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<article class="md-content__inner md-typeset">
<h1 id="_1">云计算<a class="headerlink" href="#_1" title="Permanent link">&para;</a></h1>
<p><em>云计算为ML工作负载提供按需基础设施，无需拥有硬件。本文件涵盖服务模型、主要云服务商、容器和Kubernetes、存储、云网络、无服务器计算、成本管理和基础设施即代码</em></p>
<ul>
<li>训练前沿模型需要数千个GPU持续数月。没有初创公司拥有这样的硬件。云计算让你按小时租赁，训练时扩展，推理时缩减，只为使用量付费。理解云基础设施对于任何在笔记本电脑之外构建ML系统的人来说都是必不可少的。</li>
</ul>
<h2 id="_2">云服务模型<a class="headerlink" href="#_2" title="Permanent link">&para;</a></h2>
<p><img alt="云服务层：IaaS给你最大控制权，SaaS给你最少控制权" src="../../images/cloud_service_layers.svg" /></p>
<ul>
<li>云服务按提供商管理程度的层叠划分：</li>
</ul>
<table>
<thead>
<tr>
<th>模型</th>
<th>你管理</th>
<th>提供商管理</th>
<th>示例</th>
</tr>
</thead>
<tbody>
<tr>
<td><strong>IaaS</strong>（基础设施）</td>
<td>操作系统、运行时、应用</td>
<td>硬件、虚拟化、网络</td>
<td>AWS EC2、GCP Compute Engine</td>
</tr>
<tr>
<td><strong>PaaS</strong>（平台）</td>
<td>应用、数据</td>
<td>操作系统、运行时、扩展、修补</td>
<td>AWS SageMaker、GCP Vertex AI</td>
</tr>
<tr>
<td><strong>SaaS</strong>（软件）</td>
<td>什么都不用管（只管用）</td>
<td>一切</td>
<td>OpenAI API、Weights &amp; Biases</td>
</tr>
<tr>
<td><strong>FaaS</strong>（函数）</td>
<td>单个函数</td>
<td>其他所有</td>
<td>AWS Lambda、GCP Cloud Functions</td>
</tr>
</tbody>
</table>
<ul>
<li><strong>对于ML</strong>：大多数团队混合使用。IaaS用于自定义训练（完全控制GPU实例），PaaS用于托管训练和服务（SageMaker、Vertex AI处理编排），SaaS用于工具（W&amp;B用于实验跟踪，OpenAI API用于基线比较）。</li>
</ul>
<h2 id="_3">主要云服务商<a class="headerlink" href="#_3" title="Permanent link">&para;</a></h2>
<h3 id="aws">AWS（亚马逊云服务）<a class="headerlink" href="#aws" title="Permanent link">&para;</a></h3>
<ul>
<li>最大的云服务商（约32%市场份额）。关键ML服务：<ul>
<li><strong>EC2</strong>：虚拟机。GPU实例：p4d（A100）、p5（H100）、g5（A10G用于推理）。</li>
<li><strong>S3</strong>：对象存储。存储数据集和模型权重的标准。几乎无限的容量，约$0.023/GB/月。</li>
<li><strong>SageMaker</strong>：托管ML平台。处理训练、超参数调优、部署和监控。</li>
<li><strong>EKS</strong>：托管Kubernetes。</li>
<li><strong>Lambda</strong>：无服务器函数。不适合GPU工作负载，但适用于预处理和编排。</li>
</ul>
</li>
</ul>
<h3 id="gcp">GCP（谷歌云平台）<a class="headerlink" href="#gcp" title="Permanent link">&para;</a></h3>
<ul>
<li>谷歌的云（约11%市场份额）。关键ML服务：<ul>
<li><strong>Compute Engine</strong>：虚拟机。GPU实例提供A100、H100。<strong>TPU VM</strong>用于TPU访问。</li>
<li><strong>GCS</strong>：对象存储（类似S3）。</li>
<li><strong>Vertex AI</strong>：托管ML平台。原生支持JAX/TPU。</li>
<li><strong>GKE</strong>：托管Kubernetes（最成熟的K8s产品，因为谷歌创建了Kubernetes）。</li>
<li><strong>Cloud TPU</strong>：GCP独有。v5e和v5p用于大规模训练。</li>
</ul>
</li>
</ul>
<h3 id="azure">Azure（微软）<a class="headerlink" href="#azure" title="Permanent link">&para;</a></h3>
<ul>
<li>微软的云（约23%市场份额）。关键ML服务：<ul>
<li><strong>Azure VM</strong>：GPU实例提供A100、H100。</li>
<li><strong>Azure Blob存储</strong>：对象存储。</li>
<li><strong>Azure ML</strong>：托管ML平台。</li>
