Create, upload, and use a pipeline template

A pipeline template is a resource that you can use to publish a workflow definition so that it can be reused multiple times, by a single user or by multiple users.

The Kubeflow Pipelines SDK registry client is a new client interface that you can use with a compatible registry server, such as Artifact Registry, for version control of your Kubeflow Pipelines (KFP) templates. For more information, see Use the template in a Kubeflow Pipelines SDK registry client.

This page shows you how to:

  • Create a KFP pipeline template
  • Use the Kubeflow Pipelines SDK registry client to upload the template to a pipeline template repository
  • Use the template in the Kubeflow Pipelines client

Before you begin

Before you build and run your pipeline, use the following instructions to set up your Google Cloud project and development environment in the Google Cloud console.

  1. Install v2 or later of the Kubeflow Pipelines SDK.
    (Optional) Before installing, run the following command to see which version of the Kubeflow Pipelines SDK is currently installed:

      pip freeze | grep kfp
    
  2. Install v1.15.0 or later of the Vertex AI SDK for Python.
    (Optional) Before installing, run the following command to see which version of the Vertex AI SDK for Python is currently installed:

      pip freeze | grep google-cloud-aiplatform
    
  3. (Optional) Install 390.0.0 or later of the Google Cloud CLI.

  4. Enable the Artifact Registry API.

Configuring permissions

If you have not already set up your gcloud CLI project for Vertex AI Pipelines, follow the instructions in Configure your Google Cloud project for Vertex AI Pipelines.

Additionally, assign the following predefined Identity and Access Management permissions to use Artifact Registry as the template registry:

  • roles/artifactregistry.admin: Assign this role to create and manage a repository.
  • roles/artifactregistry.repoAdmin or roles/artifactregistry.writer: Assign any of these roles to manage templates inside a repository.
  • roles/artifactregistry.reader: Assign this role to download templates from a repository.
  • roles/artifactregistry.reader: Assign this role to a service account associated with Vertex AI Pipelines to create a pipeline run from a template.

For more information about predefined Identity and Access Management roles for Artifact Registry, see Predefined Artifact Registry roles.

Use the following sample to assign roles:

gcloud projects add-iam-policy-binding PROJECT_ID \
    --member=PRINCIPAL \
    --role=ROLE

Replace the following:

  • PROJECT_ID: The project where you want to create the pipeline.
  • PRINCIPAL: The principal to which you are adding permissions.
  • ROLE: The Identity and Access Management role that you want to grant to the principal.

See Roles and permissions in the Artifact Registry documentation for more information about the following:

Create a repository in Artifact Registry

Next you'll create a repository in Artifact Registry for your pipeline templates.

Console

  1. Open Vertex AI Pipelines in the Google Cloud console.

    Go to Vertex AI Pipelines

  2. Click the Your templates tab.

  3. To open the Select repository pane, click Select repository.

  4. Click Create repository.

  5. Specify quickstart-kfp-repo as the repository name.

  6. Under Format, select Kubeflow Pipelines.

  7. Under Location Type, select Region.

  8. In the Region drop-down list, select us-central1.

  9. Click Create.

Google Cloud CLI

Run the following command to create a repository.

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

  • LOCATION: The location or region where you want to create the repository, for example, us-central1

Execute the gcloud artifacts repositories create command:

Linux, macOS, or Cloud Shell

gcloud artifacts repositories create quickstart-kfp-repo --location=LOCATION_ID --repository-format=KFP

Windows (PowerShell)

gcloud artifacts repositories create quickstart-kfp-repo --location=LOCATION_ID --repository-format=KFP

Windows (cmd.exe)

gcloud artifacts repositories create quickstart-kfp-repo --location=LOCATION_ID --repository-format=KFP
 

Create a template

Use the following code sample to define a pipeline with a single component. For information about how to define a pipeline using KFP, see Build a Pipeline.

from kfp import dsl
from kfp import compiler

@dsl.component()
def hello_world(text: str) -> str:
    print(text)
    return text

@dsl.pipeline(name='hello-world', description='A simple intro pipeline')
def pipeline_hello_world(text: str = 'hi there'):
    """Pipeline that passes small pipeline parameter string to consumer op."""

    consume_task = hello_world(
        text=text)  # Passing pipeline parameter as argument to consumer op

compiler.Compiler().compile(
    pipeline_func=pipeline_hello_world,
    package_path='hello_world_pipeline.yaml')

When you run the sample, the compiler.Compiler().compile(...) statement compiles the "hello-world" pipeline into the local YAML file named hello_world_pipeline.yaml.

