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概率论
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机器学习
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计算语言学
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音频与语音
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多模态学习
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自主系统
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统计学
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抽样
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推断
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概率论
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计数
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</a>
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概率概念
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分布
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</span>
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</a>
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贝叶斯
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</span>
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</a>
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信息论
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</span>
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机器学习
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</span>
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<span class="md-nav__icon md-icon"></span>
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机器学习
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经典机器学习
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梯度机器学习
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</span>
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</a>
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深度学习
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</span>
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</a>
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强化学习
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</span>
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</a>
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分布式深度学习
|
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</span>
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|
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|
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|
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</a>
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计算语言学
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<span class="md-nav__icon md-icon"></span>
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计算语言学
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语言学基础
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</a>
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嵌入与序列模型
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</span>
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</a>
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Transformer 与语言模型
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</span>
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|
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</a>
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<span class="md-ellipsis">
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高级文本生成
|
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|
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|
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|
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</span>
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|
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|
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|
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</a>
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计算机视觉
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计算机视觉
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图像基础
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</span>
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</a>
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目标检测与分割
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多模态学习
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多模态学习
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多模态表征
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</span>
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</a>
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统一多模态架构
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</span>
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自主系统
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感知
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视觉-语言-动作模型
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自动驾驶
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</a>
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太空与极端机器人
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</span>
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图神经网络
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3D 图网络
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计算机与操作系统
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离散数学
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并发与并行
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数据结构与算法
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基础
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</a>
|
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|
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|
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</a>
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树
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|
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</a>
|
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排序与搜索
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|
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生产级软件工程
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代码设计
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API 设计
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<li class="md-nav__item">
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<a href="#ai" class="md-nav__link">
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<span class="md-ellipsis">
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使用 AI 编码助手
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|
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</a>
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<nav class="md-nav" aria-label="使用 AI 编码助手">
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AI 助手擅长之处
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何时不应依赖 AI 助手
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<li class="md-nav__item">
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审查纪律
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|
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<li class="md-nav__item">
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<a href="#_9" class="md-nav__link">
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如何编写好的提示词
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</span>
|
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</a>
