Awesome Iterative Projects

A list of projects relying on Iterative tools to achieve awesomeness.

Missing something awesome? Anyone is welcome to submit projects to this list.

Tools & Libraries

dvthis: Utility functions and project templates for DVC pipelines using R.
nvim-dvc: Neovim plugin for DVC.
COVID Genomics/Airflow-DVC: Airflow extension for DVC.
COVID Genomics/dvc-fs: High-level abstraction for DVC file manipulation (listing & I/O) with basic support for PyFilesystem2.
zincware/ZnTrack: Create, visualize, run & benchmark DVC pipelines in Python & Jupyter notebooks.
zincware/dask4dvc: Provides a DVC-like CLI that combines DVC with Dask Distributed to make it easier to use with HPC managers like Slurm

DVC Streamlit Example: Build a custom web UI with DVC and Streamlit for visually tracking & comparing model performance during R&D (adapted from TensorFlow's transfer learning tutorial).
DVC Pipelines and Experiments Tutorial: Build maintainable Machine Learning pipelines using DVC.
CD4ML Example: Example DVC setup with AWS S3 remote storage & GitLab CI/CD.
DVC with PyCaret & FastAPI: End-to-end demo of data & model tracking (DVC), remote storage (Azure), efficient experimentation (PyCaret) & model deployment (FastAPI).

Example-get-started: Train a sklearn random forest classifier for StackOverflow question tagging.
Example-DVC-experiments: Train a Tensorflow CNN classifier for Fashion-MNIST data; used in https://dvc.org/doc/start/experiments.
Example-versioning: Used in https://dvc.org/doc/use-cases/versioning-data-and-model-files/tutorial.
DVC-Checkpoints-MNIST: A showcase for different ways to use the checkpoints. Train a PyTorch classifier on a CSV MNIST dataset.
Scalable and Distributed ML Workflows with DVC + Ray on AWS: This tutorial introduces you to integrating DVC with Ray, turning them into your go-to toolkit for creating automated, scalable, and distributed ML pipelines.

LensKit/lk-demo-experiment: Demo DVC experiment pipeline (DAG) using multiple public datasets, preprocessing & training, and Jupyter notebooks.
ModelOriented/MAIR: Monitoring impact of AI regulations with a DVC pipeline.
Kaggle-Titanic-DVC: Survival analysis DVC experiment.
VQA-With-Multimodal-Transformers: Visual Question Answering task on the DAQUAR Dataset using multimodal transformer models with an experiment pipeline tracked in DVC Studio.
pinellolab/pyrovelocity: Pyro-Velocity is a Bayesian, generative, and multivariate RNA velocity model to estimate uncertainty in predictions of future cell states from minimal models approximating transcript splicing dynamics.

Barrak, A., Eghan, E.E. and Adams, B. On the Co-evolution of ML Pipelines and Source Code - Empirical Study of DVC Projects , in Proceedings of the 28th IEEE International Conference on Software Analysis, Evolution, and Reengineering, SANER 2021. Hawaii, USA.
Barreto Simedo Pacheco, L., Rahman, M., Rabbi, F., Fathollahzadeh, P., Abdellatif, A., Shihab, E., Chen, T.P., Yang, J., and Zou, Y. DVC in Open Source ML-development: The Action and the Reaction, In Proceedings of the IEEE/ACM 3rd International Conference on AI Engineering - Software Engineering for AI (CAIN '24). Lisbon, Portugal.