DVC is a powerful tool designed to help developers create reproducible machine learning projects. It allows you to version your data and models, store them in cloud storage while keeping their versioning information in your Git repository. DVC also enables you to iterate quickly with lightweight pipelines, only running the necessary steps when changes are made. You can track experiments in your local Git repo without the need for servers, and easily compare and share experiments with others. DVC works seamlessly with Git to store and version code, while storing data and model files in a separate cache. The tool also supports various remote storage platforms for data sharing and backup. DVC’s features can be compared to Git for data, Makefiles for ML, and local experiment tracking. There is also a VS Code Extension available for a GUI experience. Installation instructions are provided for different platforms, and the DVC community is active and supportive. Contributions to the project are welcome and the tool is licensed under the Apache license version 2.0.
https://github.com/iterative/dvc