Configure a pipeline run on a persistent resource

A Vertex AI persistent resource is a long-running cluster that you can use to run custom training jobs and pipeline runs. By using a persistent resource for a pipeline run, you can help ensure compute resource availability and reduce pipeline task startup time. Persistent resources support all VMs and GPUs that are supported by custom training jobs. Learn more about persistent resources.

This page shows you how to do the following:

Create a persistent resource
Create a pipeline run using the persistent resource

Before you begin

Before you can create a pipeline run with a persistent resource, you must first complete the following prerequisites.

Define and compile a pipeline

Define your pipeline and then compile the pipeline definition into a YAML file. For more information about defining and compiling a pipeline, see Build a pipeline.

Required IAM roles

To get the permission that you need to create a persistent resource, ask your administrator to grant you the Vertex AI Administrator (roles/aiplatform.admin) IAM role on your project. For more information about granting roles, see Manage access to projects, folders, and organizations.

This predefined role contains the aiplatform.persistentResources.create permission, which is required to create a persistent resource.

You might also be able to get this permission with custom roles or other predefined roles.

Create a persistent resource

Use the following samples to create a persistent resource that you can associate with a pipeline run. For more information about creating persistent resources, see Create a persistent resource.

gcloud

To create a persistent resource that you can associate with a pipeline run, use the gcloud ai persistent-resources create command along with the --enable-custom-service-account flag.

A persistent resource can have one or more resource pools. To create multiple resource pools in a persistent resource, specify multiple --resource-pool-spec flags.

You can specify all resource pool configurations as part of the command-line or use the --config flag to specify the path to a YAML file that contains the configurations.

Before using any of the command data below, make the following replacements:

PROJECT_ID: The project ID of the Google Cloud project where you want to create the persistent resource.
LOCATION: The region where you want to create the persistent resource. For a list of supported regions, see Feature availability.
PERSISTENT_RESOURCE_ID: The ID of the persistent resource.
DISPLAY_NAME: Optional. The display name of the persistent resource.
MACHINE_TYPE: The type of virtual machine (VM) to use. For a list of supported VMs, see Machine types. This field corresponds to the machineSpec.machineType field in the ResourcePool API message.
REPLICA_COUNT: Optional. The number of replicas to create for the resource pool, if you don't want to use autoscaling. This field corresponds to the replicaCount field in the ResourcePool API message. You must specify the replica count if you don't specify the MIN_REPLICA_COUNT and MAX_REPLICA_COUNT fields.
MIN_REPLICA_COUNT: Optional. The minimum number of replicas if you're using autoscaling for the resource pool. You must specify both MIN_REPLICA_COUNT and MAX_REPLICA_COUNT to use autoscaling.
MAX_REPLICA_COUNT: Optional. The maximum number of replicas if you're using autoscaling for the resource pool. You must specify both MIN_REPLICA_COUNT and MAX_REPLICA_COUNT to use autoscaling.
CONFIG: Path to the persistent resource YAML configuration file, containing a list of ResourcePool specs. If an option is specified in both the configuration file and the command-line arguments, the command-line arguments override the configuration file. Note that keys with underscores are considered invalid.
Example YAML configuration file:
```
resourcePoolSpecs:
  machineSpec:
    machineType: n1-standard-4
  replicaCount: 1
    
```

Execute the following command:

Linux, macOS, or Cloud Shell

gcloud ai persistent-resources create \
    --persistent-resource-id=PERSISTENT_RESOURCE_ID \
    --display-name=DISPLAY_NAME \
    --project=PROJECT_ID \
    --region=LOCATION \
    --resource-pool-spec="replica-count=REPLICA_COUNT,machine-type=MACHINE_TYPE,min-replica-count=MIN_REPLICA_COUNT,max-replica-count=MAX_REPLICA_COUNT" \
    --enable-custom-service-account

Windows (PowerShell)

gcloud ai persistent-resources create `
    --persistent-resource-id=PERSISTENT_RESOURCE_ID `
    --display-name=DISPLAY_NAME `
    --project=PROJECT_ID `
    --region=LOCATION `
    --resource-pool-spec="replica-count=REPLICA_COUNT,machine-type=MACHINE_TYPE,min-replica-count=MIN_REPLICA_COUNT,max-replica-count=MAX_REPLICA_COUNT" `
    --enable-custom-service-account

Windows (cmd.exe)

gcloud ai persistent-resources create ^
    --persistent-resource-id=PERSISTENT_RESOURCE_ID ^
    --display-name=DISPLAY_NAME ^
    --project=PROJECT_ID ^
    --region=LOCATION ^
    --resource-pool-spec="replica-count=REPLICA_COUNT,machine-type=MACHINE_TYPE,min-replica-count=MIN_REPLICA_COUNT,max-replica-count=MAX_REPLICA_COUNT" ^
    --enable-custom-service-account

You should receive a response similar to the following:

Using endpoint [https://us-central1-aiplatform.googleapis.com/]
Operation to create PersistentResource [projects/PROJECT_NUMBER/locations/us-central1/persistentResources/mypersistentresource/operations/OPERATION_ID] is submitted successfully.

