Example

Create APIs that can respond to prediction requests in real-time.

Implement

$ mkdir text-generator && cd text-generator
$ touch predictor.py requirements.txt text_generator.yaml

# predictor.py

from transformers import pipeline

class PythonPredictor:
    def __init__(self, config):
        self.model = pipeline(task="text-generation")

    def predict(self, payload):
        return self.model(payload["text"])[0]

# requirements.txt

transformers
torch

# text_generator.yaml

- name: text-generator
  kind: RealtimeAPI
  predictor:
    type: python
    path: predictor.py
  compute:
    gpu: 1

Deploy

$ cortex deploy text_generator.yaml

Monitor

$ cortex get text-generator --watch

Stream logs

$ cortex logs text-generator

Make a request

$ curl http://***.elb.us-west-2.amazonaws.com/text-generator -X POST -H "Content-Type: application/json" -d '{"text": "hello world"}'

Delete

$ cortex delete text-generator