Python

To create a pipeline run withPrivate Service Connect interfaces usingthe Vertex AI SDK for Python, configure the run using theaiplatform_v1/services/pipeline_service definition.

# Import aiplatform and the appropriate API version v1fromgoogle.cloudimportaiplatform,aiplatform_v1# Initialize the Vertex SDK using PROJECT_ID and LOCATIONaiplatform.init(project="PROJECT_ID",location="LOCATION")# Create the API endpointclient_options={"api_endpoint":f"LOCATION-aiplatform.googleapis.com"}# Initialize the PipelineServiceClientclient=aiplatform_v1.PipelineServiceClient(client_options=client_options)PSCI_INTERFACE_CONFIG={"network_attachment":"NETWORK_ATTACHMENT_NAME","dns_peering_configs":[{"domain":"DNS_DOMAIN","target_project":"TARGET_PROJECT","target_network":"TARGET_NETWORK"}]}# Construct the requestrequest=aiplatform_v1.CreatePipelineJobRequest(parent=f"projects/PROJECT_ID/locations/LOCATION",pipeline_job=aiplatform_v1.PipelineJob(display_name="DISPLAY_NAME",pipeline_spec=PIPELINE_SPEC,runtime_config=aiplatform_v1.PipelineJob.RuntimeConfig(gcs_output_directory="OUTPUT_DIRECTORY",),psc_interface_config=aiplatform_v1.PscInterfaceConfig(PSCI_INTERFACE_CONFIG),)# Make the API callresponse=client.create_pipeline_job(request=request)# Print the responseprint(response)

Replace the following:

• PROJECT_ID: The project ID of the project where you want to create the pipeline run.
• LOCATION: The region where you want to create the pipeline run.
• DISPLAY_NAME: The name of the pipeline job. The maximum length for a display name is 128 UTF-8 characters.
• PIPELINE_SPEC: The pipeline spec you created inCreate a pipeline spec.
• OUTPUT_DIRECTORY: The URI of the Cloud Storage bucket for storing output artifacts. This path is the root output directory for the pipeline and is used to generate the paths of output artifacts.
• NETWORK_ATTACHMENT_NAME: The name of the Compute Engine network attachment to attach to thePipelineJob resource. To obtain the network attachment, you must have completed the steps in theBefore you begin section. For more information about the network attachment, seeSet up a VPC network, subnet, and network attachment.
• DNS_DOMAIN: The DNS name of the private cloud DNS zone you created when you set up private DNS peering.
• TARGET_PROJECT: The project hosting the VPC network.
• TARGET_NETWORK: The VPC network name.

REST

To create a pipeline run, send aPOST request by using thepipelineJobs.createmethod.

Before using any of the request data, make the following replacements:

• PROJECT_ID: The project ID of the project where you want to create the pipeline run.
• LOCATION: The region where you want to create the pipeline run.
• DISPLAY_NAME: The name of the pipeline job. The maximum length for a display name is 128 UTF-8 characters.
• PIPELINE_SPEC: The pipeline spec you created inCreate a pipeline spec.
• OUTPUT_DIRECTORY: The URI of the Cloud Storage bucket for storing output artifacts. This path is the root output directory for the pipeline and is used to generate the paths of output artifacts.
• NETWORK_ATTACHMENT_NAME: The name of the Compute Engine network attachment to attach to thePipelineJob resource. To obtain the network attachment, you must have completed the steps in theBefore you begin section. For more information about the network attachment, seeSet up a VPC network, subnet, and network attachment.
• DNS_DOMAIN: The DNS name of the private cloud DNS zone you created when you set up private DNS peering.
• TARGET_PROJECT: The project hosting the VPC network.
• TARGET_NETWORK: The VPC network name.

HTTP method and URL:

POST https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/pipelineJobs

Request JSON body:

{  "display_name": "DISPLAY_NAME",  "pipeline_spec": "PIPELINE_SPEC",  "runtime_config": {       "gcs_output_directory": "OUTPUT_DIRECTORY",   },   "psc_interface_config": {      "network_attachment": "NETWORK_ATTACHMENT_NAME",      "dns_peering_configs": [      {        "domain": "DNS_DOMAIN",        "target_project": "TARGET_PROJECT",        "target_network": "TARGET_NETWORK"      }    ]  }}

To send your request, choose one of these options:

curl -X POST \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     -H "Content-Type: application/json; charset=utf-8" \
     -d @request.json \
     "https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/pipelineJobs"

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
    -Method POST `
    -Headers $headers `
    -ContentType: "application/json; charset=utf-8" `
    -InFile request.json `
    -Uri "https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/pipelineJobs" | Select-Object -Expand Content

You should see output similar to the following.PIPELINE_JOB_ID represents the ID of the pipeline run andSERVICE_ACCOUNT_NAME represents the service account used to run the pipeline.

{  "name": "projects/PROJECT_ID/locations/LOCATION/pipelineJobs/PIPELINE_JOB_ID",  "displayName": "DISPLAY_NAME",  "createTime": "20xx-01-01T00:00:00.000000Z",  "updateTime": "20xx-01-01T00:00:00.000000Z",  "pipelineSpec":PIPELINE_SPEC,  "state": "PIPELINE_STATE_PENDING",  "labels": {    "vertex-ai-pipelines-run-billing-id": "VERTEX_AI_PIPELINES_RUN_BILLING_ID"  },  "runtimeConfig": {    "gcsOutputDirectory": "OUTPUT_DIRECTORY"  },  "serviceAccount": "SERVICE_ACCOUNT_NAME"  "pscInterfaceConfig": {    "networkAttachment": "NETWORK_ATTACHMENT_NAME",    "dnsPeeringConfigs": [      {        "domain": "DNS_DOMAIN",        "targetProject": "TARGET_PROJECT",        "targetNetwork": "TARGET_NETWORK"      }    ]  }}