+                manages interactions with Amazon SageMaker APIs and any other
+                AWS services needed. If not specified, one is created using
+                the default AWS configuration chain.
+
+        Returns:
+            sagemaker.processing.ProcessingJob: The instance of ProcessingJob created
+                using the current job name.
+        """
+        job_desc = sagemaker_session.describe_processing_job(job_name=processing_job_name)
+
+        return cls(
+            sagemaker_session=sagemaker_session,
+            job_name=processing_job_name,
+            inputs=[
+                ProcessingInput(
+                    source=processing_input["S3Input"]["S3Uri"],
+                    destination=processing_input["S3Input"]["LocalPath"],
+                    input_name=processing_input["InputName"],
+                    s3_data_type=processing_input["S3Input"].get("S3DataType"),
+                    s3_input_mode=processing_input["S3Input"].get("S3InputMode"),
+                    s3_data_distribution_type=processing_input["S3Input"].get(
+                        "S3DataDistributionType"
+                    ),
+                    s3_compression_type=processing_input["S3Input"].get("S3CompressionType"),
+                )
+                for processing_input in job_desc["ProcessingInputs"]
+            ],
+            outputs=[
+                ProcessingOutput(
+                    source=job_desc["ProcessingOutputConfig"]["Outputs"][0]["S3Output"][
+                        "LocalPath"
+                    ],
+                    destination=job_desc["ProcessingOutputConfig"]["Outputs"][0]["S3Output"][
+                        "S3Uri"
+                    ],
+                    output_name=job_desc["ProcessingOutputConfig"]["Outputs"][0]["OutputName"],
+                )
+            ],
+            output_kms_key=job_desc["ProcessingOutputConfig"].get("KmsKeyId"),
+        )
+
+    @classmethod
+    def from_processing_arn(cls, sagemaker_session, processing_job_arn):
+        """Initializes a Processing job from a Processing ARN.
+
+        Args:
+            processing_job_arn (str): ARN of the processing job.
+            sagemaker_session (sagemaker.session.Session): Session object which
+                manages interactions with Amazon SageMaker APIs and any other
+                AWS services needed. If not specified, one is created using
+                the default AWS configuration chain.
+
+        Returns:
+            sagemaker.processing.ProcessingJob: The instance of ProcessingJob created
+                using the current job name.
+        """
+        processing_job_name = processing_job_arn.split(":")[5][
+            len("processing-job/") :
+        ]  # This is necessary while the API only vends an arn.
+        return cls.from_processing_name(
+            sagemaker_session=sagemaker_session, processing_job_name=processing_job_name
+        )
 
     def _is_local_channel(self, input_url):
         """Used for Local Mode. Not yet implemented.

tests/unit/test_processing.py

@@ -13,9 +13,15 @@
 from __future__ import absolute_import
 
 import pytest
-from mock import Mock, patch
-
-from sagemaker.processing import ProcessingInput, ProcessingOutput, Processor, ScriptProcessor
+from mock import Mock, patch, MagicMock
+
+from sagemaker.processing import (
+    ProcessingInput,
+    ProcessingOutput,
+    Processor,
+    ScriptProcessor,
+    ProcessingJob,
+)
 from sagemaker.sklearn.processing import SKLearnProcessor
 from sagemaker.network import NetworkConfig
 
@@ -24,11 +30,51 @@
 ROLE = "arn:aws:iam::012345678901:role/SageMakerRole"
 CUSTOM_IMAGE_URI = "012345678901.dkr.ecr.us-west-2.amazonaws.com/my-custom-image-uri"
 
+PROCESSING_JOB_DESCRIPTION = {
+    "ProcessingInputs": [
+        {
+            "InputName": "my_dataset",
+            "S3Input": {
+                "S3Uri": "s3://path/to/my/dataset/census.csv",
+                "LocalPath": "/container/path/",
+                "S3DataType": "S3Prefix",
+                "S3InputMode": "File",
+                "S3DataDistributionType": "FullyReplicated",
+                "S3CompressionType": "None",
+            },
+        },
+        {
+            "InputName": "code",
+            "S3Input": {
+                "S3Uri": "mocked_s3_uri_from_upload_data",
+                "LocalPath": "/opt/ml/processing/input/code",
+                "S3DataType": "S3Prefix",
+                "S3InputMode": "File",
+                "S3DataDistributionType": "FullyReplicated",
+                "S3CompressionType": "None",
+            },
+        },
+    ],
+    "ProcessingOutputConfig": {
+        "Outputs": [
+            {
+                "OutputName": "my_output",
+                "S3Output": {
+                    "S3Uri": "s3://uri/",
+                    "LocalPath": "/container/path/",
+                    "S3UploadMode": "EndOfJob",
+                },
+            }
+        ],
+        "KmsKeyId": "arn:aws:kms:us-west-2:012345678901:key/output-kms-key",
+    },
+}
+
 
 @pytest.fixture()
 def sagemaker_session():
     boto_mock = Mock(name="boto_session", region_name=REGION)
-    session_mock = Mock(
+    session_mock = MagicMock(
         name="sagemaker_session",
         boto_session=boto_mock,
         boto_region_name=REGION,
@@ -42,6 +88,9 @@ def sagemaker_session():
     )
     session_mock.download_data = Mock(name="download_data")
     session_mock.expand_role.return_value = ROLE
+    session_mock.describe_processing_job = MagicMock(
+        name="describe_processing_job", return_value=PROCESSING_JOB_DESCRIPTION
+    )
     return session_mock
 
 
@@ -388,6 +437,24 @@ def test_processor_with_all_parameters(sagemaker_session):
     sagemaker_session.process.assert_called_with(**expected_args)
 
 
+def test_processing_job_from_processing_arn(sagemaker_session):
+    processing_job = ProcessingJob.from_processing_arn(
+        sagemaker_session=sagemaker_session,
+        processing_job_arn="arn:aws:sagemaker:dummy-region:dummy-account-number:processing-job/dummy-job-name",
+    )
+    assert isinstance(processing_job, ProcessingJob)
+    assert [
+        processing_input._to_request_dict() for processing_input in processing_job.inputs
+    ] == PROCESSING_JOB_DESCRIPTION["ProcessingInputs"]
+    assert [
+        processing_output._to_request_dict() for processing_output in processing_job.outputs
+    ] == PROCESSING_JOB_DESCRIPTION["ProcessingOutputConfig"]["Outputs"]
+    assert (
+        processing_job.output_kms_key
+        == PROCESSING_JOB_DESCRIPTION["ProcessingOutputConfig"]["KmsKeyId"]
+    )
+
+
 def _get_script_processor(sagemaker_session):
     return ScriptProcessor(
         role=ROLE,