September 2022
Intermediate to advanced
549 pages
11h 27m
Chinese
Content preview from 基于AWS的数据科学实践
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306
｜
第
7
章
7.15.10 SageMaker
调试器的提前停止规则
SageMaker
调试器支持许多可在规则触发时执行的内建动作。例如，
StopTraining()
动作通过在目标指标（如准确度）到达峰值且无法通过额外训练来提升时，停止当
前训练任务的方式以节约开销。峰值通过诸如
overfit
这样的规则来检测。我们按照
一定时间或步骤数量内的相对变化来配置规则。例如，如果在一千个步骤之后，模
型准确度没有达到
1%
的提升，我们需要停止训练任务并节省一些金钱。
StopTraining()
动
作将在规则触发时果断结束训练任务。与使用
Spot
实例类似，
我们应在任务提前被终止之前使用检查点，尤其是最后一个检查点。
7.16
小结
在本章，我们使用
Keras API
、
TensorFlow 2.x
和
BERT
，在
Amazon SageMaker
中
训练了第一个模型。我们深入探索了
SageMaker
的基础设施，模型开发
SDK
和
SageMaker
训练任务。我们使用
SageMaker
训练模型，描述了安全方面的最佳实践，
并探索了一些节约开支并提升性能的技巧。
我们也了解到，
BERT
的转换器神经网络架构通过使用双向的方式学习语料库中词
语的上下文表示，彻底改变了
NLP
和
NLU
领域。我们演示了如何微调预训练的
BERT
模型来为产品评价构建领域特定的文本分类器。这与上一代
NLP
模型截然
相反，例如同时只学习一个方向的
Word2Vec
，只保留学到的嵌入而丢弃原始模型
的
GloVe
，和使用需要大量内存和计算资源的复杂循环神经网络（ ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.