You can view the status of your PersistentResource create operation with the command

  $ gcloud ai operations describe projects/sample-project/locations/us-central1/operations/OPERATION_ID

Example gcloud command:

gcloud ai persistent-resources create \
    --persistent-resource-id=my-persistent-resource \
    --region=us-central1 \
    --resource-pool-spec="replica-count=4,machine-type=n1-standard-4"
    --enable-custom-service-account

Advanced `gcloud` configurations

If you want to specify configuration options that are not available in the preceding examples, you can use the --config flag to specify the path to a config.yaml file in your local environment that contains the fields of persistentResources. For example:

gcloud ai persistent-resources create \
    --persistent-resource-id=PERSISTENT_RESOURCE_ID \
    --project=PROJECT_ID \
    --region=LOCATION \
    --config=CONFIG
    --enable-custom-service-account

Python

Before trying this sample, follow the Python setup instructions in the Vertex AI quickstart using client libraries. For more information, see the Vertex AI Python API reference documentation.

To authenticate to Vertex AI, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.

To create a persistent resource that you can use with a pipeline run, set the enable_custom_service_account parameter to True in the ResourceRuntimeSpec object while creating the persistent resource.

my_example_resource = persistent_resource.PersistentResource.create(
    persistent_resource_id=PERSISTENT_RESOURCE_ID,
    display_name=DISPLAY_NAME,
    resource_pools=[
        resource_pool.ResourcePool(
            machine_spec=machine_spec.MachineSpec(
                machine_type=MACHINE_TYPE,
            ),
            replica_count=REPLICA_COUNT,
        )
    ],
    resource_runtime_spec=resource_runtime_spec.ResourceRuntimeSpec(
        enable_custom_service_account=True,
    ),
)

Replace the following:

PERSISTENT_RESOURCE_ID: The ID of the persistent resource.
DISPLAY_NAME: Optional. The display name of the persistent resource.
MACHINE_TYPE: The type of virtual machine (VM) to use. For a list of supported VMs, see Machine types. This field corresponds to the machineSpec.machineType field in the ResourcePool API message.
REPLICA_COUNT: The number of replicas to create when creating this resource pool.

REST

To create a PersistentResource resource that you can associate with a pipeline run, send a POST request by using the persistentResources/create method with the enable_custom_service_account parameter set to true in the request body.

A persistent resource can have one or more resource pools. You can configure each resource pool to use either a fixed number of replicas or autoscaling.

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

PROJECT_ID: The project ID of the Google Cloud project where you want to create the persistent resource.
LOCATION: The region where you want to create the persistent resource. For a list of supported regions, see Feature availability.
PERSISTENT_RESOURCE_ID: The ID of the persistent resource.
DISPLAY_NAME: Optional. The display name of the persistent resource.
MACHINE_TYPE: The type of virtual machine (VM) to use. For a list of supported VMs, see Machine types. This field corresponds to the machineSpec.machineType field in the ResourcePool API message.
REPLICA_COUNT: Optional. The number of replicas to create for the resource pool, if you don't want to use autoscaling. This field corresponds to the replicaCount field in the ResourcePool API message. You must specify the replica count if you don't specify the MIN_REPLICA_COUNT and MAX_REPLICA_COUNT fields.
MIN_REPLICA_COUNT: Optional. The minimum number of replicas if you're using autoscaling for the resource pool. You must specify both MIN_REPLICA_COUNT and MAX_REPLICA_COUNT to use autoscaling.
MAX_REPLICA_COUNT: Optional. The maximum number of replicas if you're using autoscaling for the resource pool. You must specify both MIN_REPLICA_COUNT and MAX_REPLICA_COUNT to use autoscaling.

HTTP method and URL:

POST https://us-central1-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/persistentResources?persistent_resource_id=PERSISTENT_RESOURCE_ID

Request JSON body:

{
  "display_name": "DISPLAY_NAME",
  "resource_pools": [
    {
      "machine_spec": {
        "machine_type": "MACHINE_TYPE"
      },
      "replica_count": REPLICA_COUNT,
      "autoscaling_spec": {
        "min_replica_count": MIN_REPLICA_COUNT,
        "max_replica_count": MAX_REPLICA_COUNT
      }
    }
  ],
  "resource_runtime_spec": {
    "enable_custom_service_account: true
  }
}

To send your request, expand one of these options:

curl (Linux, macOS, or Cloud Shell)

Note: The following command assumes that you have logged in to the gcloud CLI with your user account by running gcloud init or gcloud auth login , or by using Cloud Shell, which automatically logs you into the gcloud CLI . You can check the currently active account by running gcloud auth list.

