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

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