TensorFI: A Configurable Fault Injector for TensorFlow Applications
TensorFI: A Configurable Fault Injector for TensorFlow Applications
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TensorFI：适用于 TensorFlow 应用程序的可配置故障注入器
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
Nathan Debardeleben
中科院分区：
文献类型：
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作者：
Guanpeng Li;K. Pattabiraman;Nathan Debardeleben
机器学习 (ML) 应用程序已成为下一代硬件和软件平台的杀手级应用程序，人们对构建此类应用程序的软件框架非常感兴趣。 TensorFlow 是一种用于构建 ML 应用程序的高级数据流框架，近年来已成为最流行的框架。机器学习应用程序也越来越多地用于安全关键系统，例如自动驾驶汽车和家庭机器人。因此，迫切需要评估使用 TensorFlow 等框架构建的 ML 应用程序的弹性。在本文中，我们为 TensorFlow 构建了一个名为 TensorFI 的高级故障注入框架，用于评估 ML 应用程序的弹性。 TensorFI 灵活、易于使用且便携。它还允许机器学习应用程序员探索不同参数和算法对错误恢复的影响。
Machine Learning (ML) applications have emerged as the killer applications for next generation hardware and software platforms, and there is a lot of interest in software frameworks to build such applications. TensorFlow is a high-level dataflow framework for building ML applications and has become the most popular one in the recent past. ML applications are also being increasingly used in safety-critical systems such as self-driving cars and home robotics. Therefore, there is a compelling need to evaluate the resilience of ML applications built using frameworks such as TensorFlow. In this paper, we build a high-level fault injection framework for TensorFlow called TensorFI for evaluating the resilience of ML applications. TensorFI is flexible, easy to use, and portable. It also allows ML application programmers to explore the effects of different parameters and algorithms on error resilience.