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<h1 id="devops">部署与 DevOps<a class="headerlink" href="#devops" title="Permanent link">&para;</a></h1>
<p><em>部署是你的模型从研究产物变成产品的地方。本文涵盖用于机器学习的 Docker、模型推理、实验追踪、可重现性、生产环境监控、特征存储和管道编排——这些基础设施将一个训练好的模型从 notebook 带到数百万用户面前。</em></p>
<ul>
<li>
<p>一个只在你笔记本电脑上运行的模型是原型。一个能够可靠地大规模运行、在毫秒内提供预测结果、能够从故障中恢复并在不中断服务的情况下更新的模型才是产品。两者之间的差距就是<strong>部署与 DevOps</strong>。</p>
</li>
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<p>大多数机器学习工程师在部署、监控和调试生产问题上花费的时间比训练模型还多。理解这些基础设施对于任何构建真实 ML 系统的人来说都不是可选项。</p>
</li>
</ul>
<h2 id="docker">用于机器学习的 Docker<a class="headerlink" href="#docker" title="Permanent link">&para;</a></h2>
<ul>
<li>
<p>我们在第 13 章（操作系统）中概念性地介绍了容器。这里我们关注实践方面：为机器学习工作负载编写 Dockerfile。</p>
</li>
<li>
<p><strong>Dockerfile</strong> 是构建容器镜像的配方：</p>
</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="c"># 从官方的 CUDA 基础镜像开始</span>
<a id="__codelineno-0-2" name="__codelineno-0-2" href="#__codelineno-0-2"></a><span class="k">FROM</span><span class="w"> </span><span class="s">nvidia/cuda:12.1.0-cudnn8-runtime-ubuntu22.04</span>
<a id="__codelineno-0-3" name="__codelineno-0-3" href="#__codelineno-0-3"></a>
<a id="__codelineno-0-4" name="__codelineno-0-4" href="#__codelineno-0-4"></a><span class="c"># 系统依赖</span>
<a id="__codelineno-0-5" name="__codelineno-0-5" href="#__codelineno-0-5"></a><span class="k">RUN</span><span class="w"> </span>apt-get<span class="w"> </span>update<span class="w"> </span><span class="o">&amp;&amp;</span><span class="w"> </span>apt-get<span class="w"> </span>install<span class="w"> </span>-y<span class="w"> </span><span class="se">\</span>
<a id="__codelineno-0-6" name="__codelineno-0-6" href="#__codelineno-0-6"></a><span class="w"> </span>python3.11<span class="w"> </span>python3-pip<span class="w"> </span>git<span class="w"> </span><span class="se">\</span>
<a id="__codelineno-0-7" name="__codelineno-0-7" href="#__codelineno-0-7"></a><span class="w"> </span><span class="o">&amp;&amp;</span><span class="w"> </span>rm<span class="w"> </span>-rf<span class="w"> </span>/var/lib/apt/lists/*
<a id="__codelineno-0-8" name="__codelineno-0-8" href="#__codelineno-0-8"></a>
<a id="__codelineno-0-9" name="__codelineno-0-9" href="#__codelineno-0-9"></a><span class="c"># Python 依赖（单独安装以利用缓存）</span>
<a id="__codelineno-0-10" name="__codelineno-0-10" href="#__codelineno-0-10"></a><span class="k">COPY</span><span class="w"> </span>requirements.txt<span class="w"> </span>.
