| name | add-model |
| description | Scaffold a new KServe model server from scratch — create the model.py, Blubber config, Docker Compose service, pipeline config, and CI wiring. Use when the engineer wants to add a new inference service model to this repo. |
| argument-hint | [model-name] |
| allowed-tools | ["Bash(mkdir *)","Bash(cp *)","Read","Write","Edit","Glob","Grep"] |
Add a New Model — Inference Services
Scaffold all the files needed for a new model server.
Steps
- Create
src/models/<name>/model_server/ directory.
- 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.
- Create
src/models/<name>/model_server/requirements.txt with the model's Python dependencies. The shared python/requirements.txt is always installed alongside.
- 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.
- 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.
- Add two entries in
.pipeline/config.yaml: a <name> pipeline (test + production stages) and a <name>-publish pipeline (publish to registry).
- Create unit tests in
test/unit/<name>/.
- Wire CI triggers in the
integration/config repo: jjb/project-pipelines.yaml and zuul/layout.yaml.
- 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).