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|
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</li>
|
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|
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<li class="md-nav__item">
|
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<a href="#ai_3" class="md-nav__link">
|
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<span class="md-ellipsis">
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使用质量门控来捕捉 AI 助手的错误
|
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|
||
</span>
|
||
</a>
|
||
|
||
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测试与质量保障
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Triton、TPU 与 Pallas
|
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|
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|
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</span>
|
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|
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|
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|
||
</a>
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RISC-V 与嵌入式系统
|
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|
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|
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|
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</span>
|
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|
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|
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|
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</a>
|
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Vulkan Compute 与跨平台 GPU
|
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|
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|
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</span>
|
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|
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|
||
|
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AI 推理
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AI 推理
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量化
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</span>
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</a>
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高效架构
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</a>
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服务与批处理
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</a>
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边缘推理
|
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</span>
|
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|
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|
||
|
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</a>
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|
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<span class="md-ellipsis">
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扩缩与部署
|
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|
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|
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|
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</span>
|
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|
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|
||
|
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ML 系统设计
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云计算
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</a>
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大规模基础设施
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|
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|
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</a>
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</span>
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|
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|
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|
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</a>
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ML 设计案例
|
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|
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|
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|
||
</span>
|
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|
||
|
||
|
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应用 AI
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AI 金融
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</span>
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|
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|
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|
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</a>
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医疗健康
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前沿 AI
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量子机器学习
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神经形态计算
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太空数据中心
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去中心化 AI
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脑机接口
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适用于机器学习的设范计式
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打包与分发
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AI 助手擅长之处
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<a href="#ai_2" class="md-nav__link">
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|
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</a>
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<li class="md-nav__item">
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审查纪律
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|
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<li class="md-nav__item">
|
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<a href="#_9" class="md-nav__link">
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<span class="md-ellipsis">
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如何编写好的提示词
|
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</span>
|
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</a>
|
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|
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</li>
|
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|
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<li class="md-nav__item">
|
||
<a href="#ai_3" class="md-nav__link">
|
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<span class="md-ellipsis">
|
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|
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使用质量门控来捕捉 AI 助手的错误
|
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|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
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|
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<li class="md-nav__item">
|
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<a href="#_10" class="md-nav__link">
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<span class="md-ellipsis">
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生产力陷阱
|
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|
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</span>
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|
