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
  ├── handler.py
  ├── requirements.txt
  └── ...

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

# handler.py

from model import MyModel

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

    def handle_post(payload):
        return model(payload)

Deploy using the Python Client

import cortex

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

cx = cortex.client("cortex")
cx.deploy(api_spec, project_dir=".")

Deploy using the CLI

# api.yaml

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

cortex deploy api.yaml