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. InstallGitandDVC.

2. Clone the repository.

   git clone https://github.com/swarajpande4/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 tocode/directory and theDockerfilein the project. If needed, changes can also be made to the pipeline itself by changing thedvc.*files using dvc commands.

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