Upload the template

Console

  1. Open Vertex AI Pipelines in the Google Cloud console.

    Go to Vertex AI Pipelines

  2. Click Upload to open the Upload pipeline or component pane.

  3. In the Repository drop-down list, select the quickstart-kfp-repo repository.

  4. Specify a Name for the pipeline template.

  5. In the File field, click Choose to select and upload the compiled pipeline template YAML from your local file system.

  6. After you upload the pipeline template, it is listed on the Your templates page.

    Go to Your templates

Kubeflow Pipelines SDK client

  1. To configure your Kubeflow Pipelines SDK registry client, run the following commands:

    from kfp.registry import RegistryClient
    
    client = RegistryClient(host=f"https://us-central1-kfp.pkg.dev/PROJECT_ID/quickstart-kfp-repo")
    
  2. Upload the compiled YAML file to your repository in Artifact Registry.

    templateName, versionName = client.upload_pipeline(
      file_name="hello_world_pipeline.yaml",
      tags=["v1", "latest"],
      extra_headers={"description":"This is an example pipeline template."})
    
  3. To verify that the template was uploaded:

    1. Open Vertex AI Pipelines in the Google Cloud console.

      Go to Vertex AI Pipelines

    2. Click the Your templates tab.

    3. Click Select repository.

    4. From the list, select the quickstart-kfp-repo repository, and then click Select.

    5. You should find the uploaded template package hello-world from the list.

    6. To view list of versions of the pipeline template, click the hello-world template.

    7. To view the pipeline topology, click the version.

Use the template in Vertex AI

After you've uploaded your pipeline template to your repository in Artifact Registry, it is ready to be used in Vertex AI Pipelines.

Create a staging bucket for your template

Before you can use your pipeline template, you'll need to create a Cloud Storage bucket for staging pipeline runs.

To create the bucket, follow the instructions in Configure a Cloud Storage bucket for pipeline artifacts and then run the following command:

STAGING_BUCKET="gs://BUCKET_NAME"

Replace BUCKET_NAME with the name of the bucket you just created.

Create a pipeline run from your template

You can use the Vertex AI SDK for Python or the Google Cloud console to create a pipeline run from your template in Artifact Registry.

Console

  1. Open Vertex AI Pipelines in the Google Cloud console.

    Go to Vertex AI Pipelines

  2. Click the Your templates tab.

  3. To open the Select repository pane, click Select repository.

  4. Select the quickstart-kfp-repo repository, and then click Select.

  5. Click the hello-world package.

  6. Next to the 4f245e8f9605 version, click Create Run.

  7. Click Runtime Configuration.

  8. Enter the following under Cloud Storage location:

    gs://BUCKET_NAME
    

    Replace BUCKET_NAME with the name of the bucket you created for staging your pipeline runs.

  9. Click Submit.

Vertex AI SDK for Python

Run the following commands, replacing PROJECT_ID with the Google Cloud project that this pipeline runs in.

from google.cloud import aiplatform

# Initialize the aiplatform package
aiplatform.init(
    project="PROJECT_ID",
    location='us-central1',
    staging_bucket=STAGING_BUCKET)

# Create a job via version id.
job = aiplatform.PipelineJob(
    display_name="hello-world-latest",
    template_path="https://us-central1-kfp.pkg.dev/PROJECT_ID/quickstart-kfp-repo/hello-world/" + \
        versionName)
# Or via tag.
job = aiplatform.PipelineJob(
    display_name="hello-world-latest",
    template_path="https://us-central1-kfp.pkg.dev/PROJECT_ID/quickstart-kfp-repo/hello-world/v1")

job.submit()

View created pipeline runs

You can view the runs created by a specific pipeline version in the Vertex AI SDK for Python.

Console

  1. Open Vertex AI Pipelines in the Google Cloud console.

    Go to Vertex AI Pipelines

  2. Click the Your templates tab.

  3. Click Select repository.

  4. From the list, select the quickstart-kfp-repo repository, and then click Select.

  5. To view list of versions for the hello-world pipeline template, click the hello world template.

  6. Click the desired version for which you want to view pipeline runs.

  7. To view pipeline runs for the selected version, click View Runs and then click the Runs tab.

Vertex AI SDK for Python

To list the pipelines runs, run the pipelineJobs.list command as shown in one or more of the following examples:

  from google.cloud import aiplatform

  # To filter all runs created from a specific version
  filter = 'template_uri:"https://us-central1-kfp.pkg.dev/PROJECT_ID/quickstart-kfp-repo/hello-world/*" AND ' + \
           'template_metadata.version="%s"' % versionName
  aiplatform.PipelineJob.list(filter=filter)

  # To filter all runs created from a specific version tag
  filter = 'template_uri="https://us-central1-kfp.pkg.dev/PROJECT_ID/quickstart-kfp-repo/hello-world/latest"'
  aiplatform.PipelineJob.list(filter=filter)

  # To filter all runs created from a package
  filter = 'template_uri:"https://us-central1-kfp.pkg.dev/PROJECT_ID/quickstart-kfp-repo/hello-world/*"'
  aiplatform.PipelineJob.list(filter=filter)

  # To filter all runs created from a repo
  filter = 'template_uri:"https://us-central1-kfp.pkg.dev/PROJECT_ID/quickstart-kfp-repo/*"'
  aiplatform.PipelineJob.list(filter=filter)

Use the template in a Kubeflow Pipelines SDK registry client

You can use a Kubeflow Pipelines SDK registry client together with Artifact Registry to download and use your pipeline template.

