This release is a pre-release and may not be stable for production use.
pynb-dag-runner
pynb-dag-runner is a Python library that can run ML/data pipelines on stateless compute infrastructure (that may be ephemeral or serverless).
This means that pynb-dag-runner does not need a tracking server (or database) to record task outcomes (like logged ML metrics, models, artifacts).
Instead, pipeline outputs are emitted using the OpenTelemetry standard.
Since structured logs can be directed to a file (as one option), this can be used to run pipelines on limited or no cloud infrastructure;
after pipeline execution one only needs to preserve the structured logs.
Documentation and architecture
Demo
-
The below shows a demo ML training pipeline that uses only Github infrastructure (that is: Github actions for compute; Build artifacts for storage; and Github Pages for reporting). This uses
pynb-dag-runnerand a fork of MLFlow that can be deployed as a static website (see, https://github.com/pynb-dag-runner/mlflow). -
Codes for pipeline (MIT): https://github.com/pynb-dag-runner/mnist-digits-demo-pipeline
Roadmap and project planning
Install via PyPI
Latest release
pip install pynb-dag-runner- https://pypi.org/project/pynb-dag-runner
Snapshot of latest commit to main branch
pip install pynb-dag-runner-snapshot- https://pypi.org/project/pynb-dag-runner-snapshot
Any feedback/ideas welcome!
License
(c) Matias Dahl 2021-2022, MIT, see LICENSE.md.
1 maintainer
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