<a id="__codelineno-0-11" name="__codelineno-0-11" href="#__codelineno-0-11"></a><span class="k">RUN</span><span class="w"> </span>pip<span class="w"> </span>install<span class="w"> </span>--no-cache-dir<span class="w"> </span>-r<span class="w"> </span>requirements.txt
<a id="__codelineno-0-12" name="__codelineno-0-12" href="#__codelineno-0-12"></a>
<a id="__codelineno-0-13" name="__codelineno-0-13" href="#__codelineno-0-13"></a><span class="c"># 复制源代码（频繁更改，因此此层放在最后）</span>
<a id="__codelineno-0-14" name="__codelineno-0-14" href="#__codelineno-0-14"></a><span class="k">COPY</span><span class="w"> </span>src/<span class="w"> </span>/app/src/
<a id="__codelineno-0-15" name="__codelineno-0-15" href="#__codelineno-0-15"></a><span class="k">COPY</span><span class="w"> </span>configs/<span class="w"> </span>/app/configs/
<a id="__codelineno-0-16" name="__codelineno-0-16" href="#__codelineno-0-16"></a><span class="k">WORKDIR</span><span class="w"> </span><span class="s">/app</span>
<a id="__codelineno-0-17" name="__codelineno-0-17" href="#__codelineno-0-17"></a>
<a id="__codelineno-0-18" name="__codelineno-0-18" href="#__codelineno-0-18"></a><span class="c"># 入口点</span>
<a id="__codelineno-0-19" name="__codelineno-0-19" href="#__codelineno-0-19"></a><span class="k">CMD</span><span class="w"> </span><span class="p">[</span><span class="s2">&quot;python3&quot;</span><span class="p">,</span><span class="w"> </span><span class="s2">&quot;src/scripts/serve.py&quot;</span><span class="p">,</span><span class="w"> </span><span class="s2">&quot;--config&quot;</span><span class="p">,</span><span class="w"> </span><span class="s2">&quot;configs/serve.yaml&quot;</span><span class="p">]</span>
</code></pre></div>
<ul>
<li>
<p><strong>层缓存</strong>：Docker 会缓存每一层。如果 <code>requirements.txt</code> 没有变化，<code>pip install</code> 在重新构建时会被跳过。将不常更改的层（系统包、pip 安装）放在频繁更改的层（源代码）之前。这将 10 分钟的构建变成 10 秒的重新构建。</p>
</li>
<li>
<p><strong>GPU 访问</strong>：使用 <code>nvidia/cuda</code> 基础镜像，并使用 <code>docker run --gpus all</code> 运行。<code>nvidia-container-toolkit</code> 提供从宿主机到容器的 GPU 透传。</p>
</li>
<li>
<p><strong>多阶段构建</strong>通过将构建环境与运行环境分离来减小镜像大小：</p>
</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="c"># 构建阶段：安装构建工具、编译依赖</span>
<a id="__codelineno-1-2" name="__codelineno-1-2" href="#__codelineno-1-2"></a><span class="k">FROM</span><span class="w"> </span><span class="s">python:3.11</span><span class="w"> </span><span class="k">AS</span><span class="w"> </span><span class="s">builder</span>
<a id="__codelineno-1-3" name="__codelineno-1-3" href="#__codelineno-1-3"></a><span class="k">COPY</span><span class="w"> </span>requirements.txt<span class="w"> </span>.
<a id="__codelineno-1-4" name="__codelineno-1-4" href="#__codelineno-1-4"></a><span class="k">RUN</span><span class="w"> </span>pip<span class="w"> </span>install<span class="w"> </span>--user<span class="w"> </span>-r<span class="w"> </span>requirements.txt
<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="c"># 运行阶段：仅运行环境依赖</span>
<a id="__codelineno-1-7" name="__codelineno-1-7" href="#__codelineno-1-7"></a><span class="k">FROM</span><span class="w"> </span><span class="s">nvidia/cuda:12.1.0-cudnn8-runtime-ubuntu22.04</span>
