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README.md

Vega

中文

Vega ver1.2.0 released:

Introduction

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

  1. Full pipeline capailities: 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.
  2. 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) model.
  3. 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.
  4. High-concurrency neural network training capability: Provides high-performance trainers to accelerate model training and evaluation.
  5. Multi-Backend: PyTorch, TensorFlow, MindSpore(trial)

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 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
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 TPE: Tree-structured Parzen Estimator Approach A hyperparameter optimization Algorithm Based on Tree - Structured Parzen Estimation ref
HPO BO: Bayesian Optimization Bayesian optimization 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

Obtaining and Installing

Install Vega and the open source softwares that Vega depends on:

pip3 install --user noah-vega
python3 -m vega.tools.install_pkgs

For more detail, please refer installation guide. If you want to deploy Vega in local cluster, see the deployment guide .

Usage Guide

The Vega is highly modularized. You can configure the search space, search algorithm in a pipeline way. To run the Vega application is to load the configuration file and complete the AutoML process based on the configuration. Vega provides detailed operation examples for your reference. For details, see the examples . Example of running CARS algorithm:

cd examples
python3 ./run_pipeline.py ./nas/cars/cars.yml -b pytorch

Therefore, before using the Vega, you need to fully understand the meaning of the configuration items. For details, see the Configuration Guide.

Note:

Before running an example, you need to configure the directory where the dataset and pre-trained models are located in the algorithm configuration file. Please refer to Example Reference .

Developer Guide

The Vega framework components are decoupled, and each functional component is combined using the registration mechanism to facilitate function and algorithm extension. For details about the Vega architecture and main mechanisms, see the Developer Guide .

In addition, you can refer to the Quick Start Guide to implement a simple network search function and quickly enter the Vega application development through practice.

During the development of the Vega application, the first problem is how to introduce the service data set to the Vega application. For details, see the Dataset Guide .

For different algorithms, you can refer doc Algorithm Development Guide . You can add the new algorithm to Vega step by step based on the example provided in this document.

In most Automl algorithms, the search space is closely related to the network. We try to unify the definition of the search space so that the same search space can adapt to different search algorithms. This is called the Fine-Grained Search Space Guide . Welcome to try it.

Of course, this document cannot solve all the problems. If you have any questions, please feel free to provide feedback through the issue. We will reply to you and solve your problems in a timely manner.

Reference List

object refrence
User Install Guide, Deployment Guide, Configuration Guide, Examples, Evaluate Service
Developer Developer Guide, Quick Start Guide, Dataset Guide, Algorithm Development Guide, Fine-Grained Search Space 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}
}

Video

video Youtube

Cooperation and contribution

Welcome to use Vega. If you have any questions, ask for help, fix bugs, contribute algorithms, or improve documents, submit the issue in the community. We will reply to and communicate with you in a timely manner. Welcome to join our QQ chatroom (Chinese): 833345709.