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|
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<article class="md-content__inner md-typeset">
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|
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|
||
|
||
|
||
|
||
<h1 id="_1">代码库设计与模式<a class="headerlink" href="#_1" title="Permanent link">¶</a></h1>
|
||
<p><em>良好的代码库设计是区分研究原型与生产级软件的关键。本文涵盖项目结构、整洁代码原则、与机器学习相关的设计模式、配置管理、日志、API 设计以及打包分发。</em></p>
|
||
<ul>
|
||
<li>
|
||
<p>大多数机器学习代码始于 Jupyter notebook。Notebook 不断增长、被复制、修改、共享，最终变成由全局变量、死单元格和魔数组成的难以维护的混乱。<strong>代码库设计</strong>是一门组织代码的学科，使代码在项目增长过程中保持可理解和可修改。</p>
|
||
</li>
|
||
<li>
|
||
<p>这不是为了遵循规则而遵循规则。而是为了减少从"我想改变 X"到"X 已被修改并能正常工作"之间的时间。在精心设计的代码库中，这个时间是几分钟。在设计糟糕的代码库中，则需要几天的时间去考古、翻阅未记录的意大利面条式代码。</p>
|
||
</li>
|
||
</ul>
|
||
<h2 id="_2">项目结构<a class="headerlink" href="#_2" title="Permanent link">¶</a></h2>
|
||
<ul>
|
||
<li>一致的项目布局让任何人（包括未来的你）都能立即浏览代码库。</li>
|
||
</ul>
|
||
<div class="highlight"><pre><span></span><code><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a>my_project/
|
||
<a id="__codelineno-0-2" name="__codelineno-0-2" href="#__codelineno-0-2"></a>├── src/my_project/ # 源代码（可导入的包）
|
||
<a id="__codelineno-0-3" name="__codelineno-0-3" href="#__codelineno-0-3"></a>│ ├── __init__.py
|
||
<a id="__codelineno-0-4" name="__codelineno-0-4" href="#__codelineno-0-4"></a>│ ├── data/ # 数据加载和预处理
|
||
<a id="__codelineno-0-5" name="__codelineno-0-5" href="#__codelineno-0-5"></a>│ │ ├── __init__.py
|
||
<a id="__codelineno-0-6" name="__codelineno-0-6" href="#__codelineno-0-6"></a>│ │ ├── dataset.py
|
||
<a id="__codelineno-0-7" name="__codelineno-0-7" href="#__codelineno-0-7"></a>│ │ └── transforms.py
|
||
<a id="__codelineno-0-8" name="__codelineno-0-8" href="#__codelineno-0-8"></a>│ ├── models/ # 模型架构
|
||
<a id="__codelineno-0-9" name="__codelineno-0-9" href="#__codelineno-0-9"></a>│ │ ├── __init__.py
|
||
<a id="__codelineno-0-10" name="__codelineno-0-10" href="#__codelineno-0-10"></a>│ │ ├── transformer.py
|
||
<a id="__codelineno-0-11" name="__codelineno-0-11" href="#__codelineno-0-11"></a>│ │ └── layers.py
|
||
<a id="__codelineno-0-12" name="__codelineno-0-12" href="#__codelineno-0-12"></a>│ ├── training/ # 训练循环、优化器
|
||
<a id="__codelineno-0-13" name="__codelineno-0-13" href="#__codelineno-0-13"></a>│ │ ├── __init__.py
|
||
<a id="__codelineno-0-14" name="__codelineno-0-14" href="#__codelineno-0-14"></a>│ │ ├── trainer.py
|
||
<a id="__codelineno-0-15" name="__codelineno-0-15" href="#__codelineno-0-15"></a>│ │ └── losses.py
|
||
<a id="__codelineno-0-16" name="__codelineno-0-16" href="#__codelineno-0-16"></a>│ └── utils/ # 共享工具
|
||
<a id="__codelineno-0-17" name="__codelineno-0-17" href="#__codelineno-0-17"></a>│ ├── __init__.py
|
||
<a id="__codelineno-0-18" name="__codelineno-0-18" href="#__codelineno-0-18"></a>│ └── logging.py
|
||
<a id="__codelineno-0-19" name="__codelineno-0-19" href="#__codelineno-0-19"></a>├── configs/ # 配置文件
|
||
<a id="__codelineno-0-20" name="__codelineno-0-20" href="#__codelineno-0-20"></a>│ ├── base.yaml
|
||
<a id="__codelineno-0-21" name="__codelineno-0-21" href="#__codelineno-0-21"></a>│ └── experiment_1.yaml
|
||
<a id="__codelineno-0-22" name="__codelineno-0-22" href="#__codelineno-0-22"></a>├── scripts/ # 入口点（训练、评估、推理）
|
||
<a id="__codelineno-0-23" name="__codelineno-0-23" href="#__codelineno-0-23"></a>│ ├── train.py
|
||
<a id="__codelineno-0-24" name="__codelineno-0-24" href="#__codelineno-0-24"></a>│ ├── evaluate.py
|
||
<a id="__codelineno-0-25" name="__codelineno-0-25" href="#__codelineno-0-25"></a>│ └── serve.py
|
||
<a id="__codelineno-0-26" name="__codelineno-0-26" href="#__codelineno-0-26"></a>├── tests/ # 测试文件（镜像 src/ 结构）
|
||
<a id="__codelineno-0-27" name="__codelineno-0-27" href="#__codelineno-0-27"></a>│ ├── test_dataset.py
|
||
<a id="__codelineno-0-28" name="__codelineno-0-28" href="#__codelineno-0-28"></a>│ ├── test_model.py
|
||
<a id="__codelineno-0-29" name="__codelineno-0-29" href="#__codelineno-0-29"></a>│ └── test_trainer.py
|
||
<a id="__codelineno-0-30" name="__codelineno-0-30" href="#__codelineno-0-30"></a>├── notebooks/ # 仅用于探索（非生产代码）
|
||
<a id="__codelineno-0-31" name="__codelineno-0-31" href="#__codelineno-0-31"></a>├── pyproject.toml # 项目元数据和依赖
|
||
<a id="__codelineno-0-32" name="__codelineno-0-32" href="#__codelineno-0-32"></a>├── README.md
|
||
<a id="__codelineno-0-33" name="__codelineno-0-33" href="#__codelineno-0-33"></a>├── .gitignore
|
||
<a id="__codelineno-0-34" name="__codelineno-0-34" href="#__codelineno-0-34"></a>└── Dockerfile
|
||
</code></pre></div>
|
||
<ul>
|
||
<li>
|
||
<p><strong><code>src/</code> 布局</strong>：将源代码放在 <code>src/my_project/</code> 下可以防止从当前目录意外导入（这会掩盖在生产环境中才会暴露的导入错误）。使用 <code>pip install -e .</code> 进行开发安装。</p>
|
||
</li>
|
||
<li>
|
||
<p><strong>单仓库 vs 多仓库</strong>：<strong>单仓库</strong>将所有相关项目放在一个仓库中（跨项目更改更容易、CI 共享）。<strong>多仓库</strong>给每个项目自己的仓库（边界更清晰、版本控制独立）。大多数机器学习团队从单仓库开始，必要时再拆分。</p>
|
||
</li>
|
||
<li>
|
||
<p><strong>脚本 vs 库</strong>：将入口点（<code>train.py</code>、<code>evaluate.py</code>）保留在 <code>scripts/</code> 中。将可复用的逻辑放在 <code>src/</code> 中。训练脚本应约为 50 行：解析配置、构建数据集、构建模型、构建训练器、训练。所有复杂性都在库中。</p>
|
||
</li>
|
||
</ul>
|
||
<h2 id="_3">整洁代码原则<a class="headerlink" href="#_3" title="Permanent link">¶</a></h2>
|
||
<ul>
|
||
<li><strong>命名</strong>：你能做的唯一最有影响力的事情。名为 <code>x</code> 的变量需要你阅读周围的代码才能理解。名为 <code>learning_rate</code> 的变量是自解释的。</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"># 糟糕</span>
|
||
<a id="__codelineno-1-2" name="__codelineno-1-2" href="#__codelineno-1-2"></a><span class="k">def</span><span class="w"> </span><span class="nf">proc</span><span class="p">(</span><span class="n">d</span><span class="p">,</span> <span class="n">n</span><span class="p">,</span> <span class="n">lr</span><span class="p">):</span>
|
||
<a id="__codelineno-1-3" name="__codelineno-1-3" href="#__codelineno-1-3"></a> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n</span><span class="p">):</span>
|
||
<a id="__codelineno-1-4" name="__codelineno-1-4" href="#__codelineno-1-4"></a> <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">d</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
|
||
<a id="__codelineno-1-5" name="__codelineno-1-5" href="#__codelineno-1-5"></a> <span class="n">v</span> <span class="o">-=</span> <span class="n">lr</span> <span class="o">*</span> <span class="n">g</span><span class="p">[</span><span class="n">k</span><span class="p">]</span>
|
||
<a id="__codelineno-1-6" name="__codelineno-1-6" href="#__codelineno-1-6"></a>
|
||
<a id="__codelineno-1-7" name="__codelineno-1-7" href="#__codelineno-1-7"></a><span class="c1"># 良好</span>
|
||
<a id="__codelineno-1-8" name="__codelineno-1-8" href="#__codelineno-1-8"></a><span class="k">def</span><span class="w"> </span><span class="nf">update_parameters</span><span class="p">(</span><span class="n">parameters</span><span class="p">,</span> <span class="n">num_steps</span><span class="p">,</span> <span class="n">learning_rate</span><span class="p">):</span>
|
||
<a id="__codelineno-1-9" name="__codelineno-1-9" href="#__codelineno-1-9"></a> <span class="k">for</span> <span class="n">step</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_steps</span><span class="p">):</span>
|
||
