huawei-noah/vega

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:

1. 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.
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) models.
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 (GPU and Ascend 910), TensorFlow (GPU and Ascend 910), MindSpore (Ascend 910).
6. Ascend platform: Search and training on the Ascend 910 and model evaluation on the Ascend 310.

Algorithm List

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

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.