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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!
Clone the repository.
git clone https://github.com/swarajpande5/generalized-gan-pipeline.git
cd generalized-gan-pipeline/
Set up virtual environment for python.
pip install virtualenv
virtualenv venv/
source venv/bin/activate
pip install -r requirements.txt
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.
Run the following command to execute the pipeline after making changes to the code/
scripts.
dvc repro
Deactivate the virtual environment.
deactivate
.
├── .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
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