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Ray Serve

Ray Serve is a scalable model serving library for building online inference APIs. Serve is particularly well suited for system composition, enabling you to build a complex inference service consisting of multiple chains and business logic all in Python code.

Goal of this notebook​

This notebook shows a simple example of how to deploy an OpenAI chain into production. You can extend it to deploy your own self-hosted models where you can easily define amount of hardware resources (GPUs and CPUs) needed to run your model in production efficiently. Read more about available options including autoscaling in the Ray Serve documentation.

Setup Ray Serve​

Install ray with pip install ray[serve].

General Skeleton​

The general skeleton for deploying a service is the following:

# 0: Import ray serve and request from starlette
from ray import serve
from starlette.requests import Request

# 1: Define a Ray Serve deployment.
class LLMServe:
def __init__(self) -> None:
# All the initialization code goes here

async def __call__(self, request: Request) -> str:
# You can parse the request here
# and return a response
return "Hello World"

# 2: Bind the model to deployment
deployment = LLMServe.bind()

# 3: Run the deployment
# Shutdown the deployment

Example of deploying and OpenAI chain with custom prompts​

Get an OpenAI API key from here. By running the following code, you will be asked to provide your API key.

from langchain.chains import LLMChain
from langchain_core.prompts import PromptTemplate
from langchain_openai import OpenAI
API Reference:LLMChain | PromptTemplate | OpenAI
from getpass import getpass

OPENAI_API_KEY = getpass()
class DeployLLM:
def __init__(self):
# We initialize the LLM, template and the chain here
llm = OpenAI(openai_api_key=OPENAI_API_KEY)
template = "Question: {question}\n\nAnswer: Let's think step by step."
prompt = PromptTemplate.from_template(template)
self.chain = LLMChain(llm=llm, prompt=prompt)

def _run_chain(self, text: str):
return self.chain(text)

async def __call__(self, request: Request):
# 1. Parse the request
text = request.query_params["text"]
# 2. Run the chain
resp = self._run_chain(text)
# 3. Return the response
return resp["text"]

Now we can bind the deployment.

# Bind the model to deployment
deployment = DeployLLM.bind()

We can assign the port number and host when we want to run the deployment.

# Example port number
# Run the deployment, port=PORT_NUMBER)

Now that service is deployed on port localhost:8282 we can send a post request to get the results back.

import requests

text = "What NFL team won the Super Bowl in the year Justin Beiber was born?"
response ="http://localhost:{PORT_NUMBER}/?text={text}")

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