feat: add support for failure_policy in PipelineJob

google/cloud/aiplatform/compat/__init__.py

@@ -96,6 +96,7 @@
     types.model_evaluation_slice = types.model_evaluation_slice_v1beta1
     types.model_service = types.model_service_v1beta1
     types.operation = types.operation_v1beta1
+    types.pipeline_failure_policy = types.pipeline_failure_policy_v1beta1
     types.pipeline_job = types.pipeline_job_v1beta1
     types.pipeline_service = types.pipeline_service_v1beta1
     types.pipeline_state = types.pipeline_state_v1beta1
@@ -180,6 +181,7 @@
     types.model_evaluation_slice = types.model_evaluation_slice_v1
     types.model_service = types.model_service_v1
     types.operation = types.operation_v1
+    types.pipeline_failure_policy = types.pipeline_failure_policy_v1
     types.pipeline_job = types.pipeline_job_v1
     types.pipeline_service = types.pipeline_service_v1
     types.pipeline_state = types.pipeline_state_v1

google/cloud/aiplatform/compat/types/__init__.py

@@ -63,6 +63,7 @@
     model_evaluation_slice as model_evaluation_slice_v1beta1,
     model_service as model_service_v1beta1,
     operation as operation_v1beta1,
+    pipeline_failure_policy as pipeline_failure_policy_v1beta1,
     pipeline_job as pipeline_job_v1beta1,
     pipeline_service as pipeline_service_v1beta1,
     pipeline_state as pipeline_state_v1beta1,
@@ -126,6 +127,7 @@
     model_evaluation_slice as model_evaluation_slice_v1,
     model_service as model_service_v1,
     operation as operation_v1,
+    pipeline_failure_policy as pipeline_failure_policy_v1,
     pipeline_job as pipeline_job_v1,
     pipeline_service as pipeline_service_v1,
     pipeline_state as pipeline_state_v1,
@@ -191,6 +193,7 @@
     model_evaluation_slice_v1,
     model_service_v1,
     operation_v1,
+    pipeline_failure_policy_v1beta1,
     pipeline_job_v1,
     pipeline_service_v1,
     pipeline_state_v1,
@@ -253,6 +256,7 @@
     model_evaluation_slice_v1beta1,
     model_service_v1beta1,
     operation_v1beta1,
+    pipeline_failure_policy_v1beta1,
     pipeline_job_v1beta1,
     pipeline_service_v1beta1,
     pipeline_state_v1beta1,

google/cloud/aiplatform/pipeline_jobs.py

@@ -119,6 +119,7 @@ def __init__(
         credentials: Optional[auth_credentials.Credentials] = None,
         project: Optional[str] = None,
         location: Optional[str] = None,
+        failure_policy: Optional[str] = None,
     ):
         """Retrieves a PipelineJob resource and instantiates its
         representation.
@@ -173,6 +174,15 @@ def __init__(
             location (str):
                 Optional. Location to create PipelineJob. If not set,
                 location set in aiplatform.init will be used.
+            failure_policy (str):
+                Optional. The failure policy - "slow" or "fast".
+                Currently, the default of a pipeline is that the pipeline will continue to
+                run until no more tasks can be executed, also known as
+                PIPELINE_FAILURE_POLICY_FAIL_SLOW (corresponds to "slow").
+                However, if a pipeline is set to
+                PIPELINE_FAILURE_POLICY_FAIL_FAST (corresponds to "fast"),
+                it will stop scheduling any new tasks when a task has failed. Any
+                scheduled tasks will continue to completion.
 
         Raises:
             ValueError: If job_id or labels have incorrect format.
@@ -219,6 +229,7 @@ def __init__(
         )
         builder.update_pipeline_root(pipeline_root)
         builder.update_runtime_parameters(parameter_values)
+        builder.update_failure_policy(failure_policy)
         runtime_config_dict = builder.build()
 
         runtime_config = gca_pipeline_job.PipelineJob.RuntimeConfig()._pb

google/cloud/aiplatform/utils/pipeline_utils.py

@@ -17,6 +17,7 @@
 import copy
 import json
 from typing import Any, Dict, Mapping, Optional, Union
+from google.cloud.aiplatform.compat.types import pipeline_failure_policy
 import packaging.version
 
 
@@ -32,6 +33,7 @@ def __init__(
         schema_version: str,
         parameter_types: Mapping[str, str],
         parameter_values: Optional[Dict[str, Any]] = None,
+        failure_policy: Optional[pipeline_failure_policy.PipelineFailurePolicy] = None,
     ):
         """Creates a PipelineRuntimeConfigBuilder object.
 