<a id="__codelineno-1-10" name="__codelineno-1-10" href="#__codelineno-1-10"></a> <span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">param</span> <span class="ow">in</span> <span class="n">parameters</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
|
||
<a id="__codelineno-1-11" name="__codelineno-1-11" href="#__codelineno-1-11"></a> <span class="n">param</span> <span class="o">-=</span> <span class="n">learning_rate</span> <span class="o">*</span> <span class="n">gradients</span><span class="p">[</span><span class="n">name</span><span class="p">]</span>
|
||
</code></pre></div>
|
||
<ul>
|
||
<li>
|
||
<p><strong>单一职责原则</strong>：每个函数/类只做一件事。名为 <code>load_data_and_train_model</code> 的函数在做两件事，应该拆分。这使每个部分都可以独立测试、复用和理解。</p>
|
||
</li>
|
||
<li>
|
||
<p><strong>DRY（不要重复自己）</strong>——但不要过早抽象。如果你复制粘贴代码三次，将其提取为一个函数。但不要为只使用过一次的代码创建抽象。过早的抽象比重复更糟糕：它增加了复杂性但没有经过验证的好处。</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"># 过早抽象（一个用例，过度设计）</span>
|
||
<a id="__codelineno-2-2" name="__codelineno-2-2" href="#__codelineno-2-2"></a><span class="k">class</span><span class="w"> </span><span class="nc">AbstractDataTransformPipelineFactory</span><span class="p">:</span>
|
||
<a id="__codelineno-2-3" name="__codelineno-2-3" href="#__codelineno-2-3"></a> <span class="o">...</span>
|
||
<a id="__codelineno-2-4" name="__codelineno-2-4" href="#__codelineno-2-4"></a>
|
||
<a id="__codelineno-2-5" name="__codelineno-2-5" href="#__codelineno-2-5"></a><span class="c1"># 恰到好处（直接、清晰、在三处使用）</span>
|
||
<a id="__codelineno-2-6" name="__codelineno-2-6" href="#__codelineno-2-6"></a><span class="k">def</span><span class="w"> </span><span class="nf">normalise_image</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="n">mean</span><span class="p">,</span> <span class="n">std</span><span class="p">):</span>
|
||
<a id="__codelineno-2-7" name="__codelineno-2-7" href="#__codelineno-2-7"></a> <span class="k">return</span> <span class="p">(</span><span class="n">image</span> <span class="o">-</span> <span class="n">mean</span><span class="p">)</span> <span class="o">/</span> <span class="n">std</span>
|
||
</code></pre></div>
|
||
<ul>
|
||
<li><strong>魔数</strong>：永远不要使用未解释的字面值。</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="c1"># 糟糕</span>
|
||
<a id="__codelineno-3-2" name="__codelineno-3-2" href="#__codelineno-3-2"></a><span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">batch</span><span class="p">)</span> <span class="o">></span> <span class="mi">32</span><span class="p">:</span>
|
||
<a id="__codelineno-3-3" name="__codelineno-3-3" href="#__codelineno-3-3"></a> <span class="n">split_batch</span><span class="p">(</span><span class="n">batch</span><span class="p">,</span> <span class="mi">32</span><span class="p">)</span>
|
||
<a id="__codelineno-3-4" name="__codelineno-3-4" href="#__codelineno-3-4"></a>
|
||
<a id="__codelineno-3-5" name="__codelineno-3-5" href="#__codelineno-3-5"></a><span class="c1"># 良好</span>
|
||
<a id="__codelineno-3-6" name="__codelineno-3-6" href="#__codelineno-3-6"></a><span class="n">MAX_BATCH_SIZE</span> <span class="o">=</span> <span class="mi">32</span>
|
||
<a id="__codelineno-3-7" name="__codelineno-3-7" href="#__codelineno-3-7"></a><span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">batch</span><span class="p">)</span> <span class="o">></span> <span class="n">MAX_BATCH_SIZE</span><span class="p">:</span>
|
||
<a id="__codelineno-3-8" name="__codelineno-3-8" href="#__codelineno-3-8"></a> <span class="n">split_batch</span><span class="p">(</span><span class="n">batch</span><span class="p">,</span> <span class="n">MAX_BATCH_SIZE</span><span class="p">)</span>
|
||
</code></pre></div>
|
||
<ul>
|
||
<li><strong>函数应该简短</strong>：如果一个函数不能在一屏内显示完整（约 30 行），那它可能做得太多了。将逻辑块提取为带有描述性名称的辅助函数。然后函数体读起来就像高级摘要。</li>
|
||
</ul>
|
||
<h2 id="_4">适用于机器学习的设范计式<a class="headerlink" href="#_4" title="Permanent link">¶</a></h2>
|
||
<ul>
|
||
<li>
|
||
<p>设计模式是针对常见问题的可复用解决方案。以下是与机器学习代码库最相关的模式：</p>
|
||
</li>
|
||
<li>
|
||
<p><strong>工厂模式</strong>：在不指定确切类的情况下创建对象。当你的配置说 <code>model: "transformer"</code> 并且你需要实例化正确的类时很有用：</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="n">MODEL_REGISTRY</span> <span class="o">=</span> <span class="p">{</span>
|
||
<a id="__codelineno-4-2" name="__codelineno-4-2" href="#__codelineno-4-2"></a> <span class="s2">"transformer"</span><span class="p">:</span> <span class="n">TransformerModel</span><span class="p">,</span>
|
||
<a id="__codelineno-4-3" name="__codelineno-4-3" href="#__codelineno-4-3"></a> <span class="s2">"cnn"</span><span class="p">:</span> <span class="n">CNNModel</span><span class="p">,</span>
|
||
<a id="__codelineno-4-4" name="__codelineno-4-4" href="#__codelineno-4-4"></a> <span class="s2">"mlp"</span><span class="p">:</span> <span class="n">MLPModel</span><span class="p">,</span>
|
||
<a id="__codelineno-4-5" name="__codelineno-4-5" href="#__codelineno-4-5"></a><span class="p">}</span>
|
||
<a id="__codelineno-4-6" name="__codelineno-4-6" href="#__codelineno-4-6"></a>
|
||
<a id="__codelineno-4-7" name="__codelineno-4-7" href="#__codelineno-4-7"></a><span class="k">def</span><span class="w"> </span><span class="nf">build_model</span><span class="p">(</span><span class="n">config</span><span class="p">):</span>
|
||
<a id="__codelineno-4-8" name="__codelineno-4-8" href="#__codelineno-4-8"></a> <span class="n">model_cls</span> <span class="o">=</span> <span class="n">MODEL_REGISTRY</span><span class="p">[</span><span class="n">config</span><span class="p">[</span><span class="s2">"model"</span><span class="p">]]</span>
|
||
<a id="__codelineno-4-9" name="__codelineno-4-9" href="#__codelineno-4-9"></a> <span class="k">return</span> <span class="n">model_cls</span><span class="p">(</span><span class="o">**</span><span class="n">config</span><span class="p">[</span><span class="s2">"model_params"</span><span class="p">])</span>
|
||
</code></pre></div>
|
||
<ul>
|
||
<li>
|
||
<p>这使训练脚本与特定的模型实现解耦。添加新模型意味着在注册表中添加一行，而不是修改训练循环。</p>
|
||
</li>
|
||
<li>
|
||
<p><strong>策略模式</strong>：在运行时交换算法。适用于损失函数、优化器、调度器：</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="n">LOSS_FUNCTIONS</span> <span class="o">=</span> <span class="p">{</span>
|
||
<a id="__codelineno-5-2" name="__codelineno-5-2" href="#__codelineno-5-2"></a> <span class="s2">"mse"</span><span class="p">:</span> <span class="n">nn</span><span class="o">.</span><span class="n">MSELoss</span><span class="p">,</span>
|
||
<a id="__codelineno-5-3" name="__codelineno-5-3" href="#__codelineno-5-3"></a> <span class="s2">"cross_entropy"</span><span class="p">:</span> <span class="n">nn</span><span class="o">.</span><span class="n">CrossEntropyLoss</span><span class="p">,</span>
|
||
<a id="__codelineno-5-4" name="__codelineno-5-4" href="#__codelineno-5-4"></a> <span class="s2">"focal"</span><span class="p">:</span> <span class="n">FocalLoss</span><span class="p">,</span>
|
||
<a id="__codelineno-5-5" name="__codelineno-5-5" href="#__codelineno-5-5"></a><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">loss_fn</span> <span class="o">=</span> <span class="n">LOSS_FUNCTIONS</span><span class="p">[</span><span class="n">config</span><span class="p">[</span><span class="s2">"loss"</span><span class="p">]]()</span>
|
||
</code></pre></div>
|
||
<ul>