<li><strong>AKS</strong>：托管Kubernetes。</li>
<li><strong>OpenAI服务</strong>：通过Azure API独家访问OpenAI模型。</li>
</ul>
</li>
</ul>
<h2 id="kubernetes">容器和Kubernetes<a class="headerlink" href="#kubernetes" title="Permanent link">&para;</a></h2>
<ul>
<li>我们在第13章（操作系统）中概念性地介绍了容器（Docker）和Kubernetes，并在第15章（部署）中进行了实践。这里我们关注<strong>云特定的</strong>模式：</li>
</ul>
<h3 id="kubernetesml">Kubernetes用于ML<a class="headerlink" href="#kubernetesml" title="Permanent link">&para;</a></h3>
<ul>
<li><strong>Kubernetes（K8s）</strong>大规模编排容器。关键概念：<ul>
<li><strong>Pod</strong>：最小的可部署单元。包含一个或多个共享网络和存储的容器。一个模型服务Pod可能包含：模型服务器容器 + 用于指标收集的边车容器。</li>
<li><strong>Deployment</strong>：管理一组相同的Pod。指定所需的副本数。如果Pod崩溃，K8s会自动创建替代Pod。</li>
<li><strong>Service</strong>：一组Pod的稳定网络端点。客户端连接到Service；K8s路由到健康的Pod。类型：ClusterIP（内部）、NodePort（通过节点端口对外暴露）、LoadBalancer（通过云负载均衡器对外暴露）。</li>
<li><strong>StatefulSet</strong>：类似Deployment但用于有状态工作负载。每个Pod获得持久的身份和稳定的存储。用于数据库和分布式训练（每个工作者需要稳定的身份以便通信）。</li>
<li><strong>DaemonSet</strong>：在每个节点上运行一个Pod。用于：监控代理（Prometheus节点导出器）、日志收集器（Fluentd）、GPU设备插件（NVIDIA设备插件）。</li>
</ul>
</li>
<li><strong>K8s中的GPU调度</strong>：NVIDIA设备插件将GPU暴露为K8s资源。Pod请求GPU：</li>
</ul>
<div class="highlight"><pre><span></span><code><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a><span class="nt">resources</span><span class="p">:</span>
<a id="__codelineno-0-2" name="__codelineno-0-2" href="#__codelineno-0-2"></a><span class="w"> </span><span class="nt">limits</span><span class="p">:</span>
<a id="__codelineno-0-3" name="__codelineno-0-3" href="#__codelineno-0-3"></a><span class="w"> </span><span class="nt">nvidia.com/gpu</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">2</span><span class="w"> </span><span class="c1"># 此Pod需要2个GPU</span>
</code></pre></div>
<ul>
<li>K8s将Pod调度到具有2个可用GPU的节点上。这就是云ML平台为训练和推理分配GPU的方式。</li>
</ul>
<h3 id="_4">自动缩放<a class="headerlink" href="#_4" title="Permanent link">&para;</a></h3>
<ul>
<li><strong>水平Pod自动缩放器（HPA）</strong>：基于指标（CPU使用率、请求率、自定义指标如GPU利用率或队列深度）缩放Pod数量。</li>
<li><strong>集群自动缩放器</strong>：缩放节点数量。如果由于没有足够节点而无法调度Pod，集群自动缩放器会从云服务商处配置新的VM。当节点利用不足时，它会排空并终止它们。</li>
<li><strong>KEDA</strong>（Kubernetes事件驱动自动缩放）：基于外部事件源（Kafka队列深度、HTTP请求率）进行缩放。非常适合推理：当请求队列增长时扩展模型服务器，当队列为空时缩减。</li>
</ul>
<h2 id="_5">存储<a class="headerlink" href="#_5" title="Permanent link">&para;</a></h2>
<table>
<thead>
<tr>
<th>类型</th>
<th>特性</th>
<th>用途</th>
<th>示例</th>
</tr>
</thead>
<tbody>
<tr>
<td><strong>块存储</strong></td>
<td>低延迟，附加到单台VM</td>
<td>操作系统磁盘、数据库</td>
<td>AWS EBS、GCP Persistent Disk</td>
</tr>
<tr>
<td><strong>对象存储</strong></td>
<td>无限容量，HTTP访问</td>
<td>数据集、模型权重、日志</td>
<td>AWS S3、GCS、Azure Blob</td>
</tr>
<tr>
<td><strong>文件存储</strong></td>
