论文标题
深度学习培训实时互动分析的系统
A System for Real-Time Interactive Analysis of Deep Learning Training
论文作者
论文摘要
在深度学习模型训练期间进行诊断或探索性分析是一项挑战，但对于以渐进观察为指导的一系列决策通常是必要的。当前用于此目的的系统仅限于监视培训过程开始之前必须指定的已记录数据。每次需要新的信息时，都需要在培训过程中进行停止更改的周期。这些局限性使交互式探索和诊断任务变得困难，在模型开发过程中施加了长期繁琐的迭代。我们提出了一个新系统，该系统使用户能够在实时过程中执行交互式查询，从而生成实时信息，这些信息可以以多种格式在多个表面上以多种形式以几种所需可视化的形式渲染。为了实现这一目标，我们将深入学习培训过程的各种探索性检查和诊断任务建模为使用MAP-REDUCE范式的流的规格，许多数据科学家已经熟悉了许多数据科学家。我们的设计通过定义可组合原始素来实现一般性和可扩展性，这与当前可用系统所使用的根本不同。我们系统的开源实现可作为TensorWatch项目https://github.com/microsoft/tensorwatch获得。
Performing diagnosis or exploratory analysis during the training of deep learning models is challenging but often necessary for making a sequence of decisions guided by the incremental observations. Currently available systems for this purpose are limited to monitoring only the logged data that must be specified before the training process starts. Each time a new information is desired, a cycle of stop-change-restart is required in the training process. These limitations make interactive exploration and diagnosis tasks difficult, imposing long tedious iterations during the model development. We present a new system that enables users to perform interactive queries on live processes generating real-time information that can be rendered in multiple formats on multiple surfaces in the form of several desired visualizations simultaneously. To achieve this, we model various exploratory inspection and diagnostic tasks for deep learning training processes as specifications for streams using a map-reduce paradigm with which many data scientists are already familiar. Our design achieves generality and extensibility by defining composable primitives which is a fundamentally different approach than is used by currently available systems. The open source implementation of our system is available as TensorWatch project at https://github.com/microsoft/tensorwatch.