@@ -44,11 +46,20 @@ def __init__(
               Required. The mapping from pipeline parameter name to its type.
           parameter_values (Dict[str, Any]):
               Optional. The mapping from runtime parameter name to its value.
+          failure_policy (pipeline_failure_policy.PipelineFailurePolicy):
+              Optional. Represents the failure policy of a pipeline. Currently, the
+              default of a pipeline is that the pipeline will continue to
+              run until no more tasks can be executed, also known as
+              PIPELINE_FAILURE_POLICY_FAIL_SLOW. However, if a pipeline is
+              set to PIPELINE_FAILURE_POLICY_FAIL_FAST, it will stop
+              scheduling any new tasks when a task has failed. Any
+              scheduled tasks will continue to completion.
         """
         self._pipeline_root = pipeline_root
         self._schema_version = schema_version
         self._parameter_types = parameter_types
         self._parameter_values = copy.deepcopy(parameter_values or {})
+        self._failure_policy = failure_policy
 
     @classmethod
     def from_job_spec_json(
@@ -80,7 +91,14 @@ def from_job_spec_json(
 
         pipeline_root = runtime_config_spec.get("gcsOutputDirectory")
         parameter_values = _parse_runtime_parameters(runtime_config_spec)
-        return cls(pipeline_root, schema_version, parameter_types, parameter_values)
+        failure_policy = runtime_config_spec.get("failurePolicy")
+        return cls(
+            pipeline_root,
+            schema_version,
+            parameter_types,
+            parameter_values,
+            failure_policy,
+        )
 
     def update_pipeline_root(self, pipeline_root: Optional[str]) -> None:
         """Updates pipeline_root value.
@@ -111,6 +129,16 @@ def update_runtime_parameters(
                         parameters[k] = json.dumps(v)
             self._parameter_values.update(parameters)
 
+    def update_failure_policy(self, failure_policy: Optional[str] = None) -> None:
+        """Merges runtime failure policy.
+
+        Args:
+          failure_policy (str):
+              Optional. The failure policy - "slow" or "fast".
+        """
+        if failure_policy:
+            self._failure_policy = _FAILURE_POLICY_TO_ENUM_VALUE[failure_policy]
+
     def build(self) -> Dict[str, Any]:
         """Build a RuntimeConfig proto.
 
@@ -128,7 +156,8 @@ def build(self) -> Dict[str, Any]:
             parameter_values_key = "parameterValues"
         else:
             parameter_values_key = "parameters"
-        return {
+
+        runtime_config = {
             "gcsOutputDirectory": self._pipeline_root,
             parameter_values_key: {
                 k: self._get_vertex_value(k, v)
@@ -137,6 +166,11 @@ def build(self) -> Dict[str, Any]:
             },
         }
 
+        if self._failure_policy:
+            runtime_config["failurePolicy"] = self._failure_policy
+
+        return runtime_config
+
     def _get_vertex_value(
         self, name: str, value: Union[int, float, str, bool, list, dict]
     ) -> Union[int, float, str, bool, list, dict]:
@@ -205,3 +239,10 @@ def _parse_runtime_parameters(
             else:
                 raise TypeError("Got unknown type of value: {}".format(value))
         return result
+
+
+_FAILURE_POLICY_TO_ENUM_VALUE = {
+    "slow": pipeline_failure_policy.PipelineFailurePolicy.PIPELINE_FAILURE_POLICY_FAIL_SLOW,
+    "fast": pipeline_failure_policy.PipelineFailurePolicy.PIPELINE_FAILURE_POLICY_FAIL_FAST,
+    None: pipeline_failure_policy.PipelineFailurePolicy.PIPELINE_FAILURE_POLICY_UNSPECIFIED,
+}

tests/unit/aiplatform/test_pipeline_jobs.py

@@ -30,6 +30,7 @@
 from google.cloud.aiplatform import base
 from google.cloud.aiplatform import initializer
 from google.cloud.aiplatform import pipeline_jobs
+from google.cloud.aiplatform.compat.types import pipeline_failure_policy
 from google.cloud import storage
 from google.protobuf import json_format
 
@@ -621,6 +622,99 @@ def test_run_call_pipeline_service_create_with_timeout_not_explicitly_set(
             timeout=None,
         )
 
