Designing a Visual Analytics System for Industry-Scale Deep Neural Network Models

The complexity of industry-scale deep learning models and datasets pose unique design, visualization, and system challenges that are inadequately addressed by existing work. Through participatory design sessions with over 15 researchers and engineers at Facebook, we have designed, developed, and deployed ACTIVIS, a visual analytics system for interpreting industry-scale deep learning models and results. By tightly integrating multiple coordinated views, such as a computation graph overview of the model architecture, and a neuron activation view for pattern discovery and comparison, users can explore complex deep neural network models at both instanceand subset-level. ACTIVIS has been deployed on Facebook’s machine learning platform. This article is a summary for the VAST’17 paper (TVCG track) ActiVis: Visual Exploration of Industry-Scale Deep Neural Network Models [2]. 1 DESIGNING FOR INDUSTRY-SCALE MODELS Despite the increasing interest in developing visualization tools for deep learning interpretation [5–7], the complexity of large-scale models and datasets used in industry pose unique design challenges that are inadequately addressed by existing work. For example, while most existing visualization tools target image datasets, deep learning tasks in industry often involve different types of data, including text and numerical data. Furthermore, in designing tools for realworld deployment, it is a high priority that the tools be flexible and scalable, adapting to the wide variety of models and datasets used. These observations motivate us to design and develop ACTIVIS [2], a visual analytics system for deep neural network models, now deployed on Facebook’s machine learning platform. Since the ACTIVIS project started in April 2016, we have conducted participatory design sessions with over 15 Facebook engineers, researchers, and data scientists across multiple teams to learn about their visual analytics needs. We identified six key design challenges — for data, model, and analytics — that have not been adequately addressed by existing deep learning visualization tools. The challenges include the need to support: (1) diverse input data sources, (2) high data volume, (3) complex model architecture, (4) a great variety of models, (5) diverse subset definitions for analytics, and (6) both instanceand subset-level analyses. These challenges shape the main design goals of ACTIVIS. 2 ACTIVIS CONTRIBUTIONS ACTIVIS’s main contributions include: • A novel visual representation that unifies instanceand subsetlevel inspections of neuron activation, facilitating comparison of activation patterns for multiple instances. *e-mail: kahng@gatech.edu †e-mail: mortimer@fb.com ‡e-mail: adityakalro@fb.com §e-mail: polo@gatech.edu • An interface that tightly integrates an overview of graph-structured complex models and local inspection of neuron activations, allowing users to explore the model at different levels of abstraction. • A deployed system scaling to large datasets and models. • Case studies with Facebook engineers and data scientists that highlight how ACTIVIS helps them with their work. ACTIVIS’s multiple coordinated views help users get a high-level overview of the model from which the user can drill down to perform localized inspection of activations. ACTIVIS visualizes how neurons are activated by user-specified instances or instance subsets, to help users understand how a model derives its predictions. The subsets can be flexibly defined using data attributes, features, or output results, enabling model inspection from multiple angles. While many existing deep learning visualization tools support instancelevel exploration [6, 7], ACTIVIS is the first tool that simultaneously supports instanceand subset-level exploration. Both exploration strategies are common and effective, and they offer complementary analytics benefits. Instance-based analysis instructs how individual instances contribute to a model’s accuracy, but it is tedious to inspect many instances one by one. Subset-based analysis leverages input features or instance subsets to help reveal relationships between data attributes and machine learning algorithms’ outputs [3]. It is especially beneficial when dealing with huge datasets in industry, which may consist of millions or billions of data points. By exploring instance subsets and enabling their comparison with individual instances, users can learn how them models respond to many different slices of the data. We refer our readers to the longer version of our ACTIVIS [2] VAST’17 paper published in IEEE Transactions on Visualization and Computer Graphics.