{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/2"}],"enrichment":{"capability":"Provides the pipeline specification and protobuf definitions for Kubeflow Pipelines, enabling serialization and validation of ML workflow configurations on Kubernetes.","skillfed_tags":["kubernetes","ml-orchestration","protobuf"],"use_cases":["Serialize and validate Kubeflow pipeline definitions before submission to a Kubeflow cluster.","Inspect or programmatically manipulate pipeline specifications in protobuf format.","Build custom pipeline compilation or transformation tools on top of Kubeflow's specification layer.","Integrate Kubeflow pipeline specs with external ML workflow systems or metadata stores.","Develop Kubeflow extensions or plugins that need to work with pipeline specifications."],"what_it_does":"kfp-pipeline-spec is a low-level component of the Kubeflow Pipelines ecosystem that defines and serializes machine learning pipeline specifications using Protocol Buffers. It provides the data structures and protobuf definitions that represent a Kubeflow pipeline's configuration\u2014its tasks, dependencies, parameters, and execution metadata\u2014in a format that can be validated, stored, and transmitted across the Kubeflow control plane and Kubernetes cluster.\n\nThis package is primarily used internally by the Kubeflow Pipelines SDK and platform components rather than directly by end users. It enables the pipeline specification layer that sits between high-level pipeline definitions (written in Python) and the underlying Kubernetes and Argo Workflows execution engines. If you are building pipelines with Kubeflow, you will interact with this package indirectly through the main SDK; direct use is typically limited to advanced scenarios like custom pipeline compilation or specification inspection.","worth_installing":"Yes, if you are building or extending Kubeflow Pipelines infrastructure or need to work directly with pipeline specifications. No, if you are only writing and running standard ML pipelines\u2014install the main Kubeflow Pipelines SDK instead, which includes this as a dependency. The package is actively maintained, has no known vulnerabilities, and carries permissive licensing; the decision hinges on whether your use case requires direct access to the specification layer."},"id":"kfp-pipeline-spec","links":{"html":"https://skillfed.io/packages/kfp-pipeline-spec","md":"https://skillfed.io/packages/kfp-pipeline-spec.md","pypi":"https://pypi.org/project/kfp-pipeline-spec/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-07-09","license_spdx":null,"license_treatment":"permissive","name":"kfp-pipeline-spec","python_support":"supports_current","summary":"Kubeflow Pipelines pipeline spec"},"popularity":{"monthly_downloads":4798934,"position":2228,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"2.17.0"}