arXiv 2011.015072020-11-26cs.CV

VEGA：走向端到端可配置的AutoML管道

VEGA: Towards an End-to-End Configurable AutoML Pipeline

Hong Kong University of Science and Technology（香港科技大学）

Bochao Wang, Hang Xu, Jiajin Zhang, Chen Chen, Xiaozhi Fang, Yixing Xu, Ning Kang, Lanqing Hong, Chenhan Jiang, Xinyue Cai, Jiawei Li, Fengwei Zhou, Yong Li, Zh… 展开作者

Bochao Wang, Hang Xu, Jiajin Zhang, Chen Chen, Xiaozhi Fang, Yixing Xu, Ning Kang, Lanqing Hong, Chenhan Jiang, Xinyue Cai, Jiawei Li, Fengwei Zhou, Yong Li, Zhicheng Liu, Xinghao Chen, Kai Han, Han Shu, Dehua Song, Yunhe Wang, Wei Zhang, Chunjing Xu, Zhenguo Li, Wenzhi Liu, Tong Zhang

备注：: AutoML pipeline. Code is open-sourced at https://github.com/huawei-noah/vega

阅读 PDF

arXiv 页面 ↗HTML 原文 ↗

摘要：

自动机器学习（AutoML）是自动发现和部署机器学习模型的重要工业解决方案。然而，设计一个集成的AutoML系统面临着可配置性、可扩展性、可集成性和平台多样性四大挑战。在这项工作中，我们提出了VEGA，一个高效和全面的AutoML框架，兼容和优化多个硬件平台。VEGA管道集成了AutoML的各种模块，包括神经结构搜索（NAS），超参数优化（HPO），自动数据增强，模型压缩和完全训练。b）为了支持多种搜索算法和任务，我们设计了一种新的细粒度搜索空间及其描述语言，使其能够容易地适应不同的搜索算法和任务。c）我们将深度学习框架的通用组件抽象到统一的接口中。VEGA可以使用多个后端和硬件执行。在多个任务上进行的大量基准测试实验表明，VEGA可以改进现有的AutoML算法，并发现针对SOTA方法的新的高性能模型，例如，在Ascend上搜索的DNet模型zoo比EfficientNet-B5快10倍，比ImageNet上的RegNetX-32 GF快9.2倍。VEGA在https://github.com/huawei-noah/vega上开源。

英文摘要：

Automated Machine Learning (AutoML) is an important industrial solution for automatic discovery and deployment of the machine learning models. However, designing an integrated AutoML system faces four great challenges of configurability, scalability, integrability, and platform diversity. In this work, we present VEGA, an efficient and comprehensive AutoML framework that is compatible and optimized for multiple hardware platforms. a) The VEGA pipeline integrates various modules of AutoML, including Neural Architecture Search (NAS), Hyperparameter Optimization (HPO), Auto Data Augmentation, Model Compression, and Fully Train. b) To support a variety of search algorithms and tasks, we design a novel fine-grained search space and its description language to enable easy adaptation to different search algorithms and tasks. c) We abstract the common components of deep learning frameworks into a unified interface. VEGA can be executed with multiple back-ends and hardwares. Extensive benchmark experiments on multiple tasks demonstrate that VEGA can improve the existing AutoML algorithms and discover new high-performance models against SOTA methods, e.g. the searched DNet model zoo for Ascend 10x faster than EfficientNet-B5 and 9.2x faster than RegNetX-32GF on ImageNet. VEGA is open-sourced at https://github.com/huawei-noah/vega.

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。