|
||
<li><strong>观察者模式</strong>（回调/钩子）：让模块响应事件而不紧密耦合。训练框架（PyTorch Lightning、Keras）广泛使用回调：</li>
|
||
</ul>
|
||
<div class="highlight"><pre><span></span><code><a id="__codelineno-6-1" name="__codelineno-6-1" href="#__codelineno-6-1"></a><span class="k">class</span><span class="w"> </span><span class="nc">EarlyStopping</span><span class="p">:</span>
|
||
<a id="__codelineno-6-2" name="__codelineno-6-2" href="#__codelineno-6-2"></a> <span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">patience</span><span class="o">=</span><span class="mi">5</span><span class="p">):</span>
|
||
<a id="__codelineno-6-3" name="__codelineno-6-3" href="#__codelineno-6-3"></a> <span class="bp">self</span><span class="o">.</span><span class="n">patience</span> <span class="o">=</span> <span class="n">patience</span>
|
||
<a id="__codelineno-6-4" name="__codelineno-6-4" href="#__codelineno-6-4"></a> <span class="bp">self</span><span class="o">.</span><span class="n">best_loss</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="s1">'inf'</span><span class="p">)</span>
|
||
<a id="__codelineno-6-5" name="__codelineno-6-5" href="#__codelineno-6-5"></a> <span class="bp">self</span><span class="o">.</span><span class="n">counter</span> <span class="o">=</span> <span class="mi">0</span>
|
||
<a id="__codelineno-6-6" name="__codelineno-6-6" href="#__codelineno-6-6"></a>
|
||
<a id="__codelineno-6-7" name="__codelineno-6-7" href="#__codelineno-6-7"></a> <span class="k">def</span><span class="w"> </span><span class="nf">on_epoch_end</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">epoch</span><span class="p">,</span> <span class="n">val_loss</span><span class="p">):</span>
|
||
<a id="__codelineno-6-8" name="__codelineno-6-8" href="#__codelineno-6-8"></a> <span class="k">if</span> <span class="n">val_loss</span> <span class="o"><</span> <span class="bp">self</span><span class="o">.</span><span class="n">best_loss</span><span class="p">:</span>
|
||
<a id="__codelineno-6-9" name="__codelineno-6-9" href="#__codelineno-6-9"></a> <span class="bp">self</span><span class="o">.</span><span class="n">best_loss</span> <span class="o">=</span> <span class="n">val_loss</span>
|
||
<a id="__codelineno-6-10" name="__codelineno-6-10" href="#__codelineno-6-10"></a> <span class="bp">self</span><span class="o">.</span><span class="n">counter</span> <span class="o">=</span> <span class="mi">0</span>
|
||
<a id="__codelineno-6-11" name="__codelineno-6-11" href="#__codelineno-6-11"></a> <span class="k">else</span><span class="p">:</span>
|
||
<a id="__codelineno-6-12" name="__codelineno-6-12" href="#__codelineno-6-12"></a> <span class="bp">self</span><span class="o">.</span><span class="n">counter</span> <span class="o">+=</span> <span class="mi">1</span>
|
||
<a id="__codelineno-6-13" name="__codelineno-6-13" href="#__codelineno-6-13"></a> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">counter</span> <span class="o">>=</span> <span class="bp">self</span><span class="o">.</span><span class="n">patience</span><span class="p">:</span>
|
||
<a id="__codelineno-6-14" name="__codelineno-6-14" href="#__codelineno-6-14"></a> <span class="k">return</span> <span class="s2">"stop"</span>
|
||
</code></pre></div>
|
||
<ul>
|
||
<li><strong>依赖注入</strong>：将依赖项传入函数/类，而不是在内部创建。这使得测试变得容易（注入 mock）并且配置灵活：</li>
|
||
</ul>
|
||
<div class="highlight"><pre><span></span><code><a id="__codelineno-7-1" name="__codelineno-7-1" href="#__codelineno-7-1"></a><span class="c1"># 糟糕：硬编码依赖</span>
|
||
<a id="__codelineno-7-2" name="__codelineno-7-2" href="#__codelineno-7-2"></a><span class="k">class</span><span class="w"> </span><span class="nc">Trainer</span><span class="p">:</span>
|
||
<a id="__codelineno-7-3" name="__codelineno-7-3" href="#__codelineno-7-3"></a> <span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||
<a id="__codelineno-7-4" name="__codelineno-7-4" href="#__codelineno-7-4"></a> <span class="bp">self</span><span class="o">.</span><span class="n">logger</span> <span class="o">=</span> <span class="n">WandbLogger</span><span class="p">()</span> <span class="c1"># 没有 W&B 就无法测试</span>
|
||
<a id="__codelineno-7-5" name="__codelineno-7-5" href="#__codelineno-7-5"></a>
|
||
<a id="__codelineno-7-6" name="__codelineno-7-6" href="#__codelineno-7-6"></a><span class="c1"># 良好：注入依赖</span>
|
||
<a id="__codelineno-7-7" name="__codelineno-7-7" href="#__codelineno-7-7"></a><span class="k">class</span><span class="w"> </span><span class="nc">Trainer</span><span class="p">:</span>
|
||
<a id="__codelineno-7-8" name="__codelineno-7-8" href="#__codelineno-7-8"></a> <span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">logger</span><span class="p">):</span>
|
||
<a id="__codelineno-7-9" name="__codelineno-7-9" href="#__codelineno-7-9"></a> <span class="bp">self</span><span class="o">.</span><span class="n">logger</span> <span class="o">=</span> <span class="n">logger</span> <span class="c1"># 可以注入任何记录器，包括 mock</span>
|
||
</code></pre></div>
|
||
<h2 id="_5">配置管理<a class="headerlink" href="#_5" title="Permanent link">¶</a></h2>
|
||
<ul>
|
||
<li>
|
||
<p>硬编码超参数、文件路径和模型设置使实验无法重现，修改也很痛苦。<strong>将配置外部化</strong>到文件中。</p>
|
||
</li>
|
||
<li>
|
||
<p><strong>YAML</strong> 是机器学习配置最常见的格式：</p>
|
||
</li>
|
||
</ul>
|
||
<div class="highlight"><pre><span></span><code><a id="__codelineno-8-1" name="__codelineno-8-1" href="#__codelineno-8-1"></a><span class="c1"># configs/experiment_1.yaml</span>
|
||
<a id="__codelineno-8-2" name="__codelineno-8-2" href="#__codelineno-8-2"></a><span class="nt">model</span><span class="p">:</span>
|
||
<a id="__codelineno-8-3" name="__codelineno-8-3" href="#__codelineno-8-3"></a><span class="w"> </span><span class="nt">name</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">transformer</span>
|
||
<a id="__codelineno-8-4" name="__codelineno-8-4" href="#__codelineno-8-4"></a><span class="w"> </span><span class="nt">d_model</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">512</span>
|
||
<a id="__codelineno-8-5" name="__codelineno-8-5" href="#__codelineno-8-5"></a><span class="w"> </span><span class="nt">n_heads</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">8</span>
|
||
<a id="__codelineno-8-6" name="__codelineno-8-6" href="#__codelineno-8-6"></a><span class="w"> </span><span class="nt">n_layers</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">6</span>
|
||
<a id="__codelineno-8-7" name="__codelineno-8-7" href="#__codelineno-8-7"></a>
|
||
<a id="__codelineno-8-8" name="__codelineno-8-8" href="#__codelineno-8-8"></a><span class="nt">training</span><span class="p">:</span>
|
||
<a id="__codelineno-8-9" name="__codelineno-8-9" href="#__codelineno-8-9"></a><span class="w"> </span><span class="nt">batch_size</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">64</span>
|
||
<a id="__codelineno-8-10" name="__codelineno-8-10" href="#__codelineno-8-10"></a><span class="w"> </span><span class="nt">learning_rate</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">3e-4</span>
|
||
<a id="__codelineno-8-11" name="__codelineno-8-11" href="#__codelineno-8-11"></a><span class="w"> </span><span class="nt">max_epochs</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">100</span>
|
||
