kfp-pipeline-spec
Kubeflow Pipelines pipeline spec
Decision gist · record as of 2026-08-14
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—install 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.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.9.0 or later.
- Low installation friction with a single runtime dependency (protobuf).
- Active maintenance with recent releases; last commit 2026-08-14 and latest release 2026-07-09 indicate ongoing development.
License · maintenance · safety
Apache 2.0 (permissive) — Licensed under Apache 2.0 (permissive), allowing use in commercial and proprietary projects with minimal restrictions beyond attribution and license inclusion.
last release 2026-07-09 (36 days) · last repo commit 2026-08-14 · 4,183 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 4,798,934 downloads/mo, #2,228 on PyPI
Alternatives
Verify before relying
pip install kfp-pipeline-spec
from kfp_pipeline_spec import pipeline_spec_pb2
# Define or load a pipeline specification
spec = pipeline_spec_pb2.PipelineSpec()- Whether this package is typically used standalone or always as part of the full Kubeflow Pipelines SDK installation.
- What specific pipeline specification versions or features are supported in 2.17.0.
- Whether protobuf version constraints exist beyond what pip resolves automatically.
What it is and 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—its tasks, dependencies, parameters, and execution metadata—in a format that can be validated, stored, and transmitted across the Kubeflow control plane and Kubernetes cluster.
This 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.
Use it for
- 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.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
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—install 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.
Install
kfp-pipeline-spec on PyPI
Before you install
Low installation friction with a single runtime dependency (protobuf). Active maintenance with recent releases; last commit 2026-08-14 and latest release 2026-07-09 indicate ongoing development.
Requires Python 3.9.0 or later.
License in practice
Licensed under Apache 2.0 (permissive), allowing use in commercial and proprietary projects with minimal restrictions beyond attribution and license inclusion.
Quickstart
pip install kfp-pipeline-spec
from kfp_pipeline_spec import pipeline_spec_pb2
# Define or load a pipeline specification
spec = pipeline_spec_pb2.PipelineSpec()Verify before relying
- Whether this package is typically used standalone or always as part of the full Kubeflow Pipelines SDK installation.
- What specific pipeline specification versions or features are supported in 2.17.0.
- Whether protobuf version constraints exist beyond what pip resolves automatically.
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 | 1 packageprotobuf |
| Maintenance | Actively maintained 36 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 4,798,934 / month, #2,228 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: kfp_pipeline_spec-2.17.0-py3-none-any.whl
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