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<html lang="zh"><head><meta charset="utf-8"><meta name="viewport" content="width=device-width, initial-scale=1, maximum-scale=1"><meta><title>梁友泽的博客</title><link rel="manifest" href="/manifest.json"><meta name="application-name" content="梁友泽的博客"><meta name="msapplication-TileImage" content="/img/favicon.svg"><meta name="apple-mobile-web-app-capable" content="yes"><meta name="apple-mobile-web-app-title" content="梁友泽的博客"><meta name="apple-mobile-web-app-status-bar-style" content="default"><meta property="og:type" content="blog"><meta property="og:title" content="梁友泽的博客"><meta property="og:url" content="https://www.liangyouze.com/"><meta property="og:site_name" content="梁友泽的博客"><meta property="og:locale" content="zh_CN"><meta property="og:image" content="https://www.liangyouze.com/img/og_image.png"><meta property="article:author" content="梁友泽"><meta property="twitter:card" content="summary"><meta property="twitter:image:src" content="https://www.liangyouze.com/img/og_image.png"><script type="application/ld+json">{"@context":"https://schema.org","@type":"BlogPosting","mainEntityOfPage":{"@type":"WebPage","@id":"https://www.liangyouze.com"},"headline":"梁友泽的博客","image":["https://www.liangyouze.com/img/og_image.png"],"author":{"@type":"Person","name":"梁友泽"},"publisher":{"@type":"Organization","name":"梁友泽的博客","logo":{"@type":"ImageObject","url":"https://www.liangyouze.com/img/logo.svg"}},"description":""}</script><link rel="icon" href="/img/favicon.svg"><link rel="stylesheet" href="https://use.fontawesome.com/releases/v6.0.0/css/all.css"><link data-pjax rel="stylesheet" href="https://cdn.jsdelivr.net/npm/[email protected]/styles/atom-one-light.css"><link rel="stylesheet" href="https://fonts.googleapis.com/css2?family=Ubuntu:wght@400;600&family=Source+Code+Pro"><link data-pjax rel="stylesheet" href="/css/default.css"><style>body>.footer,body>.navbar,body>.section{opacity:0}</style><!--!--><!--!--><!--!--><script src="//busuanzi.ibruce.info/busuanzi/2.3/busuanzi.pure.mini.js" defer></script><!--!--><link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/[email protected]/build/cookieconsent.min.css"><link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/[email protected]/dist/css/lightgallery.min.css"><link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/[email protected]/dist/css/justifiedGallery.min.css"><!--!--><!--!--><!--!--><style>.pace{-webkit-pointer-events:none;pointer-events:none;-webkit-user-select:none;-moz-user-select:none;user-select:none}.pace-inactive{display:none}.pace .pace-progress{background:#3273dc;position:fixed;z-index:2000;top:0;right:100%;width:100%;height:2px}</style><script src="https://cdn.jsdelivr.net/npm/[email protected]/pace.min.js"></script><!--!--><!--!--><!-- hexo injector head_end start --><script>