<a id="__codelineno-8-12" name="__codelineno-8-12" href="#__codelineno-8-12"></a><span class="w"> </span><span class="nt">early_stopping_patience</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">10</span>
|
||
<a id="__codelineno-8-13" name="__codelineno-8-13" href="#__codelineno-8-13"></a>
|
||
<a id="__codelineno-8-14" name="__codelineno-8-14" href="#__codelineno-8-14"></a><span class="nt">data</span><span class="p">:</span>
|
||
<a id="__codelineno-8-15" name="__codelineno-8-15" href="#__codelineno-8-15"></a><span class="w"> </span><span class="nt">train_path</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">/data/train.parquet</span>
|
||
<a id="__codelineno-8-16" name="__codelineno-8-16" href="#__codelineno-8-16"></a><span class="w"> </span><span class="nt">val_path</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">/data/val.parquet</span>
|
||
<a id="__codelineno-8-17" name="__codelineno-8-17" href="#__codelineno-8-17"></a><span class="w"> </span><span class="nt">max_seq_length</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">512</span>
|
||
</code></pre></div>
|
||
<ul>
|
||
<li>
|
||
<p><strong>Hydra</strong>（Facebook）是一个支持组合（将基础配置与实验特定覆盖合并）、命令行覆盖（<code>python train.py training.lr=1e-3</code>）和多运行（超参数扫描）的配置框架。</p>
|
||
</li>
|
||
<li>
|
||
<p><strong>argparse</strong> 适用于参数较少的脚本：</p>
|
||
</li>
|
||
</ul>
|
||
<div class="highlight"><pre><span></span><code><a id="__codelineno-9-1" name="__codelineno-9-1" href="#__codelineno-9-1"></a><span class="kn">import</span><span class="w"> </span><span class="nn">argparse</span>
|
||
<a id="__codelineno-9-2" name="__codelineno-9-2" href="#__codelineno-9-2"></a>
|
||
<a id="__codelineno-9-3" name="__codelineno-9-3" href="#__codelineno-9-3"></a><span class="n">parser</span> <span class="o">=</span> <span class="n">argparse</span><span class="o">.</span><span class="n">ArgumentParser</span><span class="p">()</span>
|
||
<a id="__codelineno-9-4" name="__codelineno-9-4" href="#__codelineno-9-4"></a><span class="n">parser</span><span class="o">.</span><span class="n">add_argument</span><span class="p">(</span><span class="s2">"--lr"</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="nb">float</span><span class="p">,</span> <span class="n">default</span><span class="o">=</span><span class="mf">3e-4</span><span class="p">)</span>
|
||
<a id="__codelineno-9-5" name="__codelineno-9-5" href="#__codelineno-9-5"></a><span class="n">parser</span><span class="o">.</span><span class="n">add_argument</span><span class="p">(</span><span class="s2">"--batch-size"</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="nb">int</span><span class="p">,</span> <span class="n">default</span><span class="o">=</span><span class="mi">64</span><span class="p">)</span>
|
||
<a id="__codelineno-9-6" name="__codelineno-9-6" href="#__codelineno-9-6"></a><span class="n">parser</span><span class="o">.</span><span class="n">add_argument</span><span class="p">(</span><span class="s2">"--config"</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="nb">str</span><span class="p">,</span> <span class="n">default</span><span class="o">=</span><span class="s2">"configs/base.yaml"</span><span class="p">)</span>
|
||
<a id="__codelineno-9-7" name="__codelineno-9-7" href="#__codelineno-9-7"></a><span class="n">args</span> <span class="o">=</span> <span class="n">parser</span><span class="o">.</span><span class="n">parse_args</span><span class="p">()</span>
|
||
</code></pre></div>
|
||
<ul>
|
||
<li><strong>最佳实践</strong>：有一个包含所有默认值的基础配置，以及每个实验的配置，只覆盖更改的部分。追踪每个实验的配置及其结果。</li>
|
||
</ul>
|
||
<h2 id="_6">日志与可观测性<a class="headerlink" href="#_6" title="Permanent link">¶</a></h2>
|
||
<ul>
|
||
<li><code>print</code> 语句用于调试。<strong>日志</strong>用于生产环境：</li>
|
||
</ul>
|
||
<div class="highlight"><pre><span></span><code><a id="__codelineno-10-1" name="__codelineno-10-1" href="#__codelineno-10-1"></a><span class="kn">import</span><span class="w"> </span><span class="nn">logging</span>
|
||
<a id="__codelineno-10-2" name="__codelineno-10-2" href="#__codelineno-10-2"></a>
|
||
<a id="__codelineno-10-3" name="__codelineno-10-3" href="#__codelineno-10-3"></a><span class="n">logger</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span>
|
||
<a id="__codelineno-10-4" name="__codelineno-10-4" href="#__codelineno-10-4"></a><span class="n">logger</span><span class="o">.</span><span class="n">setLevel</span><span class="p">(</span><span class="n">logging</span><span class="o">.</span><span class="n">INFO</span><span class="p">)</span>
|
||
<a id="__codelineno-10-5" name="__codelineno-10-5" href="#__codelineno-10-5"></a>
|
||
<a id="__codelineno-10-6" name="__codelineno-10-6" href="#__codelineno-10-6"></a><span class="n">logger</span><span class="o">.</span><span class="n">debug</span><span class="p">(</span><span class="s2">"Batch loaded: </span><span class="si">%d</span><span class="s2"> samples"</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">batch</span><span class="p">))</span> <span class="c1"># 详细，用于调试</span>
|
||
<a id="__codelineno-10-7" name="__codelineno-10-7" href="#__codelineno-10-7"></a><span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">"Epoch </span><span class="si">%d</span><span class="s2">: loss=</span><span class="si">%.4f</span><span class="s2">, lr=</span><span class="si">%.6f</span><span class="s2">"</span><span class="p">,</span> <span class="n">epoch</span><span class="p">,</span> <span class="n">loss</span><span class="p">,</span> <span class="n">lr</span><span class="p">)</span> <span class="c1"># 正常运行</span>
|
||
<a id="__codelineno-10-8" name="__codelineno-10-8" href="#__codelineno-10-8"></a><span class="n">logger</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span><span class="s2">"GPU memory >90</span><span class="si">%%</span><span class="s2">, consider reducing batch size"</span><span class="p">)</span>
|
||
<a id="__codelineno-10-9" name="__codelineno-10-9" href="#__codelineno-10-9"></a><span class="n">logger</span><span class="o">.</span><span class="n">error</span><span class="p">(</span><span class="s2">"Failed to load checkpoint: </span><span class="si">%s</span><span class="s2">"</span><span class="p">,</span> <span class="n">path</span><span class="p">)</span> <span class="c1"># 可恢复的错误</span>
|
||
<a id="__codelineno-10-10" name="__codelineno-10-10" href="#__codelineno-10-10"></a><span class="n">logger</span><span class="o">.</span><span class="n">critical</span><span class="p">(</span><span class="s2">"CUDA out of memory, aborting"</span><span class="p">)</span> <span class="c1"># 致命错误</span>
|
||
</code></pre></div>
|
||
<ul>
|
||
<li>
|
||
<p><strong>为什么不用 print</strong>：日志支持级别（在生产环境中过滤调试消息）、格式化（时间戳、模块名）和处理程序（写入文件、发送到监控系统），而无需更改日志调用。</p>
|
||
</li>
|
||
<li>
|
||
<p><strong>结构化日志</strong>同时输出机器可解析的格式（JSON）和人类可读的消息。这使得可以搜索特定字段并设置告警：</p>
|
||
</li>
|
||
</ul>
|
||
<div class="highlight"><pre><span></span><code><a id="__codelineno-11-1" name="__codelineno-11-1" href="#__codelineno-11-1"></a><span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">"training_step"</span><span class="p">,</span> <span class="n">extra</span><span class="o">=</span><span class="p">{</span>
|
||