+    @pytest.mark.parametrize(
+        "job_spec",
+        [_TEST_PIPELINE_SPEC_JSON, _TEST_PIPELINE_SPEC_YAML, _TEST_PIPELINE_JOB],
+    )
+    @pytest.mark.parametrize(
+        "failure_policy",
+        [
+            (
+                "slow",
+                pipeline_failure_policy.PipelineFailurePolicy.PIPELINE_FAILURE_POLICY_FAIL_SLOW,
+            ),
+            (
+                "fast",
+                pipeline_failure_policy.PipelineFailurePolicy.PIPELINE_FAILURE_POLICY_FAIL_FAST,
+            ),
+        ],
+    )
+    @pytest.mark.parametrize("sync", [True, False])
+    def test_run_call_pipeline_service_create_with_failure_policy(
+        self,
+        mock_pipeline_service_create,
+        mock_pipeline_service_get,
+        job_spec,
+        mock_load_yaml_and_json,
+        failure_policy,
+        sync,
+    ):
+        aiplatform.init(
+            project=_TEST_PROJECT,
+            staging_bucket=_TEST_GCS_BUCKET_NAME,
+            location=_TEST_LOCATION,
+            credentials=_TEST_CREDENTIALS,
+        )
+
+        job = pipeline_jobs.PipelineJob(
+            display_name=_TEST_PIPELINE_JOB_DISPLAY_NAME,
+            template_path=_TEST_TEMPLATE_PATH,
+            job_id=_TEST_PIPELINE_JOB_ID,
+            parameter_values=_TEST_PIPELINE_PARAMETER_VALUES,
+            enable_caching=True,
+            failure_policy=failure_policy[0],
+        )
+
+        job.run(
+            service_account=_TEST_SERVICE_ACCOUNT,
+            network=_TEST_NETWORK,
+            sync=sync,
+            create_request_timeout=None,
+        )
+
+        if not sync:
+            job.wait()
+
+        expected_runtime_config_dict = {
+            "gcsOutputDirectory": _TEST_GCS_BUCKET_NAME,
+            "parameterValues": _TEST_PIPELINE_PARAMETER_VALUES,
+            "failurePolicy": failure_policy[1],
+        }
+        runtime_config = gca_pipeline_job.PipelineJob.RuntimeConfig()._pb
+        json_format.ParseDict(expected_runtime_config_dict, runtime_config)
+
+        job_spec = yaml.safe_load(job_spec)
+        pipeline_spec = job_spec.get("pipelineSpec") or job_spec
+
+        # Construct expected request
+        expected_gapic_pipeline_job = gca_pipeline_job.PipelineJob(
+            display_name=_TEST_PIPELINE_JOB_DISPLAY_NAME,
+            pipeline_spec={
+                "components": {},
+                "pipelineInfo": pipeline_spec["pipelineInfo"],
+                "root": pipeline_spec["root"],
+                "schemaVersion": "2.1.0",
+            },
+            runtime_config=runtime_config,
+            service_account=_TEST_SERVICE_ACCOUNT,
+            network=_TEST_NETWORK,
+        )
+
+        mock_pipeline_service_create.assert_called_once_with(
+            parent=_TEST_PARENT,
+            pipeline_job=expected_gapic_pipeline_job,
+            pipeline_job_id=_TEST_PIPELINE_JOB_ID,
+            timeout=None,
+        )
+
+        mock_pipeline_service_get.assert_called_with(
+            name=_TEST_PIPELINE_JOB_NAME, retry=base._DEFAULT_RETRY
+        )
+
+        assert job._gca_resource == make_pipeline_job(
+            gca_pipeline_state.PipelineState.PIPELINE_STATE_SUCCEEDED
+        )
+
     @pytest.mark.parametrize(
         "job_spec",
         [

tests/unit/aiplatform/test_utils.py

@@ -28,6 +28,7 @@
 from google.api_core import client_options, gapic_v1
 from google.cloud import aiplatform
 from google.cloud.aiplatform import compat, utils
+from google.cloud.aiplatform.compat.types import pipeline_failure_policy
 from google.cloud.aiplatform.utils import pipeline_utils, tensorboard_utils, yaml_utils
 from google.cloud.aiplatform_v1.services.model_service import (
     client as model_service_client_v1,
@@ -454,7 +455,22 @@ def test_pipeline_utils_runtime_config_builder_with_no_op_updates(self):
         expected_runtime_config = self.SAMPLE_JOB_SPEC["runtimeConfig"]
         assert expected_runtime_config == actual_runtime_config
 
-    def test_pipeline_utils_runtime_config_builder_with_merge_updates(self):
+    @pytest.mark.parametrize(
+        "failure_policy",
+        [
+            (
+                "slow",
+                pipeline_failure_policy.PipelineFailurePolicy.PIPELINE_FAILURE_POLICY_FAIL_SLOW,
+            ),
+            (
+                "fast",
+                pipeline_failure_policy.PipelineFailurePolicy.PIPELINE_FAILURE_POLICY_FAIL_FAST,
+            ),
+        ],
+    )
+    def test_pipeline_utils_runtime_config_builder_with_merge_updates(
+        self, failure_policy
+    ):
         my_builder = pipeline_utils.PipelineRuntimeConfigBuilder.from_job_spec_json(
             self.SAMPLE_JOB_SPEC
         )
@@ -468,6 +484,7 @@ def test_pipeline_utils_runtime_config_builder_with_merge_updates(self):
                 "bool_param": True,
             }
         )
+        my_builder.update_failure_policy(failure_policy[0])
         actual_runtime_config = my_builder.build()
 
         expected_runtime_config = {
@@ -481,6 +498,7 @@ def test_pipeline_utils_runtime_config_builder_with_merge_updates(self):
                 "list_param": {"stringValue": "[1, 2, 3]"},
                 "bool_param": {"stringValue": "true"},
             },
+            "failurePolicy": failure_policy[1],
         }
         assert expected_runtime_config == actual_runtime_config