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</script><!-- hexo injector head_end end --><meta name="generator" content="Hexo 7.3.0"></head><body class="is-2-column"><nav class="navbar navbar-main"><div class="container navbar-container"><div class="navbar-brand justify-content-center"><a class="navbar-item navbar-logo" href="/"><img src="/img/logo.svg" alt="梁友泽的博客" height="28"></a></div><div class="navbar-menu"><div class="navbar-start"><a class="navbar-item is-active" href="/">主页</a><a class="navbar-item" href="/archives">归档</a><a class="navbar-item" href="/categories">分类</a><a class="navbar-item" href="/tags">标签</a><a class="navbar-item" href="/about">关于我</a></div><div class="navbar-end"><a class="navbar-item" target="_blank" rel="noopener" title="Download on GitHub" href="https://github.com/ppoffice/hexo-theme-icarus"><i class="fab fa-github"></i></a><a class="navbar-item search" title="搜索" href="javascript:;"><i class="fas fa-search"></i></a></div></div></div></nav><section class="section"><div class="container"><div class="columns"><div class="column order-2 column-main is-8-tablet is-8-desktop is-8-widescreen"><div class="card"><article class="card-content article" role="article"><div class="article-meta is-size-7 is-uppercase level is-mobile"><div class="level-left"><span class="level-item"><time dateTime="2025-03-20T13:10:23.000Z" title="3/20/2025, 9:10:23 PM">2025-03-20</time>发表</span><span class="level-item"><time dateTime="2025-03-30T10:06:00.528Z" title="3/30/2025, 6:06:00 PM">2025-03-30</time>更新</span><span class="level-item"><a class="link-muted" href="/categories/azure/">azure</a></span><span class="level-item">41 分钟读完 (大约6222个字)</span></div></div><p class="title is-3 is-size-4-mobile"><a class="link-muted" href="/2025/03/20/AI%E9%A9%B1%E5%8A%A8%E7%9A%84%E7%BC%96%E8%AF%91%E5%99%A8%E4%BC%98%E5%8C%96%EF%BC%9AAzure%E4%B8%8ELLVM%E7%9A%84%E8%87%AA%E5%8A%A8%E5%8C%96%E4%BB%A3%E7%A0%81%E5%8A%A0%E9%80%9F%E6%96%B9%E6%A1%88/">AI驱动的编译器优化：Azure与LLVM的自动化代码加速方案</a></p><div class="content"><p>编译器优化领域的应用逐渐成为突破传统性能瓶颈的关键。编译器作为连接高级语言与底层硬件的桥梁，其优化能力直接影响计算任务的执行效率。然而，传统编译器（如LLVM）依赖人工经验设计优化规则，难以应对硬件多样性、算子复杂性及动态场景的挑战。本文将探讨如何通过AI技术重构编译器优化流程，并结合微软Azure云平台的算力资源与LLVM框架，构建端到端的自动化代码加速方案。</div><a class="article-more button is-small is-size-7" href="/2025/03/20/AI%E9%A9%B1%E5%8A%A8%E7%9A%84%E7%BC%96%E8%AF%91%E5%99%A8%E4%BC%98%E5%8C%96%EF%BC%9AAzure%E4%B8%8ELLVM%E7%9A%84%E8%87%AA%E5%8A%A8%E5%8C%96%E4%BB%A3%E7%A0%81%E5%8A%A0%E9%80%9F%E6%96%B9%E6%A1%88/#more">阅读更多</a></article></div><div class="card"><article class="card-content article" role="article"><div class="article-meta is-size-7 is-uppercase level is-mobile"><div class="level-left"><span class="level-item"><time dateTime="2025-03-15T13:10:23.000Z" title="3/15/2025, 9:10:23 PM">2025-03-15</time>发表</span><span class="level-item"><time dateTime="2025-03-30T02:45:23.725Z" title="3/30/2025, 10:45:23 AM">2025-03-30</time>更新</span><span class="level-item"><a class="link-muted" href="/categories/azure/">azure</a></span><span