{"categories":[{"label":"Distributed Computing","url":"https://skillfed.io/packages/category/system-distributed-computing/2"}],"enrichment":{"capability":"Extends Kubeflow Pipelines SDK with Kubernetes-native features like Secrets, PersistentVolumeClaims, node selectors, tolerations, and pod metadata configuration for pipeline tasks.","skillfed_tags":["kubeflow","kubernetes-integration","ml-pipelines"],"use_cases":["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."],"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.\n\nThe 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.","worth_installing":"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."},"id":"kfp-kubernetes","links":{"html":"https://skillfed.io/packages/kfp-kubernetes","md":"https://skillfed.io/packages/kfp-kubernetes.md","pypi":"https://pypi.org/project/kfp-kubernetes/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-07-09","license_spdx":null,"license_treatment":"permissive","name":"kfp-kubernetes","python_support":"supports_current","summary":"Kubernetes platform configuration library and generated protos."},"popularity":{"monthly_downloads":265733,"position":8316,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"2.17.0"}