kfp
Kubeflow Pipelines CLI for managing ML workflows on Kubernetes
TLDR
SYNOPSIS
kfp command [options]
DESCRIPTION
kfp is the CLI for Kubeflow Pipelines, which orchestrates ML workflows as directed acyclic graphs (DAGs) on Kubernetes. Pipelines define reusable components with inputs, outputs, and dependencies.The CLI manages the full pipeline lifecycle: compiling Python pipeline definitions to YAML, uploading pipelines, creating experiments, and submitting runs. It connects to a running KFP backend via the `--endpoint` flag.Kubeflow itself is installed using Kustomize manifests via `kubectl apply -k` from the kubeflow/manifests repository. The older kfctl deployment tool is deprecated and archived.
PARAMETERS
run create|list|get|archive|unarchive|delete
Manage pipeline runs.recurring-run create|list|get|enable|disable|delete
Manage scheduled recurring runs.pipeline create|create-version|list|list-versions|get|delete
Manage pipelines.experiment create|list|get|delete|archive|unarchive
Manage experiments.dsl compile
Compile a Python pipeline definition to YAML.component build
Build a containerized component from a Python function.diagnose_me
Run environment diagnostics (GCP-focused).-e, --experiment name
Experiment name or ID.-r, --run-name name
Name for the run.-f, --package-file file
Compiled pipeline file (YAML).-p, --pipeline-name name
Pipeline name.--endpoint url
KFP API endpoint URL.
CAVEATS
Resource-intensive; requires a Kubernetes cluster with sufficient capacity. The `diagnose_me` command is GCP-focused. The old `kfp run submit` syntax still works as a deprecated alias for `kfp run create`. Similarly, `kfp pipeline upload` is aliased to `kfp pipeline create`.
HISTORY
Kubeflow was started at Google in 2017 as a way to run TensorFlow on Kubernetes. Open-sourced in 2018, it expanded to support the full ML lifecycle. Version 1.0 released in 2020. The project is a CNCF incubating project. The kfp v2 SDK brought a redesigned Python API and CLI with `dsl compile` and simplified component authoring.
