Add multi_gpu process project

projects/README.md

@@ -6,5 +6,4 @@
 - [gradio_app](./gradio_app/README.md): Build a web app based on gradio
 - [web_demo](./web_demo/README.md): MinerU online [demo](https://opendatalab.com/OpenSourceTools/Extractor/PDF/) localized deployment version
 - [web_api](./web_api/README.md): Web API Based on FastAPI
-
-
+- [multi_gpu](./multi_gpu/README.md): Multi-GPU parallel processing based on LitServe

projects/README_zh-CN.md

@@ -6,4 +6,4 @@
 - [gradio_app](./gradio_app/README_zh-CN.md): 基于 Gradio 的 Web 应用
 - [web_demo](./web_demo/README_zh-CN.md): MinerU在线[demo](https://opendatalab.com/OpenSourceTools/Extractor/PDF/)本地化部署版本
 - [web_api](./web_api/README.md): 基于 FastAPI 的 Web API
-
+- [multi_gpu](./multi_gpu/README.md): 基于 LitServe 的多 GPU 并行处理

projects/multi_gpu/README.md

+## 项目简介
+本项目提供基于 LitServe 的多 GPU 并行处理方案。LitServe 是一个简便且灵活的 AI 模型服务引擎，基于 FastAPI 构建。它为 FastAPI 增强了批处理、流式传输和 GPU 自动扩展等功能，无需为每个模型单独重建 FastAPI 服务器。
+
+## 环境配置
+请使用以下命令配置所需的环境：
+```bash
+pip install -U litserve python-multipart filetype
+pip install -U magic-pdf[full] --extra-index-url https://wheels.myhloli.com
+pip install paddlepaddle-gpu==3.0.0b1 -i https://www.paddlepaddle.org.cn/packages/stable/cu118
+```
+
+## 快速使用
+### 1. 启动服务端
+以下示例展示了如何启动服务端，支持自定义设置：
+```python
+server = ls.LitServer(
+    MinerUAPI(output_dir='/tmp'),  # 可自定义输出文件夹
+    accelerator='cuda',  # 启用 GPU 加速
+    devices='auto',  # "auto" 使用所有 GPU
+    workers_per_device=1,  # 每个 GPU 启动一个服务实例
+    timeout=False  # 设置为 False 以禁用超时
+)
+server.run(port=8000)  # 设定服务端口为 8000
+```
+
+启动服务端命令：
+```bash
+python server.py
+```
+
+### 2. 启动客户端
+以下代码展示了客户端的使用方式，可根据需求修改配置：
+```python
+files = ['demo/small_ocr.pdf']  # 替换为文件路径，支持 jpg/jpeg、png、pdf 文件
+n_jobs = np.clip(len(files), 1, 8)  # 设置并发线程数，此处最大为 8，可根据自身修改
+results = Parallel(n_jobs, prefer='threads', verbose=10)(
+    delayed(do_parse)(p) for p in files
+)
+print(results)
+```
+
+启动客户端命令：
+```bash
+python client.py
+```
+好了，你的文件会自动在多个 GPU 上并行处理！🍻🍻🍻

projects/multi_gpu/client.py

+import base64
+import requests
+import numpy as np
+from loguru import logger
+from joblib import Parallel, delayed
+
+
+def to_b64(file_path):
+    try:
+        with open(file_path, 'rb') as f:
+            return base64.b64encode(f.read()).decode('utf-8')
+    except Exception as e:
+        raise Exception(f'File: {file_path} - Info: {e}')
+
+
+def do_parse(file_path, url='http://127.0.0.1:8000/predict', **kwargs):
+    try:
+        response = requests.post(url, json={
+            'file': to_b64(file_path),
+            'kwargs': kwargs
+        })
+
+        if response.status_code == 200:
+            output = response.json()
+            output['file_path'] = file_path
+            return output
+        else:
+            raise Exception(response.text)
+    except Exception as e:
+        logger.error(f'File: {file_path} - Info: {e}')
+
+
+if __name__ == '__main__':
+    files = ['small_ocr.pdf']
+    n_jobs = np.clip(len(files), 1, 8)
+    results = Parallel(n_jobs, prefer='threads', verbose=10)(
+        delayed(do_parse)(p) for p in files
+    )
+    print(results)

projects/multi_gpu/server.py

+import os
+import fitz
+import torch
+import base64
+import litserve as ls
+from uuid import uuid4
+from fastapi import HTTPException
+from filetype import guess_extension
+from magic_pdf.tools.common import do_parse
+from magic_pdf.model.doc_analyze_by_custom_model import ModelSingleton
+
+
+class MinerUAPI(ls.LitAPI):
+    def __init__(self, output_dir='/tmp'):
+        self.output_dir = output_dir
+
+    def setup(self, device):
+        if device.startswith('cuda'):
+            os.environ['CUDA_VISIBLE_DEVICES'] = device.split(':')[-1]
+            if torch.cuda.device_count() > 1:
+                raise RuntimeError("Remove any CUDA actions before setting 'CUDA_VISIBLE_DEVICES'.")
+
+        model_manager = ModelSingleton()
+        model_manager.get_model(True, False)
+        model_manager.get_model(False, False)
+        print(f'Model initialization complete on {device}!')
+
+    def decode_request(self, request):
+        file = request['file']
+        file = self.to_pdf(file)
+        opts = request.get('kwargs', {})
+        opts.setdefault('debug_able', False)
+        opts.setdefault('parse_method', 'auto')
+        return file, opts
+
+    def predict(self, inputs):
+        try:
+            do_parse(self.output_dir, pdf_name := str(uuid4()), inputs[0], [], **inputs[1])
+            return pdf_name
+        except Exception as e:
+            raise HTTPException(status_code=500, detail=str(e))
+        finally:
+            self.clean_memory()
+
+    def encode_response(self, response):
+        return {'output_dir': response}
+
+    def clean_memory(self):
+        import gc
+        if torch.cuda.is_available():
+            torch.cuda.empty_cache()
+            torch.cuda.ipc_collect()
+        gc.collect()
+
+    def to_pdf(self, file_base64):
+        try:
+            file_bytes = base64.b64decode(file_base64)
+            file_ext = guess_extension(file_bytes)
+            with fitz.open(stream=file_bytes, filetype=file_ext) as f:
+                if f.is_pdf: return f.tobytes()
+                return f.convert_to_pdf()
+        except Exception as e:
+            raise HTTPException(status_code=500, detail=str(e))
+
+
+if __name__ == '__main__':
+    server = ls.LitServer(
+        MinerUAPI(output_dir='/tmp'),
+        accelerator='cuda',
+        devices='auto',
+        workers_per_device=1,
+        timeout=False
+    )
+    server.run(port=8000)