wehub-resource-sync
59 lines
1.5 KiB
Python
59 lines
1.5 KiB
Python
# flake8: noqa
|
|
|
|
# __deployment_start__
|
|
import ray
|
|
from ray import serve
|
|
from fastapi import FastAPI
|
|
|
|
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
|
|
|
|
app = FastAPI()
|
|
|
|
|
|
@serve.deployment(num_replicas=2, ray_actor_options={"num_cpus": 0.2, "num_gpus": 0})
|
|
@serve.ingress(app)
|
|
class Translator:
|
|
def __init__(self):
|
|
# Load model
|
|
self.tokenizer = AutoTokenizer.from_pretrained("t5-small")
|
|
self.model = AutoModelForSeq2SeqLM.from_pretrained("t5-small")
|
|
|
|
@app.post("/")
|
|
def translate(self, text: str) -> str:
|
|
# Run inference
|
|
input_ids = self.tokenizer(
|
|
f"translate English to French: {text}", return_tensors="pt"
|
|
).input_ids
|
|
output_ids = self.model.generate(
|
|
input_ids, num_beams=4, early_stopping=True, max_length=300
|
|
)
|
|
|
|
# Post-process output to return only the translation text
|
|
translation = self.tokenizer.decode(
|
|
output_ids[0], skip_special_tokens=True, clean_up_tokenization_spaces=False
|
|
)
|
|
|
|
return translation
|
|
|
|
|
|
translator_app = Translator.bind()
|
|
# __deployment_end__
|
|
|
|
translator_app = Translator.options(ray_actor_options={}).bind()
|
|
serve.run(translator_app)
|
|
|
|
# __client_function_start__
|
|
# File name: model_client.py
|
|
import requests
|
|
|
|
response = requests.post("http://127.0.0.1:8000/", params={"text": "Hello world!"})
|
|
french_text = response.json()
|
|
|
|
print(french_text)
|
|
# __client_function_end__
|
|
|
|
assert french_text == "Bonjour monde!"
|
|
|
|
serve.shutdown()
|
|
ray.shutdown()
|