evoxbench 1.0.5

A benchmark for NAS algorithms

Neural Architecture Search as Multiobjective Optimization Benchmarks: Problem Formulation and Performance Assessment arXiv

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EvoXBench is a platfrom offering instant benchmarking of evolutionary multi-objective optimization (EMO) algorithms in neural architecture search (NAS), with ready to use test suites. It facilitates efficient performance assessments with NO requirement of GPUs or PyTorch/TensorFlow, enhancing accessibility for a broader range of research applications. It encompasses extensive test suites that cover a variety of datasets (CIFAR10, ImageNet, Cityscapes, etc.), search spaces (NASBench101, NASBench201, NATS, DARTS, ResNet50, Transformer, MNV3, MoSegNAS, etc.), and hardware devices (Eyeriss, GPUs, Samsung Note10, etc.). It provides a versatile interface compatible with multiple programming languages (Java, Matlab, Python, etc.).

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📢 Latest News & Updates

• EvoXBench has been updated to version 1.0.5! This latest release addresses bugs in CitySeg/MOP10 and HV calculation in the CitySeg/MOP test suite.

  If you're already onboard with EvoXBench, give this command a spin: pip install evoxbench==1.0.5.

⭐️ Key Features

📐 General NAS Problem Formulation

• Formulating NAS tasks into general multi-objective optimization problems.
• Exploring NAS's nuanced traits through the prism of evolutionary optimization.

🛠️ Efficient Benchmarking Pipeline

• Presenting an end-to-end worflow for instant benchmark assessments of EMO algorithms.
• Providing instant fitness evaluations as numerical optimization.

📊 Comprehensive Test Suites

• Encompassing a wide spectrum of datasets, search spaces, and hardware devices.
• Ready-to-use test multi-objective optimization suites with up to eight objectives.

Get Started

Tap the image to embark on the introductory video voyage.

Setup & Installation

1. Download requisite files:

   • database.zip via Google Drive or Baidu云盘（提取码：mhgs）

   • data.zip via Google Drive or Baidu云盘（提取码：9z45）

2. Run pip install evoxbench to get the benchmark.

3. Configure the benchmark:

    from evoxbench.database.init import config

    config("Path to database", "Path to data")
    # For instance:
    # With this structure:
    # /home/Downloads/
    # └─ database/
    # |  |  __init__.py
    # |  |  db.sqlite3
    # |  |  ...
    # |
    # └─ data/
    #    └─ darts/
    #    └─ mnv3/
    #    └─ ...
    # Then, execute:
    # config("/home/Downloads/database", "/home/Downloads/data")

About the Database

Explore our comprehensive database and understand its structure and content. Check it out here.

Community & Support

• Use the issue tracker for bugs or questions.
• Submit your enhancements through a pull request (PR).
• We have an active QQ group (ID: 297969717).
• Official Website: https://evox.group/

Sister Projects

• EvoX: A computing framework for distributed GPU-aceleration of evolutionary computation, supporting a wide spectrum of evolutionary algorithms and test problems. Check out here.

Citing EvoXBench

If you use EvoXBench in your research and want to cite it in your work, please use:

@article{EvoXBench,
  title={Neural Architecture Search as Multiobjective Optimization Benchmarks: Problem Formulation and Performance Assessment},
  author={Lu, Zhichao and Cheng, Ran and Jin, Yaochu and Tan, Kay Chen and Deb, Kalyanmoy},
  journal={IEEE Transactions on Evolutionary Computation},
  year={2023},
  publisher={IEEE}
}

Acknowledgements

A big shoutout to the following projects that have made EvoXBench possible:

NAS-Bench-101, NAS-Bench-201, NAS-Bench-301, NATS-Bench, Once for All, AutoFormer, Django, pymoo.