<a id="__codelineno-1-8" name="__codelineno-1-8" href="#__codelineno-1-8"></a><span class="k">COPY</span><span class="w"> </span>--from<span class="o">=</span>builder<span class="w"> </span>/root/.local<span class="w"> </span>/root/.local
<a id="__codelineno-1-9" name="__codelineno-1-9" href="#__codelineno-1-9"></a><span class="k">COPY</span><span class="w"> </span>src/<span class="w"> </span>/app/src/
<a id="__codelineno-1-10" name="__codelineno-1-10" href="#__codelineno-1-10"></a><span class="k">ENV</span><span class="w"> </span><span class="nv">PATH</span><span class="o">=</span>/root/.local/bin:<span class="nv">$PATH</span>
</code></pre></div>
<ul>
<li>
<p>最终镜像只包含运行时库，不包含编译器、头文件或构建工具。一个 5GB 的构建镜像变成了 2GB 的运行镜像。</p>
</li>
<li>
<p><strong>Docker Compose</strong> 运行多容器设置（模型服务器 + 负载均衡器 + 监控）：</p>
</li>
</ul>
<div class="highlight"><pre><span></span><code><a id="__codelineno-2-1" name="__codelineno-2-1" href="#__codelineno-2-1"></a><span class="c1"># docker-compose.yml</span>
<a id="__codelineno-2-2" name="__codelineno-2-2" href="#__codelineno-2-2"></a><span class="nt">services</span><span class="p">:</span>
<a id="__codelineno-2-3" name="__codelineno-2-3" href="#__codelineno-2-3"></a><span class="w"> </span><span class="nt">model</span><span class="p">:</span>
<a id="__codelineno-2-4" name="__codelineno-2-4" href="#__codelineno-2-4"></a><span class="w"> </span><span class="nt">build</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">.</span>
<a id="__codelineno-2-5" name="__codelineno-2-5" href="#__codelineno-2-5"></a><span class="w"> </span><span class="nt">ports</span><span class="p">:</span>
<a id="__codelineno-2-6" name="__codelineno-2-6" href="#__codelineno-2-6"></a><span class="w"> </span><span class="p p-Indicator">-</span><span class="w"> </span><span class="s">&quot;8080:8080&quot;</span>
<a id="__codelineno-2-7" name="__codelineno-2-7" href="#__codelineno-2-7"></a><span class="w"> </span><span class="nt">deploy</span><span class="p">:</span>
<a id="__codelineno-2-8" name="__codelineno-2-8" href="#__codelineno-2-8"></a><span class="w"> </span><span class="nt">resources</span><span class="p">:</span>
<a id="__codelineno-2-9" name="__codelineno-2-9" href="#__codelineno-2-9"></a><span class="w"> </span><span class="nt">reservations</span><span class="p">:</span>
<a id="__codelineno-2-10" name="__codelineno-2-10" href="#__codelineno-2-10"></a><span class="w"> </span><span class="nt">devices</span><span class="p">:</span>
<a id="__codelineno-2-11" name="__codelineno-2-11" href="#__codelineno-2-11"></a><span class="w"> </span><span class="p p-Indicator">-</span><span class="w"> </span><span class="nt">capabilities</span><span class="p">:</span><span class="w"> </span><span class="p p-Indicator">[</span><span class="nv">gpu</span><span class="p p-Indicator">]</span>
<a id="__codelineno-2-12" name="__codelineno-2-12" href="#__codelineno-2-12"></a><span class="w"> </span><span class="nt">prometheus</span><span class="p">:</span>
<a id="__codelineno-2-13" name="__codelineno-2-13" href="#__codelineno-2-13"></a><span class="w"> </span><span class="nt">image</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">prom/prometheus</span>
