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在深度学习落地过程中，有一个常见的误区：一旦推理速度不达标，大家的第一反应往往是拿着模型开到，比如：做剪枝、搞蒸馏、甚至牺牲精度换小模型。 实际上生产环境中的 Python 推理链路隐藏着巨大的“工程红利”。很多时候你的模型本身并不慢，慢的是 ...