<td>跨VM共享，POSIX</td>
<td>共享训练数据</td>
<td>AWS EFS、GCP Filestore、NFS</td>
</tr>
<tr>
<td><strong>数据湖</strong></td>
<td>读取时定义模式，原始数据</td>
<td>分析、特征工程</td>
<td>Delta Lake、Iceberg、Hudi</td>
</tr>
</tbody>
</table>
<ul>
<li><strong>对于ML训练</strong>：数据集存储在对象存储（S3/GCS）中。训练脚本从对象存储读取数据到内存。对于快速随机访问（随机数据加载），要么：（1）在训练前将数据集下载到本地SSD，（2）使用高吞吐量文件系统（Lustre、FSx），或（3）使用能高效流式和缓存的数据库加载库（WebDataset、FFCV）。</li>
<li><strong>模型权重</strong>：存储在带版本管理的对象存储中。70B模型在FP16下约140 GB。以1 GB/s的速度从S3加载约需2.5分钟。在本地SSD上缓存可减少推理的冷启动时间。</li>
</ul>
<h2 id="_6">云网络<a class="headerlink" href="#_6" title="Permanent link">&para;</a></h2>
<ul>
<li><strong>VPC</strong>（虚拟私有云）：云中的隔离网络。你的VM、数据库和服务在VPC内部通信。外部流量通过负载均衡器或网关进入。</li>
<li><strong>子网</strong>：将VPC划分为多个段。公有子网可访问互联网（用于API服务器）。私有子网不可访问（用于数据库、GPU工作线程）。这是最小权限安全原则在网络上的等价物。</li>
<li><strong>安全组</strong>（AWS）/ <strong>防火墙规则</strong>（GCP）：控制允许哪些流量。"允许来自任何地方的入站HTTP端口80。仅允许来自我的IP的入站SSH端口22。阻止其他所有流量。"安全组配置错误是云安全事件的首要原因。</li>
<li><strong>服务网格</strong>（Istio、Envoy）：管理K8s内部的服务间通信。提供：mTLS加密（每次服务间调用都加密）、流量路由（A/B测试：将10%流量路由到新模型）、重试、超时、断路和可观测性（哪个服务调用了哪个，花了多长时间）。</li>
</ul>
<h2 id="_7">无服务器计算<a class="headerlink" href="#_7" title="Permanent link">&para;</a></h2>
<ul>
<li><strong>无服务器</strong>（AWS Lambda、GCP Cloud Functions）：你上传一个函数，云服务商在触发时运行它。无需管理服务器，无需配置缩放。按调用次数付费（通常每100万次调用$0.20 + 计算时间）。</li>
<li><strong>冷启动</strong>：一段时间不活动后的第一次调用需要更长时间（服务商必须分配容器并加载你的代码）。冷启动为0.5-5秒，使得无服务器不适合对延迟敏感的ML推理。</li>
<li><strong>对于ML</strong>：无服务器适用于：预处理（发送到模型前调整图像大小）、后处理（格式化模型输出，发送通知）、编排（新数据到达时触发训练流水线）和轻量级推理（能容忍冷启动的小模型）。</li>
<li>无服务器<strong>不</strong>适用于：GPU推理（大多数无服务器平台不支持GPU）、长时间运行的训练作业（Lambda的15分钟超时）或有状态服务（调用之间没有持久状态）。</li>
</ul>
<h2 id="_8">成本管理<a class="headerlink" href="#_8" title="Permanent link">&para;</a></h2>
<ul>
<li>云成本是ML团队的首要运营问题。单个H100实例约<span class="arithmatex">\(8/小时。64-GPU训练运行约\)</span>500/小时。一个月的训练运行约$360,000。成本优化是工程问题，不是会计问题。</li>
<li><strong>竞价/抢占式实例</strong>：未使用的云容量以60-90%的折扣出售。服务商可在30秒到2分钟通知后回收。用于：容错训练（经常检查点，在新实例上恢复）、批量推理、数据预处理。不用于：对延迟敏感的服务（中断=停机）。</li>
<li><strong>预留实例</strong>：承诺使用1-3年，享受30-60%折扣。用于：你知道基线负载的稳态推理服务。</li>
<li><strong>自动缩放</strong>：高峰时段扩展，夜间/周末缩减。峰时需要10个GPU、夜间需要2个的模型服务器，通过自动缩放相比24/7运行10个GPU可节省约60%成本。</li>
<li><strong>合理选型</strong>：不要在7B模型上使用H100，如果它在A10G上运行良好。将GPU匹配到工作负载。使用性能分析（第16章）确定最合适的GPU。</li>
<li><strong>存储成本</strong>：对象存储便宜（S3标准约$0.023/GB/月），但会累积。一个团队如果保存每个训练检查点（每个10 GB，每个实验100个，50个实验），累积50 TB = $1,150/月。设置生命周期策略自动删除旧检查点。</li>
</ul>
<h2 id="_9">多区域部署<a class="headerlink" href="#_9" title="Permanent link">&para;</a></h2>
<ul>
<li>对于全球ML系统（服务全球用户），在单个区域部署意味着远程用户的高延迟（东京的用户访问美国服务器会增加约150ms的网络往返）和单点故障（如果该区域宕机，整个服务离线）。</li>
<li><strong>多区域模式</strong>：<ul>
<li><strong>主备模式</strong>：一个主区域处理所有流量。辅助区域有热备（复制数据，准备接收流量）。主区域故障时，DNS切换到辅助区域。故障转移期间的停机时间：30秒到几分钟。</li>
