Code arch

├── .run/
├── docs/
├── examples/
├── gtable/
├── .gitignore
├── gtable.yml
├── LICENSE
├── Makefile
├── README.md
├── requirements.txt
├── generation.py
└── evaluate.py

• .run/ : This directory holds shell scripts and Pycharm run configurations for training and testing.
• docs/ : The documents provided by this project
• examples/ : A list of datasets, and their correpsonding YML configurations files. The output of models also will be saved into the corresponding dataset folder.
• gtable/ : The directory contains source code, but now there are subdirectories.
• .gitignore : The configuration file for git command
• gtable.yml : A conda config file for this project
• LICENSE : The license for this project
• Makefile : A makefile to compile this project
• README.md : A readme file
• requirements.txt : A list of required python packages
• generation.py : A python run file for training and generate process
• evaluate.py : A python run file for evaluating performance between real and fake datasets

Software Package Installation and Configuration

1. Install CUDA and GPU Driver if using GPU (We are using cuda-10.1)

2. Install latest miniconda as Python virtual environment management

3. Create a conda environment using a YML config file:

conda env create --name "gtable" -f gtable.yml

or 

make

4. Activate Python Virtual Environment

conda activate gtable

5. Generate dependency yaml file:

conda env export > gtable.yml

A few other frequently used commands

# list all the conda environment available
conda info --envs  
# Create new environment named as `envname`
conda create --name envname
# Remove environment and its dependencies
conda remove --name envname --all
# Clone an existing environment
conda create --name clone_envname --clone envname

How to run training process

python generation.py --help
python generation.py -config examples/adults/config/adult_1_1.yml

How to run evaluate process

python evaluate.py --help
python evaluate.py -config examples/adults/config/adult_1_1.yml