class="level-item">33 分钟读完 (大约4971个字)</span></div></div><p class="title is-3 is-size-4-mobile"><a class="link-muted" href="/2025/03/15/Blackwell%20Ultra%20GPU%E5%9C%A8Azure%20AI%E4%B8%AD%E7%9A%84%E6%9C%AA%E6%9D%A5%E5%B1%95%E6%9C%9B%EF%BC%9A%E4%B8%87%E4%BA%BF%E5%8F%82%E6%95%B0%E6%A8%A1%E5%9E%8B%E8%AE%AD%E7%BB%83/">Blackwell Ultra GPU在Azure AI中的未来展望：万亿参数模型训练</a></p><div class="content"><p>随着生成式AI和大语言模型（LLM）的爆发式发展，模型规模已从千亿级迈向万亿级参数时代。这一趋势对算力基础设施提出了前所未有的挑战：如何高效支持超大规模模型的训练与推理？英伟达最新发布的Blackwell架构GPU与微软Azure AI平台的深度融合，为此提供了革命性的解决方案。本文将从Blackwell Ultra GPU的技术革新、Azure AI的生态系统适配、万亿参数模型训练的具体实践，以及未来技术演进的路径展开分析。</div><a class="article-more button is-small is-size-7" href="/2025/03/15/Blackwell%20Ultra%20GPU%E5%9C%A8Azure%20AI%E4%B8%AD%E7%9A%84%E6%9C%AA%E6%9D%A5%E5%B1%95%E6%9C%9B%EF%BC%9A%E4%B8%87%E4%BA%BF%E5%8F%82%E6%95%B0%E6%A8%A1%E5%9E%8B%E8%AE%AD%E7%BB%83/#more">阅读更多</a></article></div><div class="card"><article class="card-content article" role="article"><div class="article-meta is-size-7 is-uppercase level is-mobile"><div class="level-left"><span class="level-item"><time dateTime="2025-02-27T12:10:23.000Z" title="2/27/2025, 8:10:23 PM">2025-02-27</time>发表</span><span class="level-item"><time dateTime="2025-03-30T10:10:11.487Z" title="3/30/2025, 6:10:11 PM">2025-03-30</time>更新</span><span class="level-item"><a class="link-muted" href="/categories/azure/">azure</a></span><span class="level-item">34 分钟读完 (大约5067个字)</span></div></div><p class="title is-3 is-size-4-mobile"><a class="link-muted" href="/2025/02/27/zure%E4%B8%8ENVIDIA%20Megatron%E7%9A%84%E5%8D%8F%E5%90%8C%E4%BC%98%E5%8C%96%E6%96%B9%E6%A1%88/">zure与NVIDIA Megatron的协同优化方案</a></p><div class="content"><p>在人工智能领域，模型规模的指数级增长对分布式训练技术提出了更高要求。传统的单卡训练模式已无法支撑千亿级参数模型的训练需求，而模型并行技术通过将模型参数、计算任务和优化状态分布到多个设备上，成为突破显存与算力瓶颈的核心手段。微软Azure与NVIDIA Megatron的深度合作，通过软硬件协同优化，开创了模型并行技术的新范式。本文将从技术背景、核心优化方案、实践效果及未来展望等维度，全面解析这一技术体系的创新性与应用价值。 </p></div><a class="article-more button is-small is-size-7" href="/2025/02/27/zure%E4%B8%8ENVIDIA%20Megatron%E7%9A%84%E5%8D%8F%E5%90%8C%E4%BC%98%E5%8C%96%E6%96%B9%E6%A1%88/#more">阅读更多</a></article></div><div class="card"><article class="card-content article" role="article"><div class="article-meta is-size-7 is-uppercase level is-mobile"><div class="level-left"><span class="level-item"><time dateTime="2025-02-19T13:10:23.000Z" title="2/19/2025, 9:10:23 PM">2025-02-19</time>发表</span><span class="level-item"><time dateTime="2025-03-30T10:12:46.437Z" title="3/30/2025, 6:12:46 PM">2025-03-30</time>更新</span><span class="level-item"><a class="link-muted" href="/categories/azure/">azure</a></span><span class="level-item">29 分钟读完 (大约4304个字)</span></div></div><p class="title is-3 is-size-4-mobile"><a class="link-muted" href="/2025/02/19/%E6%B7%B1%E5%85%A5%E7%90%86%E8%A7%A3%E9%AB%98%E5%8F%AF%E6%89%A9%E5%B1%95%E6%80%A7%E5%8F%8A%E5%85%B6%E5%9C%A8%20Azure%20%E4%B8%AD%E7%9A%84%E5%AE%9E%E7%8E%B0/">深入理解高可扩展性及其在 Azure 中的实现</a></p><div class="content"><h3 id="高可扩展性的技术定义与商业价值"><a href="#高可扩展性的技术定义与商业价值" class="headerlink" title="高可扩展性的技术定义与商业价值"></a>高可扩展性的技术定义与商业价值</h3><p>在云计算体系架构中，高可扩展性（High Scalability）本质上是一种弹性工程能力，表现为系统通过智能化的资源编排机制，实现计算、存储、网络等基础资源与业务负载的动态匹配。