Example

You can deploy an API by providing a project directory. Cortex will save the project directory and make it available during API initialization.

project/
  ├── model.py
  ├── util.py
  ├── predictor.py
  ├── requirements.txt
  └── ...

You can define your Predictor class in a separate python file and import code from your project.

# predictor.py

from model import MyModel

class PythonPredictor:
    def __init__(self, config):
        model = MyModel()

    def predict(payload):
        return model(payload)

Deploy using the Python Client

import cortex

api_spec = {
    "name": "text-generator",
    "kind": "RealtimeAPI",
    "predictor": {
        "type": "python",
        "path": "predictor.py"
    }
}

cx = cortex.client("aws")
cx.create_api(api_spec, project_dir=".")

Deploy using the CLI

# api.yaml

- name: text-generator
  kind: RealtimeAPI
  predictor:
    type: python
    path: predictor.py

$ cortex deploy api.yaml