<a id="__codelineno-2-14" name="__codelineno-2-14" href="#__codelineno-2-14"></a><span class="w"> </span><span class="nt">ports</span><span class="p">:</span>
<a id="__codelineno-2-15" name="__codelineno-2-15" href="#__codelineno-2-15"></a><span class="w"> </span><span class="p p-Indicator">-</span><span class="w"> </span><span class="s">&quot;9090:9090&quot;</span>
</code></pre></div>
<h2 id="_1">模型推理<a class="headerlink" href="#_1" title="Permanent link">&para;</a></h2>
<ul>
<li>
<p><strong>模型推理</strong>是将推理作为服务运行：接收请求、运行模型、返回预测结果。</p>
</li>
<li>
<p><strong>FastAPI</strong>（在文件 03 中介绍）适用于低到中等吞吐量的最简单方法。对于高吞吐量和 GPU 优化推理，使用专用工具：</p>
</li>
<li>
<p><strong>Triton Inference Server</strong>（NVIDIA）：以 TensorRT、ONNX、PyTorch 和 TensorFlow 格式提供模型。特性：</p>
<ul>
<li><strong>动态批处理</strong>：收集单个请求并将它们分批处理以提高 GPU 效率。单个请求流被分组为 32 的批次，大幅提高吞吐量。</li>
<li><strong>模型集成</strong>：在单个请求中链式调用多个模型（预处理器 → 模型 → 后处理器）。</li>
<li><strong>多模型推理</strong>：在同一 GPU 上提供多个模型，共享资源。</li>
<li><strong>并发模型执行</strong>：在同一 GPU 上并行运行多个推理请求。</li>
</ul>
</li>
<li>
<p><strong>TorchServe</strong>（PyTorch）：以 REST/gRPC API 提供 PyTorch 模型。支持模型版本控制、A/B 测试和自定义处理器。</p>
</li>
<li>
<p><strong>vLLM</strong>：专门用于 LLM 推理。实现了 PagedAttention（高效的 KV 缓存管理）、连续批处理和跨 GPU 的张量并行。对于大语言模型，吞吐量比朴素推理高出 10-20 倍。</p>
</li>
<li>
<p><strong>Cactus</strong>（<a href="https://github.com/cactus-compute/cactus">github.com/cactus-compute/cactus</a>）：一个用于移动端和边缘端设备推理的低延迟 AI 引擎。Cactus 提供<strong>兼容 OpenAI 的 API</strong>（聊天补全、流式传输、工具调用、转录、嵌入、RAG、视觉），完全在设备上运行，当本地模型无法处理请求时自动进行<strong>云回退</strong>。这种混合架构意味着你的应用程序代码使用相同的 API，无论推理是在本地还是在云端运行——引擎根据模型置信度和设备能力来决定。提供 Python、Swift、Kotlin、Flutter、React Native 和 Rust 的 SDK，以及 HuggingFace 上预转换的模型权重。支持多模态推理（LLM、视觉、语音），配备自定义 ARM SIMD 内核以实现 ARM CPU 上的最快推理，以及零拷贝内存映射以实现 10 倍 RAM 使用降低（第 16 章、第 17 章）。</p>
</li>
<li>
<p><strong>模型格式优化</strong>：</p>
<ul>
<li><strong>ONNX</strong>：用于互操作性的开放格式。从 PyTorch/TensorFlow 导出，在任何地方运行。</li>
<li><strong>TensorRT</strong>：NVIDIA 的优化器。融合层、选择最佳内核、量化权重。在 NVIDIA GPU 上通常比 PyTorch 快 2-5 倍。</li>
<li><strong>GGUF/GGML</strong>：适用于 CPU 高效推理的格式，在消费级硬件上运行 LLM 时很流行。</li>
</ul>
</li>
</ul>
<h2 id="_2">实验追踪<a class="headerlink" href="#_2" title="Permanent link">&para;</a></h2>
<ul>
<li>
<p>没有实验追踪，机器学习研究会退化为："我觉得上周二那个我改了些配置的模型是最好的，但我不记得改了啥。"</p>
</li>
<li>
<p><strong>Weights &amp; Biases（W&amp;B）</strong>：最流行的实验追踪工具。从你的训练脚本中记录任何内容：</p>
</li>
</ul>
<div class="highlight"><pre><span></span><code><a id="__codelineno-3-1" name="__codelineno-3-1" href="#__codelineno-3-1"></a><span class="kn">import</span><span class="w"> </span><span class="nn">wandb</span>
<a id="__codelineno-3-2" name="__codelineno-3-2" href="#__codelineno-3-2"></a>
<a id="__codelineno-3-3" name="__codelineno-3-3" href="#__codelineno-3-3"></a><span class="n">wandb</span><span class="o">.</span><span class="n">init</span><span class="p">(</span><span class="n">project</span><span class="o">=</span><span class="s2">&quot;my-project&quot;</span><span class="p">,</span> <span class="n">config</span><span class="o">=</span><span class="p">{</span>
<a id="__codelineno-3-4" name="__codelineno-3-4" href="#__codelineno-3-4"></a> <span class="s2">&quot;model&quot;</span><span class="p">:</span> <span class="s2">&quot;transformer&quot;</span><span class="p">,</span>
