├── .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
-
Install CUDA and GPU Driver if using GPU (We are using cuda-10.1)
-
Install latest miniconda as Python virtual environment management
-
Create a conda environment using a YML config file:
conda env create --name "gtable" -f gtable.yml
or
make
- Activate Python Virtual Environment
conda activate gtable
- 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
python generation.py --help
python generation.py -config examples/adults/config/adult_1_1.yml
python evaluate.py --help
python evaluate.py -config examples/adults/config/adult_1_1.yml