<li><strong>双活模式</strong>：两个区域同时处理流量。用户被路由到最近的区域。两个区域都有最新数据（异步或同步复制）。单区域故障时无停机——流量自动重新路由。</li>
</ul>
</li>
<li><strong>数据复制</strong>：困难的部分。模型权重可以轻松复制（复制到每个区域的S3）。特征存储数据必须以可接受的陈旧度复制。用户数据可能有<strong>数据驻留要求</strong>（GDPR：欧洲用户数据必须留在欧洲）。</li>
<li><strong>GPU云价格比较</strong>（2026年近似值）：</li>
</ul>
<table>
<thead>
<tr>
<th>GPU</th>
<th>AWS</th>
<th>GCP</th>
<th>Azure</th>
<th>典型用途</th>
</tr>
</thead>
<tbody>
<tr>
<td>A10G（24 GB）</td>
<td>$1.00/小时（g5）</td>
<td>$0.90/小时</td>
<td>$0.90/小时</td>
<td>小模型推理</td>
</tr>
<tr>
<td>A100（80 GB）</td>
<td>$4.10/小时（p4d）</td>
<td>$3.70/小时</td>
<td>$3.40/小时</td>
<td>训练、大型推理</td>
</tr>
<tr>
<td>H100（80 GB）</td>
<td>$8.00/小时（p5）</td>
<td>$7.50/小时</td>
<td>$7.00/小时</td>
<td>前沿训练</td>
</tr>
<tr>
<td>TPU v5e</td>
<td>无</td>
<td>$1.20/小时</td>
<td>无</td>
<td>JAX大规模训练</td>
</tr>
</tbody>
</table>
<ul>
<li>竞价/抢占式定价通常比这些价格低60-70%。价格因区域和可用性而异。</li>
</ul>
<h2 id="_10">基础设施即代码<a class="headerlink" href="#_10" title="Permanent link">&para;</a></h2>
<ul>
<li><strong>IaC</strong>在版本控制的配置文件中定义基础设施（VM、网络、数据库、K8s集群）。不是在AWS控制台中点击按钮，而是编写代码描述你想要的内容，然后工具创建它。</li>
<li><strong>Terraform</strong>（HashiCorp）：标准的IaC工具。适用于所有主要云服务商。声明式：你描述期望状态，Terraform计算需要创建/修改/删除什么以达到该状态。</li>
</ul>
<div class="highlight"><pre><span></span><code><a id="__codelineno-1-1" name="__codelineno-1-1" href="#__codelineno-1-1"></a><span class="c1"># main.tf — 创建用于推理的GPU VM</span>
<a id="__codelineno-1-2" name="__codelineno-1-2" href="#__codelineno-1-2"></a><span class="kr">resource</span><span class="w"> </span><span class="nc">&quot;aws_instance&quot;</span><span class="w"> </span><span class="nv">&quot;model_server&quot;</span><span class="w"> </span><span class="p">{</span>
<a id="__codelineno-1-3" name="__codelineno-1-3" href="#__codelineno-1-3"></a><span class="w"> </span><span class="na">ami</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s2">&quot;ami-0abcdef1234567890&quot;</span><span class="c1"> # 深度学习AMI</span>
<a id="__codelineno-1-4" name="__codelineno-1-4" href="#__codelineno-1-4"></a><span class="w"> </span><span class="na">instance_type</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s2">&quot;g5.xlarge&quot;</span><span class="c1"> # A10G GPU</span>
<a id="__codelineno-1-5" name="__codelineno-1-5" href="#__codelineno-1-5"></a>
<a id="__codelineno-1-6" name="__codelineno-1-6" href="#__codelineno-1-6"></a><span class="w"> </span><span class="nb">tags</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">{</span>
<a id="__codelineno-1-7" name="__codelineno-1-7" href="#__codelineno-1-7"></a><span class="w"> </span><span class="na">Name</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s2">&quot;model-server-prod&quot;</span>