<a id="__codelineno-11-2" name="__codelineno-11-2" href="#__codelineno-11-2"></a> <span class="s2">"epoch"</span><span class="p">:</span> <span class="mi">5</span><span class="p">,</span> <span class="s2">"step"</span><span class="p">:</span> <span class="mi">1200</span><span class="p">,</span> <span class="s2">"loss"</span><span class="p">:</span> <span class="mf">0.0342</span><span class="p">,</span> <span class="s2">"lr"</span><span class="p">:</span> <span class="mf">2.1e-4</span>
|
||
<a id="__codelineno-11-3" name="__codelineno-11-3" href="#__codelineno-11-3"></a><span class="p">})</span>
|
||
</code></pre></div>
|
||
<h2 id="api">API 设计<a class="headerlink" href="#api" title="Permanent link">¶</a></h2>
|
||
<ul>
|
||
<li>
|
||
<p>如果你的模型将被其他服务使用（Web 应用、移动应用、另一个机器学习管道），它需要一个 <strong>API</strong>（应用程序编程接口）。</p>
|
||
</li>
|
||
<li>
|
||
<p><strong>REST API</strong> 使用 HTTP 方法：<code>GET</code> 用于读取，<code>POST</code> 用于创建/预测，<code>PUT</code> 用于更新，<code>DELETE</code> 用于删除。端点遵循基于资源的命名：</p>
|
||
</li>
|
||
</ul>
|
||
<div class="highlight"><pre><span></span><code><a id="__codelineno-12-1" name="__codelineno-12-1" href="#__codelineno-12-1"></a>POST /api/v1/predict # 发送输入，获取预测结果
|
||
<a id="__codelineno-12-2" name="__codelineno-12-2" href="#__codelineno-12-2"></a>GET /api/v1/models # 列出可用模型
|
||
<a id="__codelineno-12-3" name="__codelineno-12-3" href="#__codelineno-12-3"></a>GET /api/v1/models/{id} # 获取模型详情
|
||
<a id="__codelineno-12-4" name="__codelineno-12-4" href="#__codelineno-12-4"></a>POST /api/v1/models/{id}/predict # 使用特定模型进行预测
|
||
</code></pre></div>
|
||
<ul>
|
||
<li><strong>FastAPI</strong> 是机器学习推理的首选 Python 框架：</li>
|
||
</ul>
|
||
<div class="highlight"><pre><span></span><code><a id="__codelineno-13-1" name="__codelineno-13-1" href="#__codelineno-13-1"></a><span class="kn">from</span><span class="w"> </span><span class="nn">fastapi</span><span class="w"> </span><span class="kn">import</span> <span class="n">FastAPI</span>
|
||
<a id="__codelineno-13-2" name="__codelineno-13-2" href="#__codelineno-13-2"></a><span class="kn">from</span><span class="w"> </span><span class="nn">pydantic</span><span class="w"> </span><span class="kn">import</span> <span class="n">BaseModel</span>
|
||
<a id="__codelineno-13-3" name="__codelineno-13-3" href="#__codelineno-13-3"></a>
|
||
<a id="__codelineno-13-4" name="__codelineno-13-4" href="#__codelineno-13-4"></a><span class="n">app</span> <span class="o">=</span> <span class="n">FastAPI</span><span class="p">()</span>
|
||
<a id="__codelineno-13-5" name="__codelineno-13-5" href="#__codelineno-13-5"></a>
|
||
<a id="__codelineno-13-6" name="__codelineno-13-6" href="#__codelineno-13-6"></a><span class="k">class</span><span class="w"> </span><span class="nc">PredictRequest</span><span class="p">(</span><span class="n">BaseModel</span><span class="p">):</span>
|
||
<a id="__codelineno-13-7" name="__codelineno-13-7" href="#__codelineno-13-7"></a> <span class="n">text</span><span class="p">:</span> <span class="nb">str</span>
|
||
<a id="__codelineno-13-8" name="__codelineno-13-8" href="#__codelineno-13-8"></a>
|
||
<a id="__codelineno-13-9" name="__codelineno-13-9" href="#__codelineno-13-9"></a><span class="k">class</span><span class="w"> </span><span class="nc">PredictResponse</span><span class="p">(</span><span class="n">BaseModel</span><span class="p">):</span>
|
||
<a id="__codelineno-13-10" name="__codelineno-13-10" href="#__codelineno-13-10"></a> <span class="n">label</span><span class="p">:</span> <span class="nb">str</span>
|
||
<a id="__codelineno-13-11" name="__codelineno-13-11" href="#__codelineno-13-11"></a> <span class="n">confidence</span><span class="p">:</span> <span class="nb">float</span>
|
||
<a id="__codelineno-13-12" name="__codelineno-13-12" href="#__codelineno-13-12"></a>
|
||
<a id="__codelineno-13-13" name="__codelineno-13-13" href="#__codelineno-13-13"></a><span class="nd">@app</span><span class="o">.</span><span class="n">post</span><span class="p">(</span><span class="s2">"/predict"</span><span class="p">,</span> <span class="n">response_model</span><span class="o">=</span><span class="n">PredictResponse</span><span class="p">)</span>
|
||
<a id="__codelineno-13-14" name="__codelineno-13-14" href="#__codelineno-13-14"></a><span class="k">async</span> <span class="k">def</span><span class="w"> </span><span class="nf">predict</span><span class="p">(</span><span class="n">request</span><span class="p">:</span> <span class="n">PredictRequest</span><span class="p">):</span>
|
||
<a id="__codelineno-13-15" name="__codelineno-13-15" href="#__codelineno-13-15"></a> <span class="n">result</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">request</span><span class="o">.</span><span class="n">text</span><span class="p">)</span>
|
||
<a id="__codelineno-13-16" name="__codelineno-13-16" href="#__codelineno-13-16"></a> <span class="k">return</span> <span class="n">PredictResponse</span><span class="p">(</span><span class="n">label</span><span class="o">=</span><span class="n">result</span><span class="o">.</span><span class="n">label</span><span class="p">,</span> <span class="n">confidence</span><span class="o">=</span><span class="n">result</span><span class="o">.</span><span class="n">score</span><span class="p">)</span>
|
||
</code></pre></div>
|
||
<ul>
|
||
<li>
|
||
<p>FastAPI 自动生成 API 文档（在 <code>/docs</code> 的 Swagger UI），使用 Pydantic 模型验证输入/输出，并支持异步以实现高吞吐量。</p>
|
||
</li>
|
||
<li>
|
||
<p><strong>gRPC</strong> 在内部服务间通信方面比 REST 更快。它使用 Protocol Buffers（二进制序列化，比 JSON 更小更快）并支持流式传输。TensorFlow Serving、Triton Inference Server 和许多微服务架构都使用它。</p>
|
||
</li>
|
||
</ul>
|
||
<h2 id="_7">打包与分发<a class="headerlink" href="#_7" title="Permanent link">¶</a></h2>
|
||
<ul>
|
||
<li>让你的代码可以作为包安装，使其他人（和你自己的脚本）可以干净地导入：</li>
|
||
</ul>
|
||
<div class="highlight"><pre><span></span><code><a id="__codelineno-14-1" name="__codelineno-14-1" href="#__codelineno-14-1"></a><span class="c1"># pyproject.toml</span>
|
||
<a id="__codelineno-14-2" name="__codelineno-14-2" href="#__codelineno-14-2"></a><span class="k">[project]</span>
|
||
<a id="__codelineno-14-3" name="__codelineno-14-3" href="#__codelineno-14-3"></a><span class="n">name</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s2">"my-ml-project"</span>
|
||
<a id="__codelineno-14-4" name="__codelineno-14-4" href="#__codelineno-14-4"></a><span class="n">version</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s2">"0.1.0"</span>
|
||
<a id="__codelineno-14-5" name="__codelineno-14-5" href="#__codelineno-14-5"></a><span class="n">requires-python</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s2">">=3.10"</span>
|
||
<a id="__codelineno-14-6" name="__codelineno-14-6" href="#__codelineno-14-6"></a><span class="n">dependencies</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">[</span>
|
||
<a id="__codelineno-14-7" name="__codelineno-14-7" href="#__codelineno-14-7"></a><span class="w"> </span><span class="s2">"torch>=2.0"</span><span class="p">,</span>
|
||
<a id="__codelineno-14-8" name="__codelineno-14-8" href="#__codelineno-14-8"></a><span class="w"> </span><span class="s2">"jax>=0.4"</span><span class="p">,</span>
|
||
<a id="__codelineno-14-9" name="__codelineno-14-9" href="#__codelineno-14-9"></a><span class="w"> </span><span class="s2">"pydantic>=2.0"</span><span class="p">,</span>
|
||
<a id="__codelineno-14-10" name="__codelineno-14-10" href="#__codelineno-14-10"></a><span class="p">]</span>
