Add Model

Add a New Model — Inference Services

Scaffold all the files needed for a new model server.

Steps

1. Create src/models/<name>/model_server/ directory.
2. Create src/models/<name>/model_server/model.py — subclass kserve.Model with load(), preprocess(), predict(), and a if __name__ == "__main__" block that reads MODEL_NAME and MODEL_PATH from the environment and starts kserve.ModelServer().start([model]). Reference an existing model like revert_risk_model for the pattern.
3. Create src/models/<name>/model_server/requirements.txt with the model's Python dependencies. The shared python/requirements.txt is always installed alongside.
4. Copy and adapt an existing blubber.yaml to .pipeline/<name>/. Use a CPU model (e.g., revertrisk/revertrisk.yaml) as the template unless this model needs a GPU/pyTorch base image.
5. Add the service to docker-compose.yml: platform: linux/amd64, build from .pipeline/<name>/blubber.yaml, expose port 8080, set MODEL_NAME, mount /mnt/models/ from a PATH_TO_<NAME>_MODEL env var.
6. Add two entries in .pipeline/config.yaml: a <name> pipeline (test + production stages) and a <name>-publish pipeline (publish to registry).
7. Create unit tests in test/unit/<name>/.
8. Wire CI triggers in the integration/config repo: jjb/project-pipelines.yaml and zuul/layout.yaml.
9. If the model needs local Makefile support, add a target to the Makefile.

Notes

• Model artifacts are hosted at https://analytics.wikimedia.org/published/wmf-ml-models/.
• The shared Python library at python/ (decorators, preprocess utils, metrics) is available to all models.
• Most models need a .env file locally with PATH_TO_<NAME>_MODEL pointing to downloaded model files.

Input

$ARGUMENTS — the model name in kebab-case (e.g., article-quality, revert-risk-wikidata).