API服务的快速搭建和测试
使用Python的FastAPI迅速搭建一个简单API
from fastapi import FastAPI, Request
from transformers import AutoTokenizer, AutoModel
import uvicorn, json, datetime
import torch
# 设置CUDA设备信息
DEVICE = "cuda"
DEVICE_ID = "0"
CUDA_DEVICE = f"{DEVICE}:{DEVICE_ID}" if DEVICE_ID else DEVICE
# 清理CUDA缓存的函数
def torch_gc():
if torch.cuda.is_available():
with torch.cuda.device(CUDA_DEVICE):
torch.cuda.empty_cache()
torch.cuda.ipc_collect()
# 创建FastAPI应用
app = FastAPI()
# 定义POST请求的处理函数
@app.post("/")
async def create_item(request: Request):
global model, tokenizer
# 从请求中获取JSON数据
json_post_raw = await request.json()
json_post = json.dumps(json_post_raw)
json_post_list = json.loads(json_post)
# 从JSON数据中提取必要的参数
prompt = json_post_list.get('prompt')
history = json_post_list.get('history')
max_length = json_post_list.get('max_length')
top_p = json_post_list.get('top_p')
temperature = json_post_list.get('temperature')
# 调用模型生成聊天响应
response, history = model.chat(tokenizer,
prompt,
history=history,
max_length=max_length if max_length else 2048,
top_p=top_p if top_p else 0.8,
temperature=temperature if temperature else 0.8)
# 获取当前时间
now = datetime.datetime.now()
time = now.strftime("%Y-%m-%d %H:%M:%S")
# 构建响应对象
answer = {
"response": response,
"history": history,
"status": 200,
"time": time
}
# 构建日志信息
log = "[" + time + "] " + '", prompt:"' + prompt + '", response:"' + repr(response) + '"'
print(log)
# 调用函数清理CUDA缓存
torch_gc()
# 返回响应
return answer
# 主程序入口
if __name__ == '__main__':
# 加载模型和分词器
tokenizer = AutoTokenizer.from_pretrained("../base_model/chatglm3-6b", trust_remote_code=True)
model = AutoModel.from_pretrained("../base_model/chatglm3-6b", trust_remote_code=True).cuda()
model.eval()
# 启动FastAPI应用
uvicorn.run(app, host='0.0.0.0', port=8000, workers=1)
使用Python调用API
import requests
# 定义请求URL
url = "http://实际API服务地址:8000"
# 定义请求头
headers = {
"Content-Type": "application/json"
}
# 定义请求体数据
data = {
"prompt": "你好",
"history": []
}
# 发送POST请求
response = requests.post(url, headers=headers, json=data)
# 打印响应
print(response.text)