分布式执行引擎ray入门--（5）Ray Serve

import requests
from starlette.requests import Request
from typing import Dict
 
from ray import serve
 
 
# 1: Define a Ray Serve application.
@serve.deployment
class MyModelDeployment:
    def __init__(self, msg: str):
        # Initialize model state: could be very large neural net weights.
        self._msg = msg
 
    def __call__(self, request: Request) -> Dict:
        return {"result": self._msg}
 
 
app = MyModelDeployment.bind(msg="Hello world!")
 
# 2: Deploy the application locally.
serve.run(app, route_prefix="/")
 
# 3: Query the application and print the result.
print(requests.get("http://localhost:8000/").json())
# {'result': 'Hello world!'}

server脚本：

# File name: serve_quickstart.py
from starlette.requests import Request
 
import ray
from ray import serve
 
from transformers import pipeline
 
 
@serve.deployment(num_replicas=2, ray_actor_options={"num_cpus": 0.2, "num_gpus": 0})
class Translator:
    def __init__(self):
        # Load model
        self.model = pipeline("translation_en_to_fr", model="t5-small")
 
    def translate(self, text: str) -> str:
        # Run inference
        model_output = self.model(text)
 
        # Post-process output to return only the translation text
        translation = model_output[0]["translation_text"]
 
        return translation
 
    async def __call__(self, http_request: Request) -> str:
        english_text: str = await http_request.json()
        return self.translate(english_text)
 
 
translator_app = Translator.bind()

确保server脚本启动 

serve run serve_quickstart:translator_app

默认在服务在http://127.0.0.1:8000/ 运行

client脚本

# File name: model_client.py
import requests
 
english_text = "Hello world!"
 
response = requests.post("http://127.0.0.1:8000/", json=english_text)
french_text = response.text
 
print(french_text)

测试： 

python model_client.py

组合

# File name: serve_quickstart_composed.py
from starlette.requests import Request
 
import ray
from ray import serve
from ray.serve.handle import DeploymentHandle
 
from transformers import pipeline
 
 
@serve.deployment
class Translator:
    def __init__(self):
        # Load model
        self.model = pipeline("translation_en_to_fr", model="t5-small")
 
    def translate(self, text: str) -> str:
        # Run inference
        model_output = self.model(text)
 
        # Post-process output to return only the translation text
        translation = model_output[0]["translation_text"]
 
        return translation
 
 
@serve.deployment
class Summarizer:
    def __init__(self, translator: DeploymentHandle):
        self.translator = translator
 
        # Load model.
        self.model = pipeline("summarization", model="t5-small")
 
    def summarize(self, text: str) -> str:
        # Run inference
        model_output = self.model(text, min_length=5, max_length=15)
 
        # Post-process output to return only the summary text
        summary = model_output[0]["summary_text"]
 
        return summary
 
    async def __call__(self, http_request: Request) -> str:
        english_text: str = await http_request.json()
        summary = self.summarize(english_text)
 
        translation = await self.translator.translate.remote(summary)
        return translation
 
 
app = Summarizer.bind(Translator.bind())

serve run serve_quickstart_composed:app

# File name: composed_client.py
import requests
 
english_text = (
    "It was the best of times, it was the worst of times, it was the age "
    "of wisdom, it was the age of foolishness, it was the epoch of belief"
)
response = requests.post("http://127.0.0.1:8000/", json=english_text)
french_text = response.text
 
print(french_text)

测试：

python composed_client.py

结果：

c'était le meilleur des temps, c'était le pire des temps .