Setup and Connection

pip install kfp Install Kubeflow Pipelines SDK kubectl port-forward -n kubeflow svc/ml-pipeline-ui 8080:80 Port forwarding to access Kubeflow Pipelines UI kfp connect --endpoint http://localhost:8080 Connect to a local Kubeflow Pipelines instance kfp connect --endpoint https://your-kubeflow-domain.com Connect to a remote Kubeflow Pipelines instance

Pipeline Management

kfp pipeline list List all pipelines kfp pipeline get [PIPELINE_ID] Get pipeline details kfp pipeline upload [PIPELINE_PACKAGE_PATH] Upload a pipeline package kfp pipeline delete [PIPELINE_ID] Delete a pipeline

Experiment Management

kfp experiment list List all experiments kfp experiment create [EXPERIMENT_NAME] Create a new experiment kfp experiment get [EXPERIMENT_ID] Get experiment details kfp experiment delete [EXPERIMENT_ID] Delete an experiment

Run Management

kfp run list List all runs kfp run get [RUN_ID] Get run details kfp run create --experiment-name [EXPERIMENT_NAME] --pipeline-id [PIPELINE_ID] Create a run kfp run delete [RUN_ID] Delete a run

Recurring Run Management

kfp recurring_run list List all recurring runs kfp recurring_run get [RECURRING_RUN_ID] Get recurring run details kfp recurring_run create --experiment-name [EXPERIMENT_NAME] --pipeline-id [PIPELINE_ID] --cron-expression [CRON] Create a recurring run kfp recurring_run delete [RECURRING_RUN_ID] Delete a recurring run

Python SDK Examples

client = kfp.Client() Initialize the KFP client client.list_pipelines() List pipelines using Python SDK client.get_experiment(experiment_name='default') Get experiment by name client.run_pipeline(experiment_id, pipeline_name, params) Run a pipeline with parameters