feat: Support "reservedIpRanges" parameter in PipelineJob run() and submit() methods.

PiperOrigin-RevId: 592024354

Author: vertex-sdk-bot

google/cloud/aiplatform/pipeline_job_schedules.py

@@ -110,6 +110,10 @@ def __init__(
             create_pipeline_job_request["pipeline_job"][
                 "encryption_spec"
             ] = pipeline_job._gca_resource.encryption_spec
+        if "reserved_ip_ranges" in pipeline_job._gca_resource:
+            create_pipeline_job_request["pipeline_job"][
+                "reserved_ip_ranges"
+            ] = pipeline_job._gca_resource.reserved_ip_ranges
         pipeline_job_schedule_args = {
             "display_name": display_name,
             "create_pipeline_job_request": create_pipeline_job_request,

google/cloud/aiplatform/pipeline_jobs.py

@@ -294,6 +294,7 @@ def run(
         self,
         service_account: Optional[str] = None,
         network: Optional[str] = None,
+        reserved_ip_ranges: Optional[List[str]] = None,
         sync: Optional[bool] = True,
         create_request_timeout: Optional[float] = None,
     ) -> None:
@@ -309,6 +310,9 @@ def run(
                 Private services access must already be configured for the network.
                 If left unspecified, the network set in aiplatform.init will be used.
                 Otherwise, the job is not peered with any network.
+            reserved_ip_ranges (List[str]):
+                Optional. A list of names for the reserved IP ranges under the VPC network that can be used for this PipelineJob's workload.
+                For example: ['vertex-ai-ip-range'].
             sync (bool):
                 Optional. Whether to execute this method synchronously. If False, this method will unblock and it will be executed in a concurrent Future.
             create_request_timeout (float):
@@ -319,6 +323,7 @@ def run(
         self._run(
             service_account=service_account,
             network=network,
+            reserved_ip_ranges=reserved_ip_ranges,
             sync=sync,
             create_request_timeout=create_request_timeout,
         )
@@ -328,6 +333,7 @@ def _run(
         self,
         service_account: Optional[str] = None,
         network: Optional[str] = None,
+        reserved_ip_ranges: Optional[List[str]] = None,
         sync: Optional[bool] = True,
         create_request_timeout: Optional[float] = None,
     ) -> None:
@@ -342,6 +348,9 @@ def _run(
                 Optional. The full name of the Compute Engine network to which the job
                 should be peered. For example, projects/12345/global/networks/myVPC.
                 Private services access must already be configured for the network.
+            reserved_ip_ranges (List[str]):
+                Optional. A list of names for the reserved IP ranges under the VPC network that can be used for this PipelineJob's workload.
+                For example: ['vertex-ai-ip-range'].
             sync (bool):
                 Optional. Whether to execute this method synchronously. If False, this method will unblock and it will be executed in a concurrent Future.
             create_request_timeout (float):
@@ -350,6 +359,7 @@ def _run(
         self.submit(
             service_account=service_account,
             network=network,
+            reserved_ip_ranges=reserved_ip_ranges,
             create_request_timeout=create_request_timeout,
         )
 
@@ -359,6 +369,7 @@ def submit(
         self,
         service_account: Optional[str] = None,
         network: Optional[str] = None,
+        reserved_ip_ranges: Optional[List[str]] = None,
         create_request_timeout: Optional[float] = None,
         *,
         experiment: Optional[Union[str, experiment_resources.Experiment]] = None,
@@ -376,6 +387,12 @@ def submit(
                 Private services access must already be configured for the network.
                 If left unspecified, the network set in aiplatform.init will be used.
                 Otherwise, the job is not peered with any network.
+            reserved_ip_ranges (List[str]):
+                Optional. A list of names for the reserved IP ranges under the VPC
+                network that can be used for this PipelineJob's workload. For example: ['vertex-ai-ip-range'].
+
+                If left unspecified, the job will be deployed to any IP ranges under
+                the provided VPC network.
             create_request_timeout (float):
                 Optional. The timeout for the create request in seconds.
             experiment (Union[str, experiments_resource.Experiment]):
@@ -396,6 +413,9 @@ def submit(
         if network:
             self._gca_resource.network = network
 
