valohai-papi
Experimental imperative Valohai pipeline API
Decision gist · record as of 2026-08-14
Yes, if you are actively using Valohai and prefer imperative Python over YAML configuration. The low install friction and permissive license make it a low-risk experiment. However, be aware that the API is explicitly experimental, documentation is sparse, and the package has not been updated since 2021—suitable for technical exploration or established Valohai users, but not for production systems requiring stability guarantees.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python >=3.6.1,<4.0.0; documentation is minimal and examples are in the repository's papi_examples directory rather than in published docs.
- Low install friction with only two runtime dependencies (valohai-yaml and dataclasses).
- Repository remains active with recent commits, though the package itself has not been updated since 2021-07-08.
License · maintenance · safety
MIT (permissive) — MIT license permits commercial and private use with minimal restrictions, making it suitable for most projects.
last release 2021-07-08 (1863 days) · last repo commit 2026-03-18
0 known vulnerabilities (OSV.dev, 2026-08-14) · 82,808 downloads/mo, #14,133 on PyPI
Alternatives
Verify before relying
pip install valohai-papi
from valohai_papi import Pipeline
# See papi_examples directory in repository for usage examples- Whether the experimental API has stabilized or remains subject to breaking changes since 2021.
- Current state of unimplemented features mentioned in the package description.
- Active user base and production readiness beyond technical validation.
What it is and what it does
Papi is an experimental Python library for declaring Valohai pipelines using imperative syntax rather than declarative configuration. It wraps the Valohai platform's pipeline concepts into a programmatic API, letting you define machine learning workflows directly in Python code. The package depends on valohai-yaml for configuration handling and dataclasses for data structure support.
The library is explicitly marked as experimental and subject to API changes. Documentation is minimal—the primary reference is the papi_examples directory in the repository. The package has been maintained in an active state but has not received updates since mid-2021, suggesting either stability or dormancy depending on adoption.
Use it for
- Define Valohai ML pipelines programmatically in Python instead of writing YAML configuration files.
- Integrate pipeline declaration into Python-based ML workflows for version control and code review.
- Experiment with imperative pipeline APIs as part of Valohai's technical validation process.
- Build dynamic pipelines where structure is determined at runtime based on Python logic.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are actively using Valohai and prefer imperative Python over YAML configuration.
The low install friction and permissive license make it a low-risk experiment. However, be aware that the API is explicitly experimental, documentation is sparse, and the package has not been updated since 2021—suitable for technical exploration or established Valohai users, but not for production systems requiring stability guarantees.
Install
valohai-papi on PyPI
Before you install
Low install friction with only two runtime dependencies (valohai-yaml and dataclasses). Repository remains active with recent commits, though the package itself has not been updated since 2021-07-08.
Requires Python >=3.6.1,<4.0.0; documentation is minimal and examples are in the repository's papi_examples directory rather than in published docs.
License in practice
MIT license permits commercial and private use with minimal restrictions, making it suitable for most projects.
Quickstart
pip install valohai-papi
from valohai_papi import Pipeline
# See papi_examples directory in repository for usage examplesVerify before relying
- Whether the experimental API has stabilized or remains subject to breaking changes since 2021.
- Current state of unimplemented features mentioned in the package description.
- Active user base and production readiness beyond technical validation.
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release >=3.6.1,<4.0.0 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 2 packagesvalohai-yamldataclasses |
| Maintenance | Actively maintained 1,863 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 82,808 / month, #14,133 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | License :: OSI Approved :: MIT LicenseProgramming Language :: Python :: 3Programming Language :: Python :: 3.7Programming Language :: Python :: 3.8Programming Language :: Python :: 3.9 |
Evidence: valohai_papi-0.1.3-py3-none-any.whl
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See also valohai-yaml · azureml-pipeline-core · valohai-utils · kfp-pipeline-spec · azure-ml-component · kfp-server-api · azureml-pipeline-steps · azureml-pipeline · koheesio · kfp