<a id="__codelineno-1-8" name="__codelineno-1-8" href="#__codelineno-1-8"></a><span class="w"> </span><span class="p">}</span>
<a id="__codelineno-1-9" name="__codelineno-1-9" href="#__codelineno-1-9"></a><span class="p">}</span>
<a id="__codelineno-1-10" name="__codelineno-1-10" href="#__codelineno-1-10"></a>
<a id="__codelineno-1-11" name="__codelineno-1-11" href="#__codelineno-1-11"></a><span class="kr">resource</span><span class="w"> </span><span class="nc">&quot;aws_s3_bucket&quot;</span><span class="w"> </span><span class="nv">&quot;model_weights&quot;</span><span class="w"> </span><span class="p">{</span>
<a id="__codelineno-1-12" name="__codelineno-1-12" href="#__codelineno-1-12"></a><span class="w"> </span><span class="na">bucket</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s2">&quot;my-model-weights-prod&quot;</span>
<a id="__codelineno-1-13" name="__codelineno-1-13" href="#__codelineno-1-13"></a>
<a id="__codelineno-1-14" name="__codelineno-1-14" href="#__codelineno-1-14"></a><span class="w"> </span><span class="nb">versioning</span><span class="w"> </span><span class="p">{</span>
<a id="__codelineno-1-15" name="__codelineno-1-15" href="#__codelineno-1-15"></a><span class="w"> </span><span class="na">enabled</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="no">true</span>
<a id="__codelineno-1-16" name="__codelineno-1-16" href="#__codelineno-1-16"></a><span class="w"> </span><span class="p">}</span>
<a id="__codelineno-1-17" name="__codelineno-1-17" href="#__codelineno-1-17"></a><span class="p">}</span>
</code></pre></div>
<div class="highlight"><pre><span></span><code><a id="__codelineno-2-1" name="__codelineno-2-1" href="#__codelineno-2-1"></a>terraform<span class="w"> </span>init<span class="w"> </span><span class="c1"># 下载提供商插件</span>
<a id="__codelineno-2-2" name="__codelineno-2-2" href="#__codelineno-2-2"></a>terraform<span class="w"> </span>plan<span class="w"> </span><span class="c1"># 显示将要更改的内容</span>
<a id="__codelineno-2-3" name="__codelineno-2-3" href="#__codelineno-2-3"></a>terraform<span class="w"> </span>apply<span class="w"> </span><span class="c1"># 创建基础设施</span>
<a id="__codelineno-2-4" name="__codelineno-2-4" href="#__codelineno-2-4"></a>terraform<span class="w"> </span>destroy<span class="w"> </span><span class="c1"># 全部拆除</span>
</code></pre></div>
<ul>
<li><strong>IaC为何重要</strong>：可重现性（从代码重建整个基础设施）、审计（git历史显示谁更改了什么）、灾难恢复（从同一配置在不同区域重建）和环境一致性（开发、预发布和生产使用相同模板，仅参数不同）。</li>
<li><strong>Pulumi</strong>：类似Terraform，但使用真正的编程语言（Python、TypeScript、Go）而不是HCL。当基础设施逻辑复杂时（条件、循环、动态配置）很有用。</li>
</ul>
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