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ray-project--ray/doc/source/serve/doc_code/distilbert.py
T
2026-07-13 13:17:40 +08:00

62 lines
1.6 KiB
Python

# __example_code_start__
from transformers import pipeline
from fastapi import FastAPI
import torch
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())
# __example_code_end__
if __name__ == "__main__":
import requests
import ray
ray.init(runtime_env={"pip": ["transformers==4.36.2", "accelerate==0.28.0"]})
serve.run(entrypoint)
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())
assert resp.status_code == 200