|
||
<a id="__codelineno-14-11" name="__codelineno-14-11" href="#__codelineno-14-11"></a>
|
||
<a id="__codelineno-14-12" name="__codelineno-14-12" href="#__codelineno-14-12"></a><span class="k">[project.optional-dependencies]</span>
|
||
<a id="__codelineno-14-13" name="__codelineno-14-13" href="#__codelineno-14-13"></a><span class="n">dev</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">[</span><span class="s2">"pytest"</span><span class="p">,</span><span class="w"> </span><span class="s2">"ruff"</span><span class="p">,</span><span class="w"> </span><span class="s2">"mypy"</span><span class="p">]</span>
|
||
<a id="__codelineno-14-14" name="__codelineno-14-14" href="#__codelineno-14-14"></a>
|
||
<a id="__codelineno-14-15" name="__codelineno-14-15" href="#__codelineno-14-15"></a><span class="k">[build-system]</span>
|
||
<a id="__codelineno-14-16" name="__codelineno-14-16" href="#__codelineno-14-16"></a><span class="n">requires</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">[</span><span class="s2">"setuptools>=64"</span><span class="p">]</span>
|
||
<a id="__codelineno-14-17" name="__codelineno-14-17" href="#__codelineno-14-17"></a><span class="n">build-backend</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s2">"setuptools.backends._legacy:_Backend"</span>
|
||
</code></pre></div>
|
||
<div class="highlight"><pre><span></span><code><a id="__codelineno-15-1" name="__codelineno-15-1" href="#__codelineno-15-1"></a>pip<span class="w"> </span>install<span class="w"> </span>-e<span class="w"> </span><span class="s2">".[dev]"</span><span class="w"> </span><span class="c1"># 以可编辑模式安装，包含开发依赖</span>
|
||
</code></pre></div>
|
||
<ul>
|
||
<li>
|
||
<p><strong>可编辑安装</strong>（<code>-e</code>）：对源代码的更改会立即生效，无需重新安装。开发期间必不可少。</p>
|
||
</li>
|
||
<li>
|
||
<p><strong>锁定依赖</strong>：使用确切版本的 <code>requirements.txt</code>（<code>torch==2.2.1</code>，而不是 <code>torch>=2.0</code>）确保可重现性。使用 <code>pip freeze > requirements.txt</code> 捕获你当前的环境。对于更复杂的依赖管理，使用 <code>uv</code>、<code>poetry</code> 或 <code>pip-tools</code>。</p>
|
||
</li>
|
||
</ul>
|
||
<h2 id="ai">使用 AI 编码助手<a class="headerlink" href="#ai" title="Permanent link">¶</a></h2>
|
||
<ul>
|
||
<li>
|
||
<p>AI 编码助手（Claude Code、GitHub Copilot、Cursor 等）现在已成为专业工程师工作流程的一部分。使用得当，它们能极大加速开发。使用不当，它们会引入微妙的错误、侵蚀你对代码库的理解，并制造虚假的生产力感。</p>
|
||
</li>
|
||
<li>
|
||
<p>正确的心智模型：<strong>AI 助手是一个快速但缺乏经验的结对程序员</strong>。它可以快速编写代码，熟悉语法和标准模式，并且阅读过的文档比你还多。但它不了解你的特定系统、业务约束、边界情况以及设计决策背后的<em>原因</em>。你是高级工程师；AI 助手是初级工程师。你来指导、审查并承担责任。</p>
|
||
</li>
|
||
</ul>
|
||
<h3 id="ai_1">AI 助手擅长之处<a class="headerlink" href="#ai_1" title="Permanent link">¶</a></h3>
|
||
<ul>
|
||
<li>
|
||
<p><strong>样板代码和脚手架</strong>：生成 Dockerfile、CI 配置、测试夹具、数据类定义、argparse 设置。这些遵循众所周知的模式，手动编写很繁琐。让 AI 生成它们，然后审查正确性。</p>
|
||
</li>
|
||
<li>
|
||
<p><strong>编写测试</strong>：描述函数的行为，AI 助手生成测试用例。它通常会捕捉到你可能会遗漏的边界情况（空输入、负值、Unicode）。始终阅读生成的测试——它们验证的是你的假设，而不仅仅是你的代码。</p>
|
||
</li>
|
||
<li>
|
||
<p><strong>重构</strong>："将这个块提取成函数"、"将这个类改为使用 dataclasses"、"给这个模块添加类型提示"。机械性的转换，意图明确，引入细微错误的风险较低。</p>
|
||
</li>
|
||
<li>
|
||
<p><strong>探索和原型开发</strong>："写一个快速脚本来 benchmark 推理延迟"或"展示如何使用 HuggingFace tokeniser API"。AI 助手能比阅读文档更快地给你一个可用的起点。</p>
|
||
</li>
|
||
<li>
|
||
<p><strong>文档和 docstrings</strong>：AI 助手可以根据你的代码结构生成文档。你需要审查准确性，但苦力活已经自动化了。</p>
|
||
</li>
|
||
<li>
|
||
<p><strong>调试辅助</strong>：粘贴错误回溯信息并请求诊断。AI 助手通常能识别根本原因并提出修复建议，尤其是对于常见问题（形状不匹配、导入错误、CUDA 内存不足）。</p>
|
||
</li>
|
||
</ul>
|
||
<h3 id="ai_2">何时不应依赖 AI 助手<a class="headerlink" href="#ai_2" title="Permanent link">¶</a></h3>
|
||
<ul>
|
||
<li>
|
||
<p><strong>新颖的架构决策</strong>：如果你正在设计一个新的训练管道，AI 助手会给出一个通用的答案。它不了解你的数据约束、延迟要求或团队专业知识。使用 AI 助手来实现你已经深思熟虑的设计。</p>
|
||
</li>
|
||
<li>
|
||
<p><strong>安全关键代码</strong>：认证、加密、输入清理。AI 助手可能生成看起来正确但存在细微漏洞的代码（SQL 注入、不安全的默认值、时序攻击）。安全代码应由理解威胁模型的人编写，并由另一个人审查。</p>
|
||
</li>
|
||
<li>
|
||
<p><strong>性能关键的内循环</strong>：AI 助手会编写正确但天真的代码。对于 GPU 内核、内存关键的数据结构或延迟敏感的推理路径，你需要理解硬件约束（第 13 章、第 16 章）并有目的地进行优化。</p>
|
||
</li>
|
||
<li>
|
||
<p><strong>你不理解的代码</strong>：如果 AI 助手生成了 200 行代码，而你无法解释每一行的作用，那就不要提交。你现在正在维护你不理解的代码，当它出问题时（它会的），你无法调试。这是最常见也最危险的失败模式。</p>
|
||
</li>
|
||
</ul>
|
||
<h3 id="_8">审查纪律<a class="headerlink" href="#_8" title="Permanent link">¶</a></h3>
|
||
<ul>
|
||
<li>
|
||
<p><strong>在提交前始终逐行阅读</strong>生成的代码。这不是可选的。AI 助手的代码是草稿，不是成品。就像对待同事的拉取请求一样：批判性地审查它。</p>
|
||
</li>
|
||
<li>
|
||
<p><strong>检查什么</strong>：</p>
|
||
<ul>
|
||
<li><strong>正确性</strong>：它是否真的做了你要求的事情？AI 助手经常解决与你意图略有不同的问题。</li>
|
||
<li><strong>边界情况</strong>：它是否处理了空输入、None 值、负数、非常大的输入？AI 助手经常省略边界情况处理。</li>
|
||
<li><strong>幻想的 API</strong>：AI 助手可能调用不存在函数或使用不存在的参数，尤其是对于较新或较少使用的库。验证每个 API 调用是否真实存在。</li>
|
||
<li><strong>过度工程</strong>：AI 助手倾向于产生比必要更多的代码。一个 50 行的解决方案解决一个 10 行的问题，增加了不必要的复杂性。无情地简化。</li>
|
||
<li><strong>安全性</strong>：硬编码的密钥、未经清理的用户输入、不安全的默认值。AI 助手不会以对抗性思维思考。</li>
|
||
<li><strong>风格一致性</strong>：生成的代码是否与项目的约定一致（命名、模式、错误处理）？</li>
|
||
</ul>
|
||
</li>
|
||
</ul>
|
||
<h3 id="_9">如何编写好的提示词<a class="headerlink" href="#_9" title="Permanent link">¶</a></h3>
|
||
<ul>
|
||
<li>
|
||
<p>AI 助手输出的质量直接与你的指令质量成正比。模糊的提示词得到模糊的代码。</p>
|
||
</li>
|
||
<li>
|
||
<p><strong>糟糕</strong>："写一个数据加载器"</p>
|
||
</li>
|
||
<li>
|
||
<p><strong>好</strong>："为一个包含'text'和'label'列的 CSV 文件编写一个 PyTorch DataLoader。使用 HuggingFace tokeniser 'bert-base-uncased' 对文本进行分词，max_length=512。返回 input_ids、attention_mask 和 label 作为张量。处理 CSV 中标签列有缺失值的情况，跳过那些行。"</p>
|
||
</li>
|
||
<li>
|
||
<p><strong>提供上下文</strong>：告诉 AI 助手你的项目结构、现有代码、约束和约定。上下文越多，输出越好。</p>
|
||
</li>
|
||
<li>
|
||
<p><strong>指定约束</strong>："只使用标准库"、"必须兼容 Python 3.10"、"不要使用全局变量"、"遵循 <code>src/models/transformer.py</code> 中的现有模式"。</p>
|
||
</li>
|
||
<li>
|
||
<p><strong>要求解释</strong>："实现 X 并解释关键的设计决策。"这会迫使 AI 助手阐述其推理，使你更容易发现错误假设。</p>
|
||
</li>
|
||
</ul>
|
||
<h3 id="ai_3">使用质量门控来捕捉 AI 助手的错误<a class="headerlink" href="#ai_3" title="Permanent link">¶</a></h3>
|
||
<ul>
|
||
<li>
|
||
<p>你现有的质量基础设施（文件 04）捕捉 AI 助手的错误与捕捉人类的错误同样有效：</p>
|
||
<ul>
|
||
<li><strong>类型检查（mypy）</strong>：捕捉幻想的 API 签名和类型不匹配。</li>
|
||
<li><strong>代码检查（ruff）</strong>：捕捉未使用的导入、未定义的变量和风格违规。</li>
|
||
<li><strong>测试（pytest）</strong>：如果 AI 助手的代码通过了你的测试套件，它更可能是正确的。如果你还没有测试，在要求 AI 助手实现功能之前<em>先编写测试</em>（测试驱动开发与 AI 助手配合得特别好）。</li>
|
||
<li><strong>CI 管道</strong>：在每次提交时自动运行上述所有检查。</li>
|
||
</ul>
|
||
</li>
|
||
<li>
|
||
<p><strong>"AI 助手写代码" + "质量门控验证"</strong> 的组合比单独使用任何一种都更高效。AI 助手快速但草率；门控工具彻底但不写代码。两者结合，你同时获得速度和正确性。</p>
|
||
</li>
|
||
</ul>
|
||
<h3 id="_10">生产力陷阱<a class="headerlink" href="#_10" title="Permanent link">¶</a></h3>
|
||
<ul>
|
||
<li>
|
||
<p>使用编码助手的最大风险是<strong>生产力的幻觉</strong>。你可以在 10 分钟内生成 500 行代码。但如果你花 2 小时调试这些你并不理解的 500 行代码，那还不如自己花 30 分钟写 200 行代码来得快。</p>
|
||
</li>
|
||
<li>
|
||
<p>使用 AI 助手的真正生产力来自：</p>
|
||
<ol>
|
||
<li><strong>保持控制</strong>：你决定架构，AI 助手填入实现。</li>
|
||
<li><strong>理解生成的内容</strong>：如果你无法解释它，就重写它或让 AI 助手简化它。</li>
|
||
<li><strong>投资质量门控</strong>：测试、类型和代码检查的成本通过每次 AI 交互分摊。</li>
|
||
<li><strong>利用 AI 助手弥补你的弱点</strong>：如果你擅长算法但编写测试很慢，让 AI 助手写测试。如果你对 UI 代码很快但不熟悉数据库查询，让 AI 助手草拟 SQL。发挥你的优势，委托你的短板。</li>
|
||
</ol>
|
||
</li>
|
||
<li>
|
||
<p>从编码助手中获益最多的工程师是那些已经擅长编码的人。AI 助手放大你现有的技能；它不会取代你的技能。理解数据结构、算法、系统设计和软件工程（整章的内容）让你能够有效地指导 AI 助手并批判性地评估其输出。</p>
|
||
</li>
|
||
</ul>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
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