fix: pipeline upsert failed to pass parallelism_config to update

Author: qidewenwhen

src/sagemaker/workflow/pipeline.py

@@ -294,7 +294,7 @@ def upsert(
             if not (error_code == "ValidationException" and "already exists" in error_message):
                 raise ce
             # already exists
-            response = self.update(role_arn, description)
+            response = self.update(role_arn, description, parallelism_config=parallelism_config)
             # add new tags to existing resource
             if tags is not None:
                 old_tags = self.sagemaker_session.sagemaker_client.list_tags(

tests/integ/sagemaker/workflow/test_workflow.py

@@ -24,6 +24,7 @@
 
 import pandas as pd
 
+from sagemaker.utils import retry_with_backoff
 from tests.integ.sagemaker.workflow.helpers import wait_pipeline_execution
 from tests.integ.s3_utils import extract_files_from_s3
 from sagemaker.workflow.model_step import (
@@ -1002,7 +1003,7 @@ def test_create_and_update_with_parallelism_config(
         assert response["ParallelismConfiguration"]["MaxParallelExecutionSteps"] == 50
 
         pipeline.parameters = [ParameterInteger(name="InstanceCount", default_value=1)]
-        response = pipeline.update(role, parallelism_config={"MaxParallelExecutionSteps": 55})
+        response = pipeline.upsert(role, parallelism_config={"MaxParallelExecutionSteps": 55})
         update_arn = response["PipelineArn"]
         assert re.match(
             rf"arn:aws:sagemaker:{region_name}:\d{{12}}:pipeline/{pipeline_name}",
@@ -1019,6 +1020,100 @@ def test_create_and_update_with_parallelism_config(
             pass
 
 
+def test_create_and_start_without_parallelism_config_override(
+    pipeline_session, role, pipeline_name, script_dir
+):
+    sklearn_train = SKLearn(
+        framework_version="0.20.0",
+        entry_point=os.path.join(script_dir, "train.py"),
+        instance_type="ml.m5.xlarge",
+        sagemaker_session=pipeline_session,
+        role=role,
+    )
+
+    train_steps = [
+        TrainingStep(
+            name=f"my-train-{count}",
+            display_name="TrainingStep",
+            description="description for Training step",
+            step_args=sklearn_train.fit(),
+        )
+        for count in range(2)
+    ]
+    pipeline = Pipeline(
+        name=pipeline_name,
+        steps=train_steps,
+        sagemaker_session=pipeline_session,
+    )
+
+    try:
+        pipeline.create(role, parallelism_config=dict(MaxParallelExecutionSteps=1))
+        # No ParallelismConfiguration given in pipeline.start, so it won't override that in pipeline.create
+        execution = pipeline.start(parallelism_config=dict(MaxParallelExecutionSteps=2))
+
+        def validate():
+            # Only one step would be scheduled initially
+            assert len(execution.list_steps()) == 1
+
+        retry_with_backoff(validate, num_attempts=4)
+
+        wait_pipeline_execution(execution=execution)
+
+    finally:
+        try:
+            pipeline.delete()
+        except Exception:
+            pass
+
+
+def test_create_and_start_with_parallelism_config_override(
+    pipeline_session, role, pipeline_name, script_dir
+):
+    sklearn_train = SKLearn(
+        framework_version="0.20.0",
+        entry_point=os.path.join(script_dir, "train.py"),
+        instance_type="ml.m5.xlarge",
+        sagemaker_session=pipeline_session,
+        role=role,
+    )
+
+    train_steps = [
+        TrainingStep(
+            name=f"my-train-{count}",
+            display_name="TrainingStep",
+            description="description for Training step",
+            step_args=sklearn_train.fit(),
+        )
+        for count in range(2)
+    ]
+    pipeline = Pipeline(
+        name=pipeline_name,
+        steps=train_steps,
+        sagemaker_session=pipeline_session,
+    )
+
+    try:
+        pipeline.create(role, parallelism_config=dict(MaxParallelExecutionSteps=1))
+        # Override ParallelismConfiguration in pipeline.start to None
+        # so in backend, the default 100 will be applied
+        execution = pipeline.start(parallelism_config=dict(MaxParallelExecutionSteps=2))
+
+        def validate():
+            assert len(execution.list_steps()) == 2
+            for step in execution.list_steps():
+                assert step["StepStatus"] == "Executing"
+
+        retry_with_backoff(validate, num_attempts=4)
+
+        wait_pipeline_execution(execution=execution)
+
+    finally:
+        try:
+            pipeline.delete()
+        except Exception:
+            pass
+
+
 def test_model_registration_with_tuning_model(
     pipeline_session,
     role,

