Generalized GAN Pipeline

An ML / MLOps project implementing a streamlined system design for a train-test-deploy pipeline for various types of GANs (Generative Adversarial Networks). This project uses DVC for internal pipelining and GitHub Actions to enable CI/CD for the trained and tested models.

DAGsHub Link (for experimentation and pipelining): Click Here!

To build from source

1. Install Git and DVC.

2. Clone the repository.

   git clone https://github.com/swarajpande5/generalized-gan-pipeline.git
   
   cd generalized-gan-pipeline/
   

3. Set up virtual environment for python.

   pip install virtualenv
   
   virtualenv venv/
   
   source venv/bin/activate
   
   pip install -r requirements.txt
   

4. Make suitable changes to code/ directory and the Dockerfile in the project. If needed, changes can also be made to the pipeline itself by changing the dvc.* files using dvc commands.

5. Run the following command to execute the pipeline after making changes to the code/ scripts.

   dvc repro
   

6. Deactivate the virtual environment.

   deactivate
   

Files and Directory Structure

.
├── .github/workflows
    ├── pipeline-run.yaml           // Enables to run pipeline as a GitHub Action on Push
    └── push-image-on-release.yaml  // Enables to build and release a Container Image on DockerHub on Release
├── code
    ├── eval.py                     // Evaluation metrics script
    ├── featurization.py            // Featurization script
    ├── get_data.py                 // Fetches the datasets
    ├── model_class.py              // Model Class script
    └── train_model.py              // Trains the model instance
├── data
    ├── DVC data and related files   
├── metrics 
    ├── eval.json                   // Evaluation metrics for pipeline                       
    └── train_metric.json           
├── notebook (Optional)
    └── notebook.ipynb              // Jupyter Notebook 
├── dvc.lock
├── dvc.yaml
├── requirements.txt
└── README.md