<a id="__codelineno-3-5" name="__codelineno-3-5" href="#__codelineno-3-5"></a> <span class="s2">&quot;lr&quot;</span><span class="p">:</span> <span class="mf">3e-4</span><span class="p">,</span>
<a id="__codelineno-3-6" name="__codelineno-3-6" href="#__codelineno-3-6"></a> <span class="s2">&quot;batch_size&quot;</span><span class="p">:</span> <span class="mi">64</span><span class="p">,</span>
<a id="__codelineno-3-7" name="__codelineno-3-7" href="#__codelineno-3-7"></a><span class="p">})</span>
<a id="__codelineno-3-8" name="__codelineno-3-8" href="#__codelineno-3-8"></a>
<a id="__codelineno-3-9" name="__codelineno-3-9" href="#__codelineno-3-9"></a><span class="k">for</span> <span class="n">epoch</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_epochs</span><span class="p">):</span>
<a id="__codelineno-3-10" name="__codelineno-3-10" href="#__codelineno-3-10"></a> <span class="n">train_loss</span> <span class="o">=</span> <span class="n">train_one_epoch</span><span class="p">()</span>
<a id="__codelineno-3-11" name="__codelineno-3-11" href="#__codelineno-3-11"></a> <span class="n">val_loss</span> <span class="o">=</span> <span class="n">validate</span><span class="p">()</span>
<a id="__codelineno-3-12" name="__codelineno-3-12" href="#__codelineno-3-12"></a>
<a id="__codelineno-3-13" name="__codelineno-3-13" href="#__codelineno-3-13"></a> <span class="n">wandb</span><span class="o">.</span><span class="n">log</span><span class="p">({</span>
<a id="__codelineno-3-14" name="__codelineno-3-14" href="#__codelineno-3-14"></a> <span class="s2">&quot;train/loss&quot;</span><span class="p">:</span> <span class="n">train_loss</span><span class="p">,</span>
<a id="__codelineno-3-15" name="__codelineno-3-15" href="#__codelineno-3-15"></a> <span class="s2">&quot;val/loss&quot;</span><span class="p">:</span> <span class="n">val_loss</span><span class="p">,</span>
<a id="__codelineno-3-16" name="__codelineno-3-16" href="#__codelineno-3-16"></a> <span class="s2">&quot;epoch&quot;</span><span class="p">:</span> <span class="n">epoch</span><span class="p">,</span>
<a id="__codelineno-3-17" name="__codelineno-3-17" href="#__codelineno-3-17"></a> <span class="p">})</span>
<a id="__codelineno-3-18" name="__codelineno-3-18" href="#__codelineno-3-18"></a>
<a id="__codelineno-3-19" name="__codelineno-3-19" href="#__codelineno-3-19"></a> <span class="c1"># 将模型记录为产物</span>
<a id="__codelineno-3-20" name="__codelineno-3-20" href="#__codelineno-3-20"></a> <span class="k">if</span> <span class="n">val_loss</span> <span class="o">&lt;</span> <span class="n">best_loss</span><span class="p">:</span>
<a id="__codelineno-3-21" name="__codelineno-3-21" href="#__codelineno-3-21"></a> <span class="n">wandb</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s2">&quot;best_model.pt&quot;</span><span class="p">)</span>
<a id="__codelineno-3-22" name="__codelineno-3-22" href="#__codelineno-3-22"></a>
<a id="__codelineno-3-23" name="__codelineno-3-23" href="#__codelineno-3-23"></a><span class="n">wandb</span><span class="o">.</span><span class="n">finish</span><span class="p">()</span>
</code></pre></div>
<ul>
<li>
<p>W&amp;B 提供：用于比较运行的仪表板、超参数扫描工具、模型注册表、数据集版本控制和团队协作。</p>
</li>
<li>
<p><strong>MLflow</strong>：开源替代方案。在本地或服务器上运行：</p>
