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

TL;DR

VEGA is a versatile AutoML framework that integrates multiple modules, supports various hardware platforms, and improves model discovery efficiency, demonstrating significant speedups over state-of-the-art methods on benchmark tasks.

Contribution

The paper introduces VEGA, an end-to-end AutoML pipeline with a novel search space and unified framework supporting multiple back-ends and hardware platforms.

Findings

VEGA accelerates model search, e.g., 10x faster than EfficientNet-B5.

VEGA discovers high-performance models surpassing SOTA.

Extensive benchmarks validate VEGA's effectiveness across tasks.

Abstract

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.