Tensorflow2.0笔记 - Himmelblau函数优化案例

一、目录

1 框架
2. 入门
3. 安装教程
4. 相关文档、案例阅读

二、实现

1 框架：Ray：将一个模型拆分到多个显卡中，实现分布式预测、训练等功能。
2. 入门 ：
案例：通过ray 实现分布式部署，分布式推理服务。
参考：https://zhuanlan.zhihu.com/p/647973148?utm_id=0
文件名：test.py
pip install ray
pip install “ray[serve]”

import pandas as pd

import ray
from ray import serve
from starlette.requests import Request

@serve.deployment(ray_actor_options={"num_gpus": 2})        #两个gpu 将模型拆分，进行推理
class PredictDeployment:
    def __init__(self, model_id: str):
        from transformers import AutoModelForCausalLM, AutoTokenizer
        import torch

        self.model = AutoModelForCausalLM.from_pretrained(
            model_id,
            torch_dtype=torch.float16,
            device_map="auto",
        )
        self.tokenizer = AutoTokenizer.from_pretrained(model_id)

    def generate(self, text: str) -> pd.DataFrame:
        input_ids = self.tokenizer(text, return_tensors="pt").input_ids.to(
            self.model.device
        )
        gen_tokens = self.model.generate(
            input_ids,
            temperature=0.9,
            max_length=200,
        )
        return pd.DataFrame(
            self.tokenizer.batch_decode(gen_tokens), columns=["responses"]
        )

    async def __call__(self, http_request: Request) -> str:    #异步实现http 框架
        json_request: str = await http_request.json()
        print(json_request)
        prompt=json_request[0]

        return self.generate(prompt["text"])

deployment = PredictDeployment.bind(model_id="huggyllama/llama-13b")

运行>> serve run test:deployment #后台启动的 后台关闭指令： serve shutdown
测试：

import requests
sample_input = {"text": "Funniest joke ever:"}
output = requests.post("http://localhost:8000/", json=[sample_input]).json()
print(output)

3. 安装教程
   pip install ray
   依赖环境：https://github.com/ray-project/ray
4. 相关文档、案例阅读
   网址： https://github.com/ray-project/ray
   文档-案例：https://docs.ray.io/en/latest/serve/index.html