# GraphPNAS
Code release for the paper

GraphPNAS: Learning Distribution of Good Neural Architectures via Deep Graph Generative Models 

## Dependencies
Python 3, PyTorch(1.5.0)

Other dependencies can be installed via 

  ```pip install -r requirements.txt```

## Preparation
put nasbench101 file to ./data/nasbench_only108.tfrecord
unzip nasbench201 .tar files to data folder: ./data/nasbench_201

## Run Demos
To reproduce NB101 results from paper:
  ```python run_nas_multiple.py -c config/nb101.yaml```

To reproduce NB201 results from paper:
  ```python run_nas_multiple.py -c config/nb201_*.yaml```

To run neural architecture search:

  ```python run_nas.py -c config/nas.yaml```

To test a final architecture

  ```python run_final.py -c config/final.yaml```
  
A description of the hyperparameters is included in their respective yaml files.
