Example

HTTP

Create HTTP APIs that 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

gRPC

To make the above API use gRPC as its protocol, make the following changes (the rest of the steps are the same):

Add protobuf file

Create a predictor.proto file in your project's directory:

<!-- predictor.proto -->
syntax = "proto3";
package text_generator;
service Predictor {
rpc Predict (Message) returns (Message);
}
message Message {
string text = 1;
}

Set the predictor.protobuf_path field in the API spec to point to the predictor.proto file:

# text_generator.yaml
- name: text-generator
kind: RealtimeAPI
predictor:
type: python
path: predictor.py
protobuf_path: predictor.proto
compute:
gpu: 1

Make a gRPC request

grpcurl -plaintext -proto predictor.proto -d '{"text": "hello-world"}' ***.elb.us-west-2.amazonaws.com:80 text_generator.Predictor/Predict