tests/unit/sagemaker/workflow/test_pipeline.py

@@ -126,10 +126,12 @@ def test_pipeline_create_with_parallelism_config(sagemaker_session_mock, role_ar
         name="MyPipeline",
         parameters=[],
         steps=[],
-        pipeline_experiment_config=ParallelismConfiguration(max_parallel_execution_steps=10),
         sagemaker_session=sagemaker_session_mock,
     )
-    pipeline.create(role_arn=role_arn)
+    pipeline.create(
+        role_arn=role_arn,
+        parallelism_config=ParallelismConfiguration(max_parallel_execution_steps=10),
+    )
     assert sagemaker_session_mock.sagemaker_client.create_pipeline.called_with(
         PipelineName="MyPipeline",
         PipelineDefinition=pipeline.definition(),
@@ -138,6 +140,42 @@ def test_pipeline_create_with_parallelism_config(sagemaker_session_mock, role_ar
     )
 
 
+def test_pipeline_create_and_start_with_parallelism_config(sagemaker_session_mock, role_arn):
+    pipeline = Pipeline(
+        name="MyPipeline",
+        parameters=[],
+        steps=[],
+        sagemaker_session=sagemaker_session_mock,
+    )
+    pipeline.create(
+        role_arn=role_arn,
+        parallelism_config=ParallelismConfiguration(max_parallel_execution_steps=10),
+    )
+    assert sagemaker_session_mock.sagemaker_client.create_pipeline.called_with(
+        PipelineName="MyPipeline",
+        PipelineDefinition=pipeline.definition(),
+        RoleArn=role_arn,
+        ParallelismConfiguration={"MaxParallelExecutionSteps": 10},
+    )
+
+    sagemaker_session_mock.sagemaker_client.start_pipeline_execution.return_value = dict(
+        PipelineExecutionArn="pipeline-execution-arn"
+    )
+
+    # No ParallelismConfiguration specified
+    pipeline.start()
+    assert sagemaker_session_mock.sagemaker_client.start_pipeline_execution.call_args[1] == {
+        "PipelineName": "MyPipeline"
+    }
+
+    # Specify ParallelismConfiguration to another value which will be honored in backend
+    pipeline.start(parallelism_config=ParallelismConfiguration(max_parallel_execution_steps=20))
+    assert sagemaker_session_mock.sagemaker_client.start_pipeline_execution.called_with(
+        PipelineName="MyPipeline",
+        ParallelismConfiguration={"MaxParallelExecutionSteps": 20},
+    )
+
+
 @patch("sagemaker.s3.S3Uploader.upload_string_as_file_body")
 def test_large_pipeline_create(sagemaker_session_mock, role_arn):
     sagemaker_session_mock.sagemaker_config = {}
@@ -200,10 +238,12 @@ def test_pipeline_update_with_parallelism_config(sagemaker_session_mock, role_ar
         name="MyPipeline",
         parameters=[],
         steps=[],
-        pipeline_experiment_config=ParallelismConfiguration(max_parallel_execution_steps=10),
         sagemaker_session=sagemaker_session_mock,
     )
-    pipeline.create(role_arn=role_arn)
+    pipeline.create(
+        role_arn=role_arn,
+        parallelism_config=ParallelismConfiguration(max_parallel_execution_steps=10),
+    )
     assert sagemaker_session_mock.sagemaker_client.update_pipeline.called_with(
         PipelineName="MyPipeline",
         PipelineDefinition=pipeline.definition(),