</li>
</ul>
<div class="highlight"><pre><span></span><code><a id="__codelineno-4-1" name="__codelineno-4-1" href="#__codelineno-4-1"></a><span class="kn">import</span><span class="w"> </span><span class="nn">mlflow</span>
<a id="__codelineno-4-2" name="__codelineno-4-2" href="#__codelineno-4-2"></a>
<a id="__codelineno-4-3" name="__codelineno-4-3" href="#__codelineno-4-3"></a><span class="n">mlflow</span><span class="o">.</span><span class="n">set_experiment</span><span class="p">(</span><span class="s2">&quot;my-experiment&quot;</span><span class="p">)</span>
<a id="__codelineno-4-4" name="__codelineno-4-4" href="#__codelineno-4-4"></a>
<a id="__codelineno-4-5" name="__codelineno-4-5" href="#__codelineno-4-5"></a><span class="k">with</span> <span class="n">mlflow</span><span class="o">.</span><span class="n">start_run</span><span class="p">():</span>
<a id="__codelineno-4-6" name="__codelineno-4-6" href="#__codelineno-4-6"></a> <span class="n">mlflow</span><span class="o">.</span><span class="n">log_params</span><span class="p">({</span><span class="s2">&quot;lr&quot;</span><span class="p">:</span> <span class="mf">3e-4</span><span class="p">,</span> <span class="s2">&quot;batch_size&quot;</span><span class="p">:</span> <span class="mi">64</span><span class="p">})</span>
<a id="__codelineno-4-7" name="__codelineno-4-7" href="#__codelineno-4-7"></a> <span class="n">mlflow</span><span class="o">.</span><span class="n">log_metric</span><span class="p">(</span><span class="s2">&quot;val_loss&quot;</span><span class="p">,</span> <span class="mf">0.042</span><span class="p">,</span> <span class="n">step</span><span class="o">=</span><span class="n">epoch</span><span class="p">)</span>
<a id="__codelineno-4-8" name="__codelineno-4-8" href="#__codelineno-4-8"></a> <span class="n">mlflow</span><span class="o">.</span><span class="n">pytorch</span><span class="o">.</span><span class="n">log_model</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="s2">&quot;model&quot;</span><span class="p">)</span>
</code></pre></div>
<ul>
<li><strong>模型注册表</strong>：训练模型的中央存储，带版本控制、阶段（开发 → 预发布 → 生产）和元数据。W&amp;B 和 MLflow 都提供注册表。注册表回答："当前生产环境中的是哪个模型，谁训练的，其验证准确率是多少，以及由哪个代码/数据产生？"</li>
</ul>
<h2 id="_3">可重现性<a class="headerlink" href="#_3" title="Permanent link">&para;</a></h2>
<ul>
<li>
<p>可重现性意味着：给定相同的代码、数据和配置，产生相同的模型。这在机器学习中出奇地困难，因为 GPU 操作的非确定性、数据打乱和浮点数累积。</p>
</li>
<li>
<p><strong>可重现性检查清单</strong>：</p>
</li>
</ul>
<table>
<thead>
<tr>
<th>什么</th>
<th>如何做</th>
</tr>
</thead>
<tbody>
<tr>
<td>代码版本</td>
<td>Git 提交哈希值</td>
</tr>
<tr>
<td>配置 / 超参数</td>
<td>配置文件（在 Git 中版本控制或记录到 W&amp;B）</td>
</tr>
<tr>
<td>随机种子</td>
<td>设置并记录所有种子（Python、NumPy、PyTorch、CUDA）</td>
</tr>
<tr>
<td>数据版本</td>
<td>DVC 哈希值、数据集版本标签或 S3 对象版本</td>
</tr>
<tr>
<td>依赖项</td>
<td><code>pip freeze</code>、Docker 镜像哈希值或锁定文件</td>
</tr>
<tr>
<td>硬件</td>
<td>GPU 类型、GPU 数量、CUDA 版本</td>
</tr>
<tr>
<td>非确定性</td>
<td><code>torch.backends.cudnn.deterministic = True</code>（较慢但可重现）</td>
</tr>
</tbody>
</table>
<ul>
<li>
<p><strong>锁定所有内容</strong>：<code>pip install torch==2.2.1</code> 而不是 <code>torch&gt;=2.0</code>。次版本号升级可能改变数值行为、优化器实现或默认超参数。</p>
</li>
<li>
<p><strong>使用 Docker 实现可重现性</strong>：Docker 镜像锁定了操作系统、系统库、Python 版本和 pip 包。镜像哈希值是完整的环境指纹。如果你能重现 Docker 镜像，就能重现训练。</p>