其核心诉求始终如一：通过纵向扩容（Scale Up）或横向拓容（Scale Out）的灵活组合，构建具备非线性增长能力的数字基础设施，既能在流量脉冲场景下实现毫秒级资源弹性供给，又能在业务低峰期自动回收冗余资源，最终达成服务稳定性与成本效率的黄金平衡。</div><a class="article-more button is-small is-size-7" href="/2025/02/19/%E6%B7%B1%E5%85%A5%E7%90%86%E8%A7%A3%E9%AB%98%E5%8F%AF%E6%89%A9%E5%B1%95%E6%80%A7%E5%8F%8A%E5%85%B6%E5%9C%A8%20Azure%20%E4%B8%AD%E7%9A%84%E5%AE%9E%E7%8E%B0/#more">阅读更多</a></article></div><div class="card"><article class="card-content article" role="article"><div class="article-meta is-size-7 is-uppercase level is-mobile"><div class="level-left"><span class="level-item"><time dateTime="2025-02-12T13:10:23.000Z" title="2/12/2025, 9:10:23 PM">2025-02-12</time>发表</span><span class="level-item"><time dateTime="2025-03-30T10:14:41.457Z" title="3/30/2025, 6:14:41 PM">2025-03-30</time>更新</span><span class="level-item"><a class="link-muted" href="/categories/azure/">azure</a></span><span class="level-item">31 分钟读完 (大约4682个字)</span></div></div><p class="title is-3 is-size-4-mobile"><a class="link-muted" href="/2025/02/12/Azure%E6%97%A0%E6%9C%8D%E5%8A%A1%E5%99%A8GPU%E5%AE%9E%E6%88%98%EF%BC%9A%E4%BD%8E%E6%88%90%E6%9C%AC%E8%BF%90%E8%A1%8C%E5%A4%9A%E6%A8%A1%E6%80%81%E5%A4%A7%E6%A8%A1%E5%9E%8B/">Azure无服务器GPU实战：低成本运行多模态大模型</a></p><div class="content"><p>随着多模态大模型（如视觉-语言模型、文本-音频生成模型等）的快速发展，企业对高效、低成本的算力需求日益迫切。Azure 无服务器 GPU 服务结合其弹性扩展和按需付费的特性，为开发者提供了部署多模态大模型的理想平台。本文将从实战角度，探讨如何基于 Azure 无服务器 GPU 基础设施，低成本运行多模态大模型。</div><a class="article-more button is-small is-size-7" href="/2025/02/12/Azure%E6%97%A0%E6%9C%8D%E5%8A%A1%E5%99%A8GPU%E5%AE%9E%E6%88%98%EF%BC%9A%E4%BD%8E%E6%88%90%E6%9C%AC%E8%BF%90%E8%A1%8C%E5%A4%9A%E6%A8%A1%E6%80%81%E5%A4%A7%E6%A8%A1%E5%9E%8B/#more">阅读更多</a></article></div><div class="card"><article class="card-content article" role="article"><div class="article-meta is-size-7 is-uppercase level is-mobile"><div class="level-left"><span class="level-item"><time dateTime="2025-01-26T13:14:23.000Z" title="1/26/2025, 9:14:23 PM">2025-01-26</time>发表</span><span class="level-item"><time dateTime="2025-03-30T12:15:50.318Z" title="3/30/2025, 8:15:50 PM">2025-03-30</time>更新</span><span class="level-item"><a class="link-muted" href="/categories/azure/">azure</a></span><span class="level-item">40 分钟读完 (大约5955个字)</span></div></div><p class="title is-3 is-size-4-mobile"><a class="link-muted" href="/2025/01/26/%E5%A4%9A%E6%A8%A1%E6%80%81%E6%A8%A1%E5%9E%8B%E6%96%B0%E6%A0%87%E6%9D%86%EF%BC%9AAzure%20AI%E9%9B%86%E6%88%90Mistral%20Small%203.1%E7%9A%84%E5%AE%9E%E8%B7%B5%E6%8C%87%E5%8D%97/">多模态模型新标杆：Azure AI集成Mistral Small 3.1的实践指南</a></p><div class="content"><p>随着人工智能技术的飞速发展，多模态模型逐渐成为推动行业创新的核心引擎。