kfp-kubernetes
Kubernetes platform configuration library and generated protos.
Decision gist · record as of 2026-08-14
Yes, if you are authoring Kubeflow Pipelines and need to configure Kubernetes-specific features like storage, secrets, or scheduling. The package is actively maintained, has low install friction, carries a permissive license, and integrates cleanly with the kfp SDK. Install it only if you are already using Kubeflow Pipelines; it is not useful standalone.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires an active Kubeflow Pipelines installation and a Kubernetes cluster to run pipelines; Python >=3.9.0 required.
- Low install friction with a pure-Python wheel distribution.
- Active maintenance with recent releases; last commit 2026-08-14 and latest release 2026-07-09.
License · maintenance · safety
Apache 2.0 (permissive) — Apache 2.0 permissive license allows commercial and private use with minimal restrictions, typical for open-source infrastructure tools.
last release 2026-07-09 (36 days) · last repo commit 2026-08-14 · 4,184 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 265,733 downloads/mo, #8,316 on PyPI
Alternatives
Verify before relying
pip install kfp-kubernetes
from kfp import dsl
from kfp import kubernetes
@dsl.component
def my_task():
print('hello')
@dsl.pipeline
def my_pipeline():
task = my_task()
kubernetes.use_secret_as_env(task, secret_name='my-secret', secret_key_to_env={'password': 'SECRET_VAR'})- Whether this package works standalone or requires a specific version of kfp to be installed first.
- Compatibility with different Kubernetes versions or Kubeflow Pipelines versions beyond the runtime dependency on kfp.
- Performance characteristics when managing large numbers of volumes or secrets across pipeline tasks.
What it is and what it does
kfp-kubernetes is an addon library to Kubeflow Pipelines SDK that brings native Kubernetes concepts into pipeline authoring. Instead of writing generic pipeline code, you can directly configure Kubernetes-level details like Secrets, PersistentVolumeClaims, node selectors, tolerations, image pull policies, and pod metadata directly on pipeline tasks. This bridges the gap between high-level pipeline logic and low-level Kubernetes pod configuration.
The library works by providing a set of functions that modify task definitions to inject Kubernetes-specific configuration. You define your pipeline components normally using the kfp SDK, then call functions like `use_secret_as_env`, `mount_pvc`, `add_pod_label`, or `set_timeout` to layer on Kubernetes behavior. This approach keeps pipeline logic separate from infrastructure concerns while giving you full control over how tasks run on the cluster.
Use it for
- Mount Kubernetes Secrets as environment variables or volumes in pipeline task containers for credential and configuration management.
- Attach PersistentVolumeClaims to tasks for shared data storage across pipeline stages or ephemeral volumes for temporary task-local storage.
- Configure pod scheduling constraints using node selectors and tolerations to run tasks on specific cluster nodes or hardware.
- Set pod metadata like labels and annotations for observability, cost tracking, or integration with external monitoring systems.
- Control container image pull behavior and set task timeouts using Kubernetes pod spec fields directly from pipeline code.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are authoring Kubeflow Pipelines and need to configure Kubernetes-specific features like storage, secrets, or scheduling.
The package is actively maintained, has low install friction, carries a permissive license, and integrates cleanly with the kfp SDK. Install it only if you are already using Kubeflow Pipelines; it is not useful standalone.
Install
kfp-kubernetes on PyPI
Before you install
Low install friction with a pure-Python wheel distribution. Active maintenance with recent releases; last commit 2026-08-14 and latest release 2026-07-09. Requires Python >=3.9.0.
Requires an active Kubeflow Pipelines installation and a Kubernetes cluster to run pipelines; Python >=3.9.0 required.
License in practice
Apache 2.0 permissive license allows commercial and private use with minimal restrictions, typical for open-source infrastructure tools.
Quickstart
pip install kfp-kubernetes
from kfp import dsl
from kfp import kubernetes
@dsl.component
def my_task():
print('hello')
@dsl.pipeline
def my_pipeline():
task = my_task()
kubernetes.use_secret_as_env(task, secret_name='my-secret', secret_key_to_env={'password': 'SECRET_VAR'})Verify before relying
- Whether this package works standalone or requires a specific version of kfp to be installed first.
- Compatibility with different Kubernetes versions or Kubeflow Pipelines versions beyond the runtime dependency on kfp.
- Performance characteristics when managing large numbers of volumes or secrets across pipeline tasks.
Package facts
| License | Apache 2.0 permissive |
| Python support | Supports the current Python release >=3.9.0 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 4 packagesprotobufkfprequestsurllib3 |
| Maintenance | Actively maintained 36 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 265,733 / month, #8,316 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: kfp_kubernetes-2.17.0-py3-none-any.whl
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See also kfp · kfp-pipeline-spec · kfp-server-api · google-cloud-pipeline-components · kubeflow · kr8s · kubernetes · pykube-ng · kubernetes-typed · portforward