</li>
</ul>
<h2 id="_4">生产环境监控<a class="headerlink" href="#_4" title="Permanent link">&para;</a></h2>
<ul>
<li>
<p>部署模型不是终点——而是一系列新问题的开始。随着现实世界的变化（<strong>概念漂移</strong>）以及输入数据分布的变化（<strong>数据漂移</strong>），模型会随时间推移而退化。</p>
</li>
<li>
<p><strong>需要监控的内容</strong>：</p>
<ul>
<li>
<p><strong>延迟</strong>：推理需要多长时间？追踪 p50（中位数）、p95 和 p99。p99 为 500ms 意味着每 100 个用户中有 1 个要等待半秒钟，这可能不可接受。</p>
</li>
<li>
<p><strong>吞吐量</strong>：每秒处理多少个请求？系统是否跟得上需求？</p>
</li>
<li>
<p><strong>错误率</strong>：有多少比例的请求失败（异常、超时、无效输入）？</p>
</li>
<li>
<p><strong>模型指标</strong>：在验证集上的准确率、精确率、召回率。如果生产环境中存在标注数据（例如用户纠正），追踪在线指标。</p>
</li>
<li>
<p><strong>数据漂移</strong>：输入数据的分布是否发生了变化？在白天照片上训练的模型可能在夜间照片上失败。统计检验（KS 检验、PSI）将训练分布与在线分布进行比较。</p>
</li>
<li>
<p><strong>特征漂移</strong>：单个特征的分布是否发生了变化？训练时呈正态分布但在生产时呈双峰分布的特征，表明数据管道存在问题。</p>
</li>
</ul>
</li>
<li>
<p><strong>工具</strong>：</p>
<ul>
<li><strong>Prometheus</strong> + <strong>Grafana</strong>：基础设施监控的标准方案。Prometheus 收集指标，Grafana 将其可视化为带告警的仪表板。</li>
<li><strong>Evidently AI</strong>：开源机器学习监控。生成关于数据漂移、模型性能和数据质量的报告。</li>
</ul>
</li>
<li>
<p><strong>告警</strong>：不要只放在仪表板上——设置自动告警。"如果 p99 延迟超过 200ms 持续 5 分钟，发送 Slack 通知。""如果数据漂移评分超过阈值，通知值班工程师。"</p>
</li>
</ul>
<h2 id="_5">特征存储<a class="headerlink" href="#_5" title="Permanent link">&para;</a></h2>
<ul>
<li>
<p><strong>特征存储</strong>是预计算特征的集中式仓库，在训练和推理之间共享。它解决两个问题：</p>
<ul>
<li>
<p><strong>训练-推理偏差</strong>：训练期间使用的特征必须与推理期间使用的特征完全相同。如果训练使用一种方式计算的 <code>user_age_at_signup</code>，而推理使用不同的方式计算，模型的预测结果会静默出错。</p>
</li>
<li>
<p><strong>特征复用</strong>：多个模型通常使用相同的特征（用户人口统计、物品嵌入、聚合统计）。计算一次并共享，避免了重复和不一致性。</p>
</li>
</ul>
</li>
<li>
<p><strong>Feast</strong> 是最流行的开源特征存储。它管理在线特征（低延迟，从 Redis 或 DynamoDB 提供）和离线特征（批处理，存储在数据仓库中用于训练）。</p>
</li>
<li>
<p>特征存储对于推荐系统、欺诈检测以及任何特征从原始数据管道计算而来的应用都至关重要。</p>
</li>
</ul>
<h2 id="_6">管道编排<a class="headerlink" href="#_6" title="Permanent link">&para;</a></h2>
<ul>
<li>
<p>生产级机器学习系统不仅仅是模型。它是一个<strong>管道</strong>：数据采集 → 预处理 → 特征计算 → 训练 → 评估 → 部署 → 监控。每个步骤依赖于前一步骤，可以独立失败，可能需要在不同的时间表上运行。</p>
</li>
<li>
<p><strong>编排器</strong>管理这些管道：</p>
</li>
<li>
<p><strong>Apache Airflow</strong>：数据管道编排的标准方案。DAG（有向无环图）定义任务依赖关系。每个任务独立运行，失败时可以重试，并通过 Web UI 进行监控。</p>
</li>
</ul>
<div class="highlight"><pre><span></span><code><a id="__codelineno-5-1" name="__codelineno-5-1" href="#__codelineno-5-1"></a><span class="c1"># airflow DAG 示例（简化）</span>
<a id="__codelineno-5-2" name="__codelineno-5-2" href="#__codelineno-5-2"></a><span class="kn">from</span><span class="w"> </span><span class="nn">airflow</span><span class="w"> </span><span class="kn">import</span> <span class="n">DAG</span>
<a id="__codelineno-5-3" name="__codelineno-5-3" href="#__codelineno-5-3"></a><span class="kn">from</span><span class="w"> </span><span class="nn">airflow.operators.python</span><span class="w"> </span><span class="kn">import</span> <span class="n">PythonOperator</span>
<a id="__codelineno-5-4" name="__codelineno-5-4" href="#__codelineno-5-4"></a>
<a id="__codelineno-5-5" name="__codelineno-5-5" href="#__codelineno-5-5"></a><span class="n">dag</span> <span class="o">=</span> <span class="n">DAG</span><span class="p">(</span><span class="s2">&quot;training_pipeline&quot;</span><span class="p">,</span> <span class="n">schedule</span><span class="o">=</span><span class="s2">&quot;@daily&quot;</span><span class="p">)</span>
