Implementation of DE for NAS, benchmarked in the paper Differential Evolution for Neural Architecture Search accepted to the 1st NAS Workshop at ICLR 2020.

To access the paper:

• updated Arxiv version of the main paper
• ICLR 2020 version + slides + videos

To cite the paper or code:

@inproceedings{awad-iclr20,
  author    = {N. Awad and N. Mallik and F. Hutter},
  title     = {Differential Evolution for Neural Architecture Search},
  booktitle = {Proceedings of the 1st workshop on neural architecture search(@{ICLR}'20)},
  year      = {2020},
  month     = apr
}

To contact authors for queries reqarding the paper:

• Neeratyoy Mallik ([email protected])
• Noor Awad ([email protected])

Refer here (the parallel implementation of DE is also available here).

Refer here.

The instructions to setup the benchmarks can be found here.

To run DE on NAS-Bench-101 or NAS-HPO-Bench:

PYTHONPATH=$PWD python3 denas/examples/nas101/run_de_nas101.py

To run DE on NAS-Bench-1shot1:

PYTHONPATH=$PWD python3 denas/examples/nas1shot1/run_de_nas1shot1.py

To run DE on NAS-Bench-201:

PYTHONPATH=$PWD python3 denas/examples/nas201/run_de_nas201.py

Plots can be generated in a similar way, by passing the directory of the stored output files. For example:

PYTHONPATH=$PWD python3 denas/utils/plot_regret.py --path denas/examples/results/cifara

PYTHONPATH=$PWD python3 denas/utils/plot_cdf.py --path denas/examples/results/nas101/cifara

For the above plotting script to work with the output of NAS-Bench-1shot1, the output files need to be additionally preprocessed, for example:

PYTHONPATH=$PWD python3 denas/utils/convert_files.py --path denas/examples/results/nas1shot1/ --ssp 1