+        if reserved_ip_ranges:
+            self._gca_resource.reserved_ip_ranges = reserved_ip_ranges
+
         try:
             output_artifacts_gcs_dir = (
                 self._gca_resource.runtime_config.gcs_output_directory

tests/unit/aiplatform/test_pipeline_jobs.py

@@ -63,6 +63,7 @@
 _TEST_HTTPS_TEMPLATE_PATH = "https://raw.githubusercontent.com/repo/pipeline.json"
 _TEST_PARENT = f"projects/{_TEST_PROJECT}/locations/{_TEST_LOCATION}"
 _TEST_NETWORK = f"projects/{_TEST_PROJECT}/global/networks/{_TEST_PIPELINE_JOB_ID}"
+_TEST_RESERVED_IP_RANGES = ["vertex-ai-ip-range"]
 
 _TEST_PIPELINE_JOB_NAME = f"projects/{_TEST_PROJECT}/locations/{_TEST_LOCATION}/pipelineJobs/{_TEST_PIPELINE_JOB_ID}"
 _TEST_PIPELINE_JOB_LIST_READ_MASK = field_mask.FieldMask(
@@ -231,6 +232,7 @@ def mock_pipeline_service_create():
             create_time=_TEST_PIPELINE_CREATE_TIME,
             service_account=_TEST_SERVICE_ACCOUNT,
             network=_TEST_NETWORK,
+            reserved_ip_ranges=_TEST_RESERVED_IP_RANGES,
         )
         yield mock_create_pipeline_job
 
@@ -267,6 +269,7 @@ def make_pipeline_job(state):
         create_time=_TEST_PIPELINE_CREATE_TIME,
         service_account=_TEST_SERVICE_ACCOUNT,
         network=_TEST_NETWORK,
+        reserved_ip_ranges=_TEST_RESERVED_IP_RANGES,
         job_detail=gca_pipeline_job.PipelineJobDetail(
             pipeline_run_context=gca_context.Context(
                 name=_TEST_PIPELINE_JOB_NAME,
@@ -548,6 +551,90 @@ def test_run_call_pipeline_service_create(
             gca_pipeline_state.PipelineState.PIPELINE_STATE_SUCCEEDED
         )
 
+    @pytest.mark.parametrize(
+        "job_spec",
+        [_TEST_PIPELINE_SPEC_JSON, _TEST_PIPELINE_SPEC_YAML, _TEST_PIPELINE_JOB],
+    )
+    @pytest.mark.parametrize("sync", [True, False])
+    def test_run_call_pipeline_service_run_with_reserved_ip_ranges(
+        self,
+        mock_pipeline_service_create,
+        mock_pipeline_service_get,
+        mock_pipeline_bucket_exists,
+        job_spec,
+        mock_load_yaml_and_json,
+        sync,
+    ):
+        import yaml
+
+        aiplatform.init(
+            project=_TEST_PROJECT,
+            staging_bucket=_TEST_GCS_BUCKET_NAME,
+            location=_TEST_LOCATION,
+            credentials=_TEST_CREDENTIALS,
+            service_account=_TEST_SERVICE_ACCOUNT,
+            network=_TEST_NETWORK,
+        )
+
+        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,
+            input_artifacts=_TEST_PIPELINE_INPUT_ARTIFACTS,
+            enable_caching=True,
+        )
+
+        job.run(
+            reserved_ip_ranges=_TEST_RESERVED_IP_RANGES,
+            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,
+            "inputArtifacts": {"vertex_model": {"artifactId": "456"}},
+        }
+        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,
+            reserved_ip_ranges=_TEST_RESERVED_IP_RANGES,
+        )
+
+        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",
         [_TEST_PIPELINE_SPEC_JSON, _TEST_PIPELINE_SPEC_YAML, _TEST_PIPELINE_JOB],