  • To list the resources in the repository, run the following commands:

    templatePackages = client.list_packages()
    templatePackage = client.get_package(package_name = "hello-world")
    
    versions = client.list_versions(package_name="hello-world")
    version = client.get_version(package_name="hello-world", version=versionName)
    
    tags = client.list_tags(package_name = "hello-world")
    tag = client.get_tag(package_name = "hello-world", tag="latest")
    

    For the complete list of available methods and documents, see the proto files in the Artifact Registry GitHub repo.

  • To download the template to your local file system, run the following commands:

    # Sample 1
    filename = client.download_pipeline(
      package_name = "hello-world",
      version = versionName)
    # Sample 2
    filename = client.download_pipeline(
      package_name = "hello-world",
      tag = "v1")
    # Sample 3
    filename = client.download_pipeline(
      package_name = "hello-world",
      tag = "v1",
      file_name = "hello-world-template.yaml")
    

Use the Artifact Registry REST API

The following sections summarize how to use the Artifact Registry REST API to manage your pipeline templates in your Artifact Registry repository.

Upload a pipeline template using the Artifact Registry REST API

You can upload a pipeline template by creating an HTTP request using the parameter values described in this section, where:

  • PROJECT_ID is the Google Cloud project that this pipeline runs in.
  • REPO_ID is the ID of your Artifact Registry repository.

Example curl request

curl -X POST \
    -H "Authorization: Bearer $(gcloud auth print-access-token)" \
    -F tags=v1,latest \
    -F content=@pipeline_spec.yaml \
    https://us-central1-kfp.pkg.dev/PROJECT_ID/REPO_ID

Constructing the upload request

The request is an HTTP or HTTPS multipart request. It must include the authentication token in the request header. For more information, see gcloud auth print-access-token.

The payload of the request is the contents of the pipeline_spec.yaml file (or .zip package). The recommended size limit is 10 MiB.

The package name is taken from the pipeline_spec.pipeline_info.name entry in the pipeline_spec.yaml file. The package name uniquely identifies the package and is immutable across versions. It can be between 4 and 128 characters long and must match the following regular expression: ^[a-z0-9][a-z0-9-]{3,127}$.

The package tags are a list of up to eight comma-separated tags. Each tag must match the following regular expression: ^[a-zA-Z0-9\-._~:@+]{1,128}$.

If a tag exists and points to a pipeline that's already been uploaded, the tag is updated to point to the pipeline that you're currently uploading. For example, if the latest tag points to a pipeline you've already uploaded, and you upload a new version with --tag=latest, the latest tag is removed from the previously uploaded pipeline and assigned to the new pipeline you're uploading.

If the pipeline you're uploading is identical to a pipeline you've previously uploaded, the upload succeeds. The uploaded pipeline's metadata, including its version tags, is updated to match the parameter values of your upload request.

Upload response

If the upload request succeeds, it returns an HTTP OK status. The body of the response is as follows:

{packageName}/{versionName=sha256:abcdef123456...}

where versionName is the sha256 digest of pipeline_spec.yaml formatted as a hex string.

Download a pipeline template using the Artifact Registry REST API

You can download a pipeline template by creating an HTTP request using the parameter values described in this section, where:

  • PROJECT_ID is the Google Cloud project that this pipeline runs in.
  • REPO_ID is the ID of your Artifact Registry repository.
  • PACKAGE_ID is the package ID of your uploaded template.
  • TAG is the version tag.
  • VERSION is the template version in the format of sha256:abcdef123456....

For standard Artifact Registry download, you should form the download link as follows:

`url = https://us-central1-kfp.pkg.dev/PROJECT_ID/REPO_ID/PACKAGE_ID/VERSION`
`url = https://us-central1-kfp.pkg.dev/PROJECT_ID/REPO_ID/PACKAGE_ID/TAG`

Example curl requests

curl -X GET \
    -H "Authorization: Bearer $(gcloud auth print-access-token)" \
    https://us-central1-kfp.pkg.dev/PROJECT_ID/REPO_ID/PACKAGE_ID/VERSION

You can replace VERSION with TAG and download the same template, as shown in the following example:

curl -X GET \
    -H "Authorization: Bearer $(gcloud auth print-access-token)" \
    https://us-central1-kfp.pkg.dev/PROJECT_ID/REPO_ID/PACKAGE_ID/TAG

Download response

If the download request succeeds, it returns an HTTP OK status. The body of the response is the contents of the pipeline_spec.yaml file.