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Vega

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Vega ver1.8.5 released

Bug Fixed:
- Fixed a bug when the SPNAS algorithm cluster training fails.
- Fixed bugs such as model copy failure in safe mode.

Introduction

Vega is an AutoML algorithm tool chain developed by Noah's Ark Laboratory, the main features are as follows:

Full pipeline capabilities: The AutoML capabilities cover key functions such as Hyperparameter Optimization, Data Augmentation, Network Architecture Search (NAS), Model Compression, and Fully Train. These functions are highly decoupled and can be configured as required, construct a complete pipeline.
Industry-leading AutoML algorithms: Provides Noah's Ark Laboratory's self-developed industry-leading algorithm (Benchmark) and Model Zoo to download the state-of-the-art (SOTA) models.
Fine-grained network search space: The network search space can be freely defined, and rich network architecture parameters are provided for use in the search space. The network architecture parameters and model training hyperparameters can be searched at the same time, and the search space can be applied to Pytorch, TensorFlow and MindSpore.
High-concurrency neural network training capability: Provides high-performance trainers to accelerate model training and evaluation.
Multi-Backend: PyTorch (GPU and Ascend 910), TensorFlow (GPU and Ascend 910), MindSpore (Ascend 910).
Ascend platform: Search and training on the Ascend 910 and model evaluation on the Ascend 310.

Algorithm List

Category	Algorithm	Description	reference
NAS	CARS: Continuous Evolution for Efficient Neural Architecture Search	Structure Search Method of Multi-objective Efficient Neural Network Based on Continuous Evolution	ref
NAS	ModularNAS: Towards Modularized and Reusable Neural Architecture Search	A code library for various neural architecture search methods including weight sharing and network morphism	ref
NAS	MF-ASC	Multi-Fidelity neural Architecture Search with Co-kriging	ref
NAS	NAGO: Neural Architecture Generator Optimization	An Hierarchical Graph-based Neural Architecture Search Space	ref
NAS	SR-EA	An Automatic Network Architecture Search Method for Super Resolution	ref
NAS	ESR-EA: Efficient Residual Dense Block Search for Image Super-resolution	Multi-objective image super-resolution based on network architecture search	ref
NAS	Adelaide-EA: SEGMENTATION-Adelaide-EA-NAS	Network Architecture Search Algorithm for Image Segmentation	ref
NAS	SP-NAS: Serial-to-Parallel Backbone Search for Object Detection	Serial-to-Parallel Backbone Search for Object Detection Efficient Search Algorithm for Object Detection and Semantic Segmentation in Trunk Network Architecture	ref
NAS	SM-NAS: Structural-to-Modular NAS	Two-stage object detection architecture search algorithm	Coming soon
NAS	Auto-Lane: CurveLane-NAS	An End-to-End Framework Search Algorithm for Lane Lines	ref
NAS	AutoFIS	An automatic feature selection algorithm for recommender system scenes	ref
NAS	AutoGroup	An automatically learn feature interaction for recommender system scenes	ref
NAS	MF-ASC	Multi-Fidelity neural Architecture Search with Co-kriging	ref
Model Compression	Quant-EA: Quantization based on Evolutionary Algorithm	Automatic mixed bit quantization algorithm, using evolutionary strategy to quantize each layer of the CNN network	ref
Model Compression	Prune-EA	Automatic channel pruning algorithm using evolutionary strategies	ref
HPO	ASHA: Asynchronous Successive Halving Algorithm	Dynamic continuous halving algorithm	ref
HPO	BOHB: Hyperband with Bayesian Optimization	Hyperband with Bayesian Optimization	ref
HPO	BOSS: Bayesian Optimization via Sub-Sampling	A universal hyperparameter optimization algorithm based on Bayesian optimization framework for resource-constraint hyper-parameters search	ref
Data Augmentation	PBA: Population Based Augmentation: Efficient Learning of Augmentation Policy Schedules	Data augmentation based on PBT optimization	ref
Data Augmentation	CycleSR: Unsupervised Image Super-Resolution with an Indirect Supervised Path	Unsupervised style migration algorithm for low-level vision problem.	ref
Fully Train	Beyond Dropout: Feature Map Distortion to Regularize Deep Neural Networks	Neural network training (regularization) based on disturbance of feature map	ref
Fully Train	Circumventing Outliers of AutoAugment with Knowledge Distillation	Joint knowledge distillation and data augmentation for high performance classication model training, achieved 85.8% Top-1 accuracy on ImageNet 1k	Coming soon

Installation

Run the following commands to install Vega:

pip3 install --user --upgrade noah-vega

Usage

Run the vega command to run the Vega application. For example, run the following command to run the CARS algorithm:

vega ./examples/nas/cars/cars.yml

The cars.yml file contains definitions such as pipeline, search algorithm, search space, and training parameters. Vega provides more than 40 examples for reference: Examples, Example Guide, and Configuration Guide.

The security mode is applicable to communication with high security requirements. Before running this command, run the security configuration.

vega ./examples/nas/cars/cars.yml -s

Reference

Reader	Refrence
User	Install Guide, Deployment Guide, Configuration Guide, Security Configuration, Examples, Evaluate Service
Developer	Development Reference, Quick Start Guide, Dataset Guide, Algorithm Development Guide

FAQ

For common problems and exception handling, please refer to FAQ.

Citation

@misc{wang2020vega,
      title={VEGA: Towards an End-to-End Configurable AutoML Pipeline},
      author={Bochao Wang and Hang Xu and Jiajin Zhang and Chen Chen and Xiaozhi Fang and Ning Kang and Lanqing Hong and Wei Zhang and Yong Li and Zhicheng Liu and Zhenguo Li and Wenzhi Liu and Tong Zhang},
      year={2020},
      eprint={2011.01507},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

Cooperation and Contribution

Welcome to use Vega. If you have any questions or suggestions, need help, fix bugs, contribute new algorithms, or improve the documentation, submit an issue in the community. We will reply to and communicate with you in a timely manner.