Metadata-Version: 2.1
Name: evoxbench
Version: 1.0.0
Summary: A benchmark for NAS algorithms
Home-page: https://github.com/EMI-Group/evoxbench
Author: EMI-Group
Author-email: emi-group@outlook.com
License: UNKNOWN
Project-URL: Bug Reports, https://github.com/EMI-Group/evoxbench/issues
Project-URL: Source, https://github.com/EMI-Group/evoxbench
Description: # Neural Architecture Search as Multiobjective Optimization Benchmarks: Problem Formulation and Performance Assessment [[arXiv]](https://arxiv.org/abs/2208.04321)
        
        ## Preparation Steps
        
        1. Download the following two requried files:
            - ``database.zip`` file
              from [Google Drive](https://drive.google.com/file/d/11bQ1paHEWHDnnTPtxs2OyVY_Re-38DiO/view?usp=sharing) 
              or [Baidu云盘（提取码：mhgs）](https://pan.baidu.com/s/1PwWloA543-81O-GFkA7GKg)
              
            - ``data.zip`` file
              from [Google Drive](https://drive.google.com/file/d/1_NsI-W40F9uh3deCg_z4W4Hme49rBznc/view?usp=sharing)
              or [Baidu云盘（提取码：idxw）](https://pan.baidu.com/s/1N7cbR2-Q757Yy2auG_ckfA?pwd=idxw)
              
        2. ``pip install evoxbench`` to install the benchmark.
        
        3. Configure the benchmark via the following steps:
         
        ```python
            from evoxbench.database.init import config
        
            config("Path to databae", "Path to data")
            # For example
            # If you have the following structure
            # /home/Downloads/
            # └─ database/
            # |  |  __init__.py
            # |  |  db.sqlite3
            # |  |  ...
            # |  
            # └─ data/
            #    └─ darts/
            #    └─ mnv3/
            #    └─ ...
            # Then you should do:
            # config("/home/Downloads/database", "/home/Downloads/data")
        ```
        
        ## Database
        
        Visit this webpage for more information: https://github.com/liuxukun2000/evoxdatabase
        
        ## Acknowledgement
        
        Codes are developed upon: [NAS-Bench-101](https://github.com/google-research/nasbench)
        , [NAS-Bench-201](https://github.com/D-X-Y/NAS-Bench-201), [NAS-Bench-301](https://github.com/automl/nasbench301)
        , [NATS-Bench](https://xuanyidong.com/assets/projects/NATS-Bench)
        , [Once for All](https://github.com/mit-han-lab/once-for-all)
        , [AutoFormer](https://github.com/microsoft/Cream/tree/main/AutoFormer), [Django](https://www.djangoproject.com/)
        , [pymoo](https://pymoo.org/) 
        
Keywords: benchmark,evolution algorithm,neural architecture search
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Software Development :: Build Tools
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3 :: Only
Requires-Python: >=3.7, <4
Description-Content-Type: text/markdown
