Connecting to Kubeflow Pipelines on Google Cloud using the SDK

How to connect to different Kubeflow Pipelines installations on Google Cloud using the Kubeflow Pipelines SDK

This guide describes how to connect to your Kubeflow Pipelines cluster on Google Cloud using the Kubeflow Pipelines SDK.

Before you begin

How SDK connects to Kubeflow Pipelines API

Kubeflow Pipelines includes an API service named ml-pipeline-ui. The ml-pipeline-ui API service is deployed in the same Kubernetes namespace you deployed Kubeflow Pipelines in.

The Kubeflow Pipelines SDK can send REST API requests to this API service, but the SDK needs to know the hostname to connect to the API service.

If the hostname can be accessed without authentication, it’s very simple to connect to it. For example, you can use kubectl port-forward to access it via localhost:

# The Kubeflow Pipelines API service and the UI is available at
# http://localhost:3000 without authentication check.
$ kubectl port-forward svc/ml-pipeline-ui 3000:80 --namespace kubeflow
# Change the namespace if you deployed Kubeflow Pipelines in a different
# namespace.
import kfp
client = kfp.Client(host='http://localhost:3000')

When deploying Kubeflow Pipelines on Google Cloud, a public endpoint for this API service is auto-configured for you, but this public endpoint has security checks to protect your cluster from unauthorized access.

The following sections introduce how to authenticate your SDK requests to connect to Kubeflow Pipelines via the public endpoint.

Connecting to Kubeflow Pipelines standalone or AI Platform Pipelines

Refer to Connecting to AI Platform Pipelines using the Kubeflow Pipelines SDK for both Kubeflow Pipelines standalone and AI Platform Pipelines.

Kubeflow Pipelines standalone deployments also show up in AI Platform Pipelines. They have the name “pipeline” by default, but you can customize the name by overriding the appName parameter in params.env when deploying Kubeflow Pipelines standalone.

Connecting to Kubeflow Pipelines in a full Kubeflow deployment

A full Kubeflow deployment on Google Cloud uses an Identity-Aware Proxy (IAP) to manage access to the public Kubeflow endpoint. The steps below let you connect to Kubeflow Pipelines in a full Kubeflow deployment with authentication through IAP.

  1. Find out your IAP OAuth 2.0 client ID.

    You or your cluster admin followed Set up OAuth for Cloud IAP to deploy your full Kubeflow deployment on Google Cloud. You need the OAuth client ID created in that step.

    You can browse all of your existing OAuth client IDs in the Credentials page of Google Cloud Console.

  2. Create another SDK OAuth Client ID for authenticating Kubeflow Pipelines SDK users. Follow the steps to set up a client ID to authenticate from a desktop app. Take a note of the client ID and client secret. This client ID and secret can be shared among all SDK users, because a separate login step is still needed below.

  3. To connect to Kubeflow Pipelines public endpoint, initiate SDK client like the following:

    import kfp
    client = kfp.Client(host='https://<KF_NAME>.endpoints.<PROJECT>',
    • Pass your IAP OAuth client ID found in step 1 to client_id argument.
    • Pass your SDK OAuth client ID and secret created in step 2 to other_client_id and other_client_secret arguments.
  4. When you init the SDK client for the first time, you will be asked to log in. The Kubeflow Pipelines SDK stores obtained credentials in $HOME/.config/kfp/credentials.json. You do not need to log in again unless you manually delete the credentials file.

    To use the SDK from cron tasks where you cannot log in manually, you can copy the credentials file in `$HOME/.config/kfp/credentials.json` to another machine.
    However, you should keep the credentials safe and never expose it to
    third parties.
  5. After login, you can use the client.



  • Error “Failed to authorize with API resource references: there is no user identity header” when using SDK methods.

    Direct access to the API service without authentication works for Kubeflow Pipelines standalone, AI Platform Pipelines, and Kubeflow 1.0 or earlier.

    However, it fails authorization checks for Kubeflow Pipelines with multi-user isolation in the full Kubeflow deployment starting from Kubeflow 1.1. Multi-user isolation requires all API access to authenticate as a user. Refer to Kubeflow Pipelines Multi-user isolation documentation for more details.


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Last modified January 3, 2023: Fix hyper-links (8c0f3b4)