refactor: remove multi_gpu project reference from README files

projects/README.md

@@ -3,5 +3,4 @@
 ## Project List
 
 - Projects not yet compatible with version 2.0:
-  - [multi_gpu](./multi_gpu/README.md): Multi-GPU parallel processing based on LitServe
   - [mcp](./mcp/README.md): MCP server based on the official API

projects/README_zh-CN.md

@@ -3,5 +3,4 @@
 ## 项目列表
 
 - 未兼容2.0版本的项目列表
-  - [multi_gpu](./multi_gpu/README.md): 基于 LitServe 的多 GPU 并行处理
   - [mcp](./mcp/README.md): 基于官方api的mcp server

projects/multi_gpu/README.md

@@ -1,44 +0,0 @@
-## 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 magic-pdf[full] litserve python-multipart filetype
-```
-
-## 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 pdf、jpg/jpeg、png、doc、docx、ppt、pptx 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

@@ -1,39 +0,0 @@
-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 = ['demo/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

@@ -1,98 +0,0 @@
-import os
-import uuid
-import shutil
-import tempfile
-import gc
-import fitz
-import torch
-import base64
-import filetype
-import litserve as ls
-from pathlib import Path
-from fastapi import HTTPException
-
-
-class MinerUAPI(ls.LitAPI):
-    def __init__(self, output_dir='/tmp'):
-        self.output_dir = Path(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'.")
-
-        from magic_pdf.tools.cli import do_parse, convert_file_to_pdf
-        from magic_pdf.model.doc_analyze_by_custom_model import ModelSingleton
-
-        self.do_parse = do_parse
-        self.convert_file_to_pdf = convert_file_to_pdf
-
-        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.cvt2pdf(file)
-        opts = request.get('kwargs', {})
-        opts.setdefault('debug_able', False)
-        opts.setdefault('parse_method', 'auto')
-        return file, opts
-
-    def predict(self, inputs):
-        try:
-            pdf_name = str(uuid.uuid4())
-            output_dir = self.output_dir.joinpath(pdf_name)
-            self.do_parse(self.output_dir, pdf_name, inputs[0], [], **inputs[1])
-            return output_dir
-        except Exception as e:
-            shutil.rmtree(output_dir, ignore_errors=True)
-            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):
-        if torch.cuda.is_available():
-            torch.cuda.empty_cache()
-            torch.cuda.ipc_collect()
-        gc.collect()
-
-    def cvt2pdf(self, file_base64):
-        try:
-            temp_dir = Path(tempfile.mkdtemp())
-            temp_file = temp_dir.joinpath('tmpfile')
-            file_bytes = base64.b64decode(file_base64)
-            file_ext = filetype.guess_extension(file_bytes)
-
-            if file_ext in ['pdf', 'jpg', 'png', 'doc', 'docx', 'ppt', 'pptx']:
-                if file_ext == 'pdf':
-                    return file_bytes
-                elif file_ext in ['jpg', 'png']:
-                    with fitz.open(stream=file_bytes, filetype=file_ext) as f:
-                        return f.convert_to_pdf()
-                else:
-                    temp_file.write_bytes(file_bytes)
-                    self.convert_file_to_pdf(temp_file, temp_dir)
-                    return temp_file.with_suffix('.pdf').read_bytes()
-            else:
-                raise Exception('Unsupported file format')
-        except Exception as e:
-            raise HTTPException(status_code=500, detail=str(e))
-        finally:
-            shutil.rmtree(temp_dir, ignore_errors=True)
-
-
-if __name__ == '__main__':
-    server = ls.LitServer(
-        MinerUAPI(output_dir='/tmp'),
-        accelerator='cuda',
-        devices='auto',
-        workers_per_device=1,
-        timeout=False
-    )
-    server.run(port=8000)