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