Quan Minh Phan, Ngoc Hoang Luong

• Clone this repo:

$ git clone https://github.com/ELO-Lab/ENAS_TFI
$ cd ENAS_TFI

• Install dependencies:

$ pip install -r requirements.txt

• Download data in here and put into data folder

• For single-objective NAS problems:

python main.py  --n_runs 21 --warm_up 0 --nSamples_for_warm_up 0 --problem_name [problem_name] --algorithm_name GA --seed 0

• For multi-objective NAS problems:

python main.py  --n_runs 21 --warm_up 0 --nSamples_for_warm_up 0 --problem_name [problem_name] --algorithm_name NSGA-II --seed 0

--problem_name [problem_name] receives one of following values:

To search with the Warmup method, set --warm_up 1 and set the number of samples --nSamples_for_warm_up. In our experiments, we set --nSamples_for_warm_up 500.

To experiment with the different population_size or maximum_number_of_evaluations, set another value in main.py (for population_size) and factory.py (for maximum_number_of_evaluations)

• For single-objective NAS problems:

python visualize_so.py  --path_results [path_results]

• For multi-objective NAS problems:

python visualize_mo.py  --path_results [path_results]

For example: python visualize_mo.py --path_results .\results\MO-NAS101

Note: [path_results] must only contains results of experiments are conducted on the same problem.

Our source code is inspired by:

• pymoo: Multi-objective Optimization in Python
• NSGA-Net: Neural Architecture Search using Multi-Objective Genetic Algorithm
• NAS-Bench-101: Towards Reproducible Neural Architecture Search
• NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search
• How Powerful are Performance Predictors in Neural Architecture Search?