kfp
Kubeflow Pipelines SDK
Decision gist · record as of 2026-08-14
Yes, if you have a Kubeflow backend deployed on Kubernetes and need to orchestrate multi-step ML workflows. The package is actively maintained, permissively licensed, and has low install friction. No known vulnerabilities. Install only if you already have or plan to set up a Kubeflow infrastructure; it is not a standalone ML framework.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires a running Kubeflow Pipelines backend or Kubernetes cluster with Kubeflow deployed; a valid host URL is needed to connect the client.
- Low friction installation with a pure-Python wheel.
- The package is actively maintained with a recent release and has 4183 repository stars.
License · maintenance · safety
permissive license (permissive) — Licensed under Apache Software License (permissive), which allows commercial and private use with minimal restrictions.
last release 2026-07-09 (36 days) · last repo commit 2026-08-14 · 4,183 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 16,374,224 downloads/mo, #1,156 on PyPI
Alternatives
Verify before relying
pip install kfp
from kfp import dsl
import kfp
@dsl.component
def add(a: float, b: float) -> float:
return a + b
@dsl.pipeline(name='Addition pipeline')
def add_pipeline(a: float = 1.0, b: float = 7.0):
task = add(a=a, b=b)
client = kfp.Client(host='<my-host-url>')
client.create_run_from_pipeline_func(add_pipeline, arguments={'a': 7.0, 'b': 8.0})- Whether the package requires specific Kubernetes version compatibility or cluster configuration.
- Performance characteristics when scaling to large numbers of pipeline tasks or concurrent runs.
- Detailed compatibility matrix between kfp versions and Kubeflow backend versions.
What it is and what it does
Kubeflow Pipelines is a Python SDK for building machine learning workflows as directed acyclic graphs of containerized tasks. You define pipeline components using Python decorators, compose them into workflows, and submit them to a Kubeflow backend running on Kubernetes. The SDK handles task orchestration, artifact management, and provides integration with Google Cloud services through its dependencies on google-cloud-storage and google-auth.
The package is designed for teams building reproducible, scalable ML systems. It lets you parameterize pipelines, schedule recurring runs, organize executions into experiments, and visualize results through the Kubeflow Dashboard. Dependencies include Kubernetes client libraries, protocol buffers for serialization, and CLI tools via click for command-line interaction.
Use it for
- Define multi-step ML training workflows with data preprocessing, model training, and evaluation as separate containerized tasks.
- Schedule recurring pipeline runs on a Kubernetes cluster with different parameters or datasets.
- Organize and track multiple pipeline executions as experiments with artifact storage and metadata logging.
- Build reusable pipeline components that can be composed into different workflows across teams.
- Deploy production ML systems where task dependencies, retries, and resource allocation are managed by Kubernetes.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you have a Kubeflow backend deployed on Kubernetes and need to orchestrate multi-step ML workflows.
The package is actively maintained, permissively licensed, and has low install friction. No known vulnerabilities. Install only if you already have or plan to set up a Kubeflow infrastructure; it is not a standalone ML framework.
Install
kfp on PyPI
Before you install
Low friction installation with a pure-Python wheel. The package is actively maintained with a recent release and has 4183 repository stars. Supports Python 3.9 through 3.13.
Requires a running Kubeflow Pipelines backend or Kubernetes cluster with Kubeflow deployed; a valid host URL is needed to connect the client.
License in practice
Licensed under Apache Software License (permissive), which allows commercial and private use with minimal restrictions.
Quickstart
pip install kfp
from kfp import dsl
import kfp
@dsl.component
def add(a: float, b: float) -> float:
return a + b
@dsl.pipeline(name='Addition pipeline')
def add_pipeline(a: float = 1.0, b: float = 7.0):
task = add(a=a, b=b)
client = kfp.Client(host='<my-host-url>')
client.create_run_from_pipeline_func(add_pipeline, arguments={'a': 7.0, 'b': 8.0})Verify before relying
- Whether the package requires specific Kubernetes version compatibility or cluster configuration.
- Performance characteristics when scaling to large numbers of pipeline tasks or concurrent runs.
- Detailed compatibility matrix between kfp versions and Kubeflow backend versions.
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.9.0 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 16 packagesclickclick-option-groupdocstring-parsergoogle-api-coregoogle-authgoogle-cloud-storagekfp-pipeline-speckfp-server-apikubernetesprotobufPyYAMLrequestsrequests-toolbelttabulateurllib3typing-extensions |
| Maintenance | Actively maintained 36 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 16,374,224 / month, #1,156 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Intended Audience :: DevelopersIntended Audience :: EducationIntended Audience :: Science/ResearchLicense :: OSI Approved :: Apache Software LicenseProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.9Topic :: Scientific/EngineeringTopic :: Scientific/Engineering :: Artificial IntelligenceTopic :: Software DevelopmentTopic :: Software Development :: LibrariesTopic :: Software Development :: Libraries :: Python Modules |
Evidence: kfp-2.17.0-py3-none-any.whl
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See also google-cloud-pipeline-components · kfp-kubernetes · kfp-pipeline-spec · kfp-server-api · kubeflow · argo-workflows · zenml · kedro · koheesio · piper