TensorFlow估算器：管理高级机器学习框架中的简单性与灵活性
TensorFlow Estimators: Managing Simplicity vs. Flexibility in High-Level Machine Learning Frameworks
摘要：
我们提出了一个框架，用于指定，训练，评估和部署机器学习模型。我们的重点是简化从业者的尖端机器学习，以便将这些技术投入生产。认识到深度学习领域的快速发展，我们没有尝试在特定领域语言（DSL）或类似的配置语言中捕获所有可能的模型架构的设计空间。我们允许用户编写代码来定义他们的模型，但提供了抽象，指导开发人员以有利于生产化的方式编写模型。我们还提供了一个统一的Estimate接口，使得独立于模型实现编写下游基础设施（例如分布式训练，超参数调优）成为可能。我们通过提供不同抽象级别的API来平衡灵活性和简单性的竞争需求，使通用模型架构开箱即用，同时提供旨在加速模型架构实验的实用程序库。为了使开箱即用的模型在广泛的问题中灵活可用，这些固定估计器不仅在传统的超参数上进行参数化，而且还使用特征列进行参数化，特征列是一种描述如何解释输入数据的声明性规范。我们讨论了在研究和生产环境中使用该框架的经验，并展示了对代码健康、可维护性和开发速度的影响。
英文摘要：
We present a framework for specifying, training, evaluating, and deploying machine learning models. Our focus is on simplifying cutting edge machine learning for practitioners in order to bring such technologies into production. Recognizing the fast evolution of the field of deep learning, we make no attempt to capture the design space of all possible model architectures in a domain- specific language (DSL) or similar configuration language. We allow users to write code to define their models, but provide abstractions that guide develop- ers to write models in ways conducive to productionization. We also provide a unifying Estimator interface, making it possible to write downstream infrastructure (e.g. distributed training, hyperparameter tuning) independent of the model implementation. We balance the competing demands for flexibility and simplicity by offering APIs at different levels of abstraction, making common model architectures available out of the box, while providing a library of utilities designed to speed up experimentation with model architectures. To make out of the box models flexible and usable across a wide range of problems, these canned Estimators are parameterized not only over traditional hyperparameters, but also using feature columns, a declarative specification describing how to interpret input data. We discuss our experience in using this framework in re- search and production environments, and show the impact on code health, maintainability, and development speed.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