<a id="__codelineno-5-6" name="__codelineno-5-6" href="#__codelineno-5-6"></a>
<a id="__codelineno-5-7" name="__codelineno-5-7" href="#__codelineno-5-7"></a><span class="n">preprocess</span> <span class="o">=</span> <span class="n">PythonOperator</span><span class="p">(</span><span class="n">task_id</span><span class="o">=</span><span class="s2">&quot;preprocess&quot;</span><span class="p">,</span> <span class="n">python_callable</span><span class="o">=</span><span class="n">preprocess_data</span><span class="p">,</span> <span class="n">dag</span><span class="o">=</span><span class="n">dag</span><span class="p">)</span>
<a id="__codelineno-5-8" name="__codelineno-5-8" href="#__codelineno-5-8"></a><span class="n">train</span> <span class="o">=</span> <span class="n">PythonOperator</span><span class="p">(</span><span class="n">task_id</span><span class="o">=</span><span class="s2">&quot;train&quot;</span><span class="p">,</span> <span class="n">python_callable</span><span class="o">=</span><span class="n">train_model</span><span class="p">,</span> <span class="n">dag</span><span class="o">=</span><span class="n">dag</span><span class="p">)</span>
<a id="__codelineno-5-9" name="__codelineno-5-9" href="#__codelineno-5-9"></a><span class="n">evaluate</span> <span class="o">=</span> <span class="n">PythonOperator</span><span class="p">(</span><span class="n">task_id</span><span class="o">=</span><span class="s2">&quot;evaluate&quot;</span><span class="p">,</span> <span class="n">python_callable</span><span class="o">=</span><span class="n">evaluate_model</span><span class="p">,</span> <span class="n">dag</span><span class="o">=</span><span class="n">dag</span><span class="p">)</span>
<a id="__codelineno-5-10" name="__codelineno-5-10" href="#__codelineno-5-10"></a><span class="n">deploy</span> <span class="o">=</span> <span class="n">PythonOperator</span><span class="p">(</span><span class="n">task_id</span><span class="o">=</span><span class="s2">&quot;deploy&quot;</span><span class="p">,</span> <span class="n">python_callable</span><span class="o">=</span><span class="n">deploy_model</span><span class="p">,</span> <span class="n">dag</span><span class="o">=</span><span class="n">dag</span><span class="p">)</span>
<a id="__codelineno-5-11" name="__codelineno-5-11" href="#__codelineno-5-11"></a>
<a id="__codelineno-5-12" name="__codelineno-5-12" href="#__codelineno-5-12"></a><span class="n">preprocess</span> <span class="o">&gt;&gt;</span> <span class="n">train</span> <span class="o">&gt;&gt;</span> <span class="n">evaluate</span> <span class="o">&gt;&gt;</span> <span class="n">deploy</span>
</code></pre></div>
<ul>
<li>
<p><strong>Kubeflow Pipelines</strong>：在 Kubernetes 上运行机器学习特定编排。每个步骤在容器中运行，GPU 资源按需分配，实验自动追踪。</p>
</li>
<li>
<p><strong>Prefect</strong> 和 <strong>Dagster</strong>：Airflow 的现代替代方案，拥有更好的开发者体验、原生 Python API 和内置数据血缘追踪。</p>
</li>
<li>
<p><strong>何时需要编排</strong>：当你的管道有超过 2-3 个步骤、按计划运行、涉及多个团队或服务、或需要自动故障恢复时。单一脚本的训练任务不需要编排器。每天重新训练的管道——从 5 个数据源采集数据、训练 3 个模型、评估它们并部署最佳模型——绝对需要。</p>
</li>
</ul>
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