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PDF Processing with Multi-GPU Support / 多GPU支持的PDF处理

中文说明

概述

本项目提供了一个使用多GPU并行处理PDF文件的Python脚本。它基于 MinerU 库，能够将PDF文件转换为Markdown格式，并支持在多GPU环境下高效处理大量文件。

主要功能

多GPU支持：利用多个GPU并行处理PDF文件，显著提高处理速度。
日志记录：支持控制台和文件日志记录，便于调试和监控。

tmux shortcuts & cheatsheet

start new:

tmux

start new with session name:

tmux new -s myname

ZSH CheatSheet

This is a cheat sheet for how to perform various actions to ZSH, which can be tricky to find on the web as the syntax is not intuitive and it is generally not very well-documented.

Strings

Description	Syntax
Get the length of a string	`${#VARNAME}`
Get a single character	`${VARNAME[index]}`

	def solve_me(solution):
	res = solution()
	del res
	if res:
	print("Solved")


	solution = lambda: [
	g.write(b"\x09\x09\x09\x09")
	for x, y, g in [

	// check version
	node -v \|\| node --version

	// list locally installed versions of node
	nvm ls

	// list remove available versions of node
	nvm ls-remote

	// install specific version of node

	# Project Policy

	This policy provides a single, authoritative, and machine-readable source of truth for AI coding agents and humans, ensuring that all work is governed by clear, unambiguous rules and workflows. It aims to eliminate ambiguity, reduce supervision needs, and facilitate automation while maintaining accountability and compliance with best practices.

	# 1. Introduction

	> Rationale: Sets the context, actors, and compliance requirements for the policy, ensuring all participants understand their roles and responsibilities.

	## 1.1 Actors

	import re

	from txtai import Embeddings, LLM

	# Prompt courtesy of the following link: https://github.com/codelion/optillm/blob/main/cot_reflection.py
	def cot(system, user):
	system = f"""
	{system}

	You are an AI assistant that uses a Chain of Thought (CoT) approach with reflection to answer queries. Follow these steps: