https://github.com/wd60622/pymc-mlflow-example
MLflow logging for PyMC
https://github.com/wd60622/pymc-mlflow-example
bayesian-inference machine-learning mlflow
Last synced: 8 months ago
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MLflow logging for PyMC
- Host: GitHub
- URL: https://github.com/wd60622/pymc-mlflow-example
- Owner: wd60622
- Created: 2024-08-19T23:46:43.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-01-23T18:28:08.000Z (8 months ago)
- Last Synced: 2025-02-02T00:01:47.264Z (8 months ago)
- Topics: bayesian-inference, machine-learning, mlflow
- Language: Python
- Homepage:
- Size: 711 KB
- Stars: 7
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# PyMC with MLflow
These are simple examples of using PyMC with MLflow, taking advantage of the
`pymc_marketing.mlflow` module.
This focuses on logging parameters, metrics, and artifacts to MLflow.
Suggestions or Questions? [Comment on this Issue](https://github.com/pymc-labs/pymc-marketing/issues/938)
## Scripts
There are four scripts:
1. [Non-PyMC example showing how to log parameters, metrics, and artifacts to MLflow](./01-basic-introduction.py)
2. [PyMC example which logs some PyMC related metrics to MLflow](./02-pymc-context.py)
3. [Logging that and more with `pymc_marketing.mlflow` module](./03-pymc-autologging.py)
4. [Autologging of Marketing Mix Model with `pymc_marketing.mlflow` module](./04-pymc-marketing-mmm)
Kick them off with `make experiments`. View with `make serve`. Clean up with `make clean_up`.
Use the `environment.yml` file to create the conda environment. i.e. `conda env create -f environment.yml`.
There are some helper functions in the `utils.py` file which help setup mlflow and define some reused PyMC models.
## Resources
- [`pymc_marketing.mlflow` module](https://www.pymc-marketing.io/en/latest/api/generated/pymc_marketing.mlflow.html)
- [MLflow Documentation](https://www.mlflow.org/docs/latest/index.html)