Serve a Text Classification Model

This example uses a DistilBERT model to build an IMDB review classification application with Ray Serve.

To run this example, install the following:

pip install "ray[serve]" requests torch transformers

This example uses the distilbert-base-uncased model and FastAPI. Save the following code to a file named distilbert_app.py:

Use the following Serve code:

from fastapi import FastAPI
import torch
from transformers import pipeline

from ray import serve
from ray.serve.handle import DeploymentHandle


app = FastAPI()


@serve.deployment(num_replicas=1)
@serve.ingress(app)
class APIIngress:
    def __init__(self, distilbert_model_handle: DeploymentHandle) -> None:
        self.handle = distilbert_model_handle

    @app.get("/classify")
    async def classify(self, sentence: str):
        return await self.handle.classify.remote(sentence)


@serve.deployment(
    ray_actor_options={"num_gpus": 1},
    autoscaling_config={"min_replicas": 0, "max_replicas": 2},
)
class DistilBertModel:
    def __init__(self):
        self.classifier = pipeline(
            "sentiment-analysis",
            model="distilbert-base-uncased",
            framework="pt",
            # Transformers requires you to pass device with index
            device=torch.device("cuda:0"),
        )

    def classify(self, sentence: str):
        return self.classifier(sentence)


entrypoint = APIIngress.bind(DistilBertModel.bind())

Use serve run distilbert_app:entrypoint to start the Serve application.

Note

The autoscaling config sets min_replicas to 0, which means the deployment starts with no ObjectDetection replicas. These replicas spawn only when a request arrives. When no requests arrive after a certain period of time, Serve downscales ObjectDetection back to 0 replica to save GPU resources.

You should see the following messages in the logs:

(ServeController pid=362, ip=10.0.44.233) INFO 2023-03-08 16:44:57,579 controller 362 http_state.py:129 - Starting HTTP proxy with name 'SERVE_CONTROLLER_ACTOR:SERVE_PROXY_ACTOR-7396d5a9efdb59ee01b7befba448433f6c6fc734cfa5421d415da1b3' on node '7396d5a9efdb59ee01b7befba448433f6c6fc734cfa5421d415da1b3' listening on '127.0.0.1:8000'
(ServeController pid=362, ip=10.0.44.233) INFO 2023-03-08 16:44:57,588 controller 362 http_state.py:129 - Starting HTTP proxy with name 'SERVE_CONTROLLER_ACTOR:SERVE_PROXY_ACTOR-a30ea53938547e0bf88ce8672e578f0067be26a7e26d23465c46300b' on node 'a30ea53938547e0bf88ce8672e578f0067be26a7e26d23465c46300b' listening on '127.0.0.1:8000'
(ProxyActor pid=439, ip=10.0.44.233) INFO:     Started server process [439]
(ProxyActor pid=5779) INFO:     Started server process [5779]
(ServeController pid=362, ip=10.0.44.233) INFO 2023-03-08 16:44:59,362 controller 362 deployment_state.py:1333 - Adding 1 replica to deployment 'APIIngress'.
2023-03-08 16:45:01,316 SUCC <string>:93 -- Deployed Serve app successfully.

Use the following code to send requests:

import requests

prompt = "This was a masterpiece. Not completely faithful to the books, but enthralling from beginning to end. Might be my favorite of the three."
input = "%20".join(prompt.split(" "))
resp = requests.get(f"http://127.0.0.1:8000/classify?sentence={prompt}")
print(resp.status_code, resp.json())

The output of the client code is the response status code, the label, which is positive in this example, and the label’s score.

200 [{'label': 'LABEL_1', 'score': 0.9994940757751465}]