y settings.

Enabling CUDA for GPU-backed hardwareNote that if you wish to use this orchestrator to run steps on a GPU, you will need to follow the instructions on this page to ensure that it works. It requires adding some extra settings customization and is essential to enable CUDA for the GPU to give its full acceleration.

Important Note for Multi-Tenancy Deployments

Kubeflow has a notion of multi-tenancy built into its deployment. Kubeflow's multi-user isolation simplifies user operations because each user only views and edited the Kubeflow components and model artifacts defined in their configuration.

Using the ZenML Kubeflow orchestrator on a multi-tenant deployment without any settings will result in the following error:

HTTP response body: {"error":"Invalid input error: Invalid resource references for experiment. ListExperiment requires filtering by namespace.","code":3,"message":"Invalid input error: Invalid resource references for experiment. ListExperiment requires filtering by

namespace.","details":[{"@type":"type.googleapis.com/api.Error","error_message":"Invalid resource references for experiment. ListExperiment requires filtering by namespace.","error_details":"Invalid input error: Invalid resource references for experiment. ListExperiment requires filtering by namespace."}]}

In order to get it to work, we need to leverage the KubeflowOrchestratorSettings referenced above. By setting the namespace option, and by passing in the right authentication credentials to the Kubeflow Pipelines Client, we can make it work.

First, when registering your Kubeflow orchestrator, please make sure to include the kubeflow_hostname parameter. The kubeflow_hostname must end with the /pipeline post-fix.

zenml orchestrator register <NAME> \

--flavor=kubeflow \

--kubeflow_hostname=<KUBEFLOW_HOSTNAME> # e.g. https://mykubeflow.example.com/pipeline

Then, ensure that you use the pass the right settings before triggering a pipeline run. The following snippet will prove useful:

import requests