在这一背景下，Mistral AI推出的Small 3（Mistral-Small-24B-Instruct-2501）凭借其240亿参数的强大性能和开源特性，迅速成为高效推理领域的新标杆。与此同时，微软Azure 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class="link-muted" href="/2025/01/20/%E4%BD%BF%E7%94%A8Azure%E4%BA%BA%E8%84%B8API%E5%AF%B9%E5%9B%BE%E7%89%87%E8%BF%9B%E8%A1%8C%E4%BA%BA%E8%84%B8%E8%AF%86%E5%88%AB/">使用Azure人脸API对图片进行人脸识别</a></p><div class="content"><p>人脸识别作为人工智能领域较为成熟的机器学习应用方向，已在多个生产场景中发挥重要作用。从生物特征认证到智能考勤系统，从公共安防监控到商业客流分析，这项技术正持续赋能产业数字化转型。针对中小型企业在技术研发中普遍面临的算法门槛，微软Azure人脸API通过封装先进机器学习模型，提供标准化REST API接口及多语言SDK工具包，有效降低开发者的技术集成难度。</div><a class="article-more button is-small is-size-7" href="/2025/01/20/%E4%BD%BF%E7%94%A8Azure%E4%BA%BA%E8%84%B8API%E5%AF%B9%E5%9B%BE%E7%89%87%E8%BF%9B%E8%A1%8C%E4%BA%BA%E8%84%B8%E8%AF%86%E5%88%AB/#more">阅读更多</a></article></div><div class="card"><article class="card-content article" role="article"><div class="article-meta is-size-7 is-uppercase level is-mobile"><div class="level-left"><span class="level-item"><time dateTime="2025-01-15T13:13:23.000Z" title="1/15/2025, 9:13:23 PM">2025-01-15</time>发表</span><span class="level-item"><time dateTime="2025-03-30T10:16:41.261Z" title="3/30/2025, 6:16:41 PM">2025-03-30</time>更新</span><span class="level-item"><a class="link-muted" href="/categories/azure/">azure</a></span><span class="level-item">22 分钟读完 (大约3355个字)</span></div></div><p class="title is-3 is-size-4-mobile"><a class="link-muted" href="/2025/01/15/%E5%AE%9E%E6%97%B6%E8%AF%AD%E9%9F%B3%E6%8E%A8%E7%90%86%E4%BC%98%E5%8C%96%EF%BC%9AAzure%E4%B8%8ETriton%E7%9A%84%E6%B5%81%E5%BC%8F%E6%89%B9%E5%A4%84%E7%90%86%E6%9E%B6%E6%9E%84%E8%AE%BE%E8%AE%A1/">实时语音推理优化：Azure与Triton的流式批处理架构设计</a></p><div class="content"><p>在智能客服、实时翻译等场景中，语音推理服务的延迟直接影响用户体验。传统批处理架构的固定延迟与静态资源分配难以满足流式数据的动态需求。本文结合微软Azure云平台与NVIDIA Triton推理服务器的技术优势，提出一种融合动态批处理、硬件感知调度和混合精度计算的流式架构设计，在保证99%请求延迟低于200ms的前提下，实现GPU利用率从45%提升至85%的突破。</p></div><a class="article-more button is-small is-size-7" href="/2025/01/15/%E5%AE%9E%E6%97%B6%E8%AF%AD%E9%9F%B3%E6%8E%A8%E7%90%86%E4%BC%98%E5%8C%96%EF%BC%9AAzure%E4%B8%8ETriton%E7%9A%84%E6%B5%81%E5%BC%8F%E6%89%B9%E5%A4%84%E7%90%86%E6%9E%B6%E6%9E%84%E8%AE%BE%E8%AE%A1/#more">阅读更多</a></article></div><div class="card"><article class="card-content article" role="article"><div class="article-meta is-size-7 is-uppercase level is-mobile"><div class="level-left"><span class="level-item"><time dateTime="2025-01-13T13:10:23.000Z" title="1/13/2025, 9:10:23 PM">2025-01-13</time>发表</span><span class="level-item"><time dateTime="2025-03-30T10:19:44.336Z" title="3/30/2025, 6:19:44 PM">2025-03-30</time>更新</span><span class="level-item"><a class="link-muted" href="/categories/azure/">azure</a></span><span class="level-item">8 分钟读完 (大约1160个字)</span></div></div><p class="title is-3 is-size-4-mobile"><a class="link-muted" href="/2025/01/13/Azure%20OpenAI%E6%9C%8D%E5%8A%A1%E5%85%A8%E8%A7%A3%E6%9E%90%EF%BC%9A%E4%BB%8EGPT-4%E5%88%B0DALL-E%E7%9A%84%E6%A8%A1%E5%9E%8B%E7%94%9F%E6%80%81/">Azure OpenAI服务全解析：从GPT-4到DALL-E的模型生态</a></p><div class="content"><h2 id="一、Azure-OpenAI服务全景概览"><a href="#一、Azure-OpenAI服务全景概览" class="headerlink" title="一、Azure OpenAI服务全景概览"></a>一、Azure OpenAI服务全景概览</h2><p>作为微软人工智能战略的核心载体，Azure OpenAI服务构建起覆盖自然语言处理、计算机视觉、语音交互的全栈式AI能力矩阵。