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

├── 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("aws")
cx.create_api(api_spec, project_dir=".")

Deploy using the CLI

# api.yaml
- name: text-generator
kind: RealtimeAPI
type: python
path: handler.py
cortex deploy api.yaml