Save the request body in a file named request.json. Run the following command in the terminal to create or overwrite this file in the current directory:

cat > request.json << 'EOF'
{
  "display_name": "DISPLAY_NAME",
  "resource_pools": [
    {
      "machine_spec": {
        "machine_type": "MACHINE_TYPE"
      },
      "replica_count": REPLICA_COUNT,
      "autoscaling_spec": {
        "min_replica_count": MIN_REPLICA_COUNT,
        "max_replica_count": MAX_REPLICA_COUNT
      }
    }
  ],
  "resource_runtime_spec": {
    "enable_custom_service_account: true
  }
}
EOF

Then execute the following command to send your REST request:

curl -X POST \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     -H "Content-Type: application/json; charset=utf-8" \
     -d @request.json \
     "https://us-central1-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/persistentResources?persistent_resource_id=PERSISTENT_RESOURCE_ID"

PowerShell (Windows)

Note: The following command assumes that you have logged in to the gcloud CLI with your user account by running gcloud init or gcloud auth login . You can check the currently active account by running gcloud auth list.

Save the request body in a file named request.json. Run the following command in the terminal to create or overwrite this file in the current directory:

@'
{
  "display_name": "DISPLAY_NAME",
  "resource_pools": [
    {
      "machine_spec": {
        "machine_type": "MACHINE_TYPE"
      },
      "replica_count": REPLICA_COUNT,
      "autoscaling_spec": {
        "min_replica_count": MIN_REPLICA_COUNT,
        "max_replica_count": MAX_REPLICA_COUNT
      }
    }
  ],
  "resource_runtime_spec": {
    "enable_custom_service_account: true
  }
}
'@  | Out-File -FilePath request.json -Encoding utf8

Then execute the following command to send your REST request:

$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://us-central1-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/persistentResources?persistent_resource_id=PERSISTENT_RESOURCE_ID" | Select-Object -Expand Content

You should receive a JSON response similar to the following:

{
  "name": "projects/PROJECT_NUMBER/locations/LOCATION/persistentResources/mypersistentresource/operations/OPERATION_ID",
  "metadata": {
    "@type": "type.googleapis.com/google.cloud.aiplatform.v1.CreatePersistentResourceOperationMetadata",
    "genericMetadata": {
      "createTime": "2023-02-08T21:17:15.009668Z",
      "updateTime": "2023-02-08T21:17:15.009668Z"
    }
  }
}

Create a pipeline run using the persistent resource

Use the following code sample to create a pipeline run that uses the persistent resource:

  job = aiplatform.PipelineJob(display_name = 'DISPLAY_NAME',
  template_path = 'COMPILED_PIPELINE_PATH',
  pipeline_root = 'PIPELINE_ROOT',
  project = 'PROJECT_ID',
  location = 'LOCATION',
  default_runtime = {
    "persistentResourceRuntimeDetail": {
    "persistentResourceName": "PERSISTENT_RESOURCE_ID",
    "taskResourceUnavailableWaitTimeMs": WAIT_TIME,
    "taskResourceUnavailableTimeoutBehavior": TIMEOUT_BEHAVIOR,
    }
  }

Replace the following:

DISPLAY_NAME: The name of the pipeline. This appears in the Google Cloud console.
COMPILED_PIPELINE_PATH: The path to your compiled pipeline YAML file. It can be a local path or a Cloud Storage URI.
PIPELINE_ROOT: Specify a Cloud Storage URI to store the artifacts of your pipeline run.
PROJECT_ID: The Google Cloud project that this pipeline runs in.
LOCATION: The region where the pipeline run is executed. For more information about the regions where Vertex AI Pipelines is available, see the Vertex AI locations guide. If you don't set this parameter, Vertex AI Pipelines uses the default location set in aiplatform.init.
PERSISTENT_RESOURCE_ID: The ID of the persistent resource that you created.
WAIT_TIME: The time in milliseconds to wait if the persistent resource is unavailable.
TIMEOUT_BEHAVIOR: Specify the fall back behavior of the pipeline task in case the WAIT_TIME is exceeded. Possible values include the following:
- FAIL The pipeline task fails after exceeding the wait time.
- FALL_BACK_TO_ON_DEMAND The pipeline task continues to run using the default Vertex AI training resources, without using the persistent resource.

What's next

Learn how to run a pipeline.

Configure a pipeline run on a persistent resource

Before you begin

Define and compile a pipeline

Required IAM roles

Create a persistent resource

gcloud

Linux, macOS, or Cloud Shell

Windows (PowerShell)

Windows (cmd.exe)

Advanced gcloud configurations

Python

REST

curl (Linux, macOS, or Cloud Shell)

PowerShell (Windows)

Create a pipeline run using the persistent resource

What's next

Advanced `gcloud` configurations