该平台集成了OpenAI最前沿的技术成果，通过企业级云服务架构为开发者提供安全可控的AI能力调用环境。其模型体系呈现三大特征：</div><a class="article-more button is-small is-size-7" href="/2025/01/13/Azure%20OpenAI%E6%9C%8D%E5%8A%A1%E5%85%A8%E8%A7%A3%E6%9E%90%EF%BC%9A%E4%BB%8EGPT-4%E5%88%B0DALL-E%E7%9A%84%E6%A8%A1%E5%9E%8B%E7%94%9F%E6%80%81/#more">阅读更多</a></article></div><div class="card"><article class="card-content article" role="article"><div class="article-meta is-size-7 is-uppercase level is-mobile"><div class="level-left"><span class="level-item"><time dateTime="2025-01-12T13:10:23.000Z" title="1/12/2025, 9:10:23 PM">2025-01-12</time>发表</span><span class="level-item"><time dateTime="2025-03-27T09:10:36.760Z" title="3/27/2025, 5:10:36 PM">2025-03-27</time>更新</span><span class="level-item"><a class="link-muted" href="/categories/azure/">azure</a></span><span class="level-item">31 分钟读完 (大约4625个字)</span></div></div><p class="title is-3 is-size-4-mobile"><a class="link-muted" href="/2025/01/12/%E5%A6%82%E4%BD%95%E5%88%A9%E7%94%A8Azure%20AI%E4%BC%98%E5%8C%96%E5%A4%A7%E6%A8%A1%E5%9E%8B%E6%8E%A8%E7%90%86%EF%BC%9ATensorRT-LLM%E4%B8%8EBlackwell%E5%B9%B3%E5%8F%B0%E6%B7%B1%E5%BA%A6%E6%95%B4%E5%90%88/">如何利用Azure AI优化大模型推理：TensorRT-LLM与Blackwell平台深度整合</a></p><div class="content"><p>随着生成式人工智能与大语言模型（DeepSeek、GPT、Llama等）加速渗透产业场景，模型推理的高效性、低延迟和成本控制已成为企业落地的关键突破口。微软Azure AI与英伟达Blackwell平台的深度融合为行业带来突破性解决方案，通过整合TensorRT-LLM的量化优化、动态批处理等核心技术，结合Blackwell架构的万亿级参数处理能力，使DeepSeek等百亿参数大模型的推理效率提升达18倍。这种从底层芯片到中间件、云服务的全栈式优化，不仅为Llama-3、Claude等主流模型提供开箱即用的部署方案，更通过Azure AI云平台的弹性算力调度，将大模型应用的边际成本降低47%，真正打通了从算法创新到商业变现的技术闭环。</div><a 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href="/archives/2021/03/"><span class="level-start"><span class="level-item">三月 2021</span></span><span class="level-end"><span class="level-item tag">1</span></span></a></li><li><a class="level is-mobile" href="/archives/2021/01/"><span class="level-start"><span class="level-item">一月 2021</span></span><span class="level-end"><span class="level-item tag">1</span></span></a></li><li><a class="level is-mobile" href="/archives/2020/10/"><span class="level-start"><span class="level-item">十月 2020</span></span><span class="level-end"><span class="level-item tag">1</span></span></a></li><li><a class="level is-mobile" href="/archives/2020/08/"><span class="level-start"><span class="level-item">八月 2020</span></span><span class="level-end"><span class="level-item tag">1</span></span></a></li><li><a class="level is-mobile" href="/archives/2020/07/"><span class="level-start"><span class="level-item">七月 2020</span></span><span class="level-end"><span class="level-item 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