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Data Version Control or DVC is an open source tool for data science projects. It helps data scientists manage their code and data together in a simple form of Git-like commands.
Get started
| Step | Command |
|---|---|
| Track code and data together | $ git add train.py $ dvc add images.zip |
| Connect code and data by commands | $ dvc run -d images.zip -o images/ unzip -q images.zip $ dvc run -d images/ -d train.py -o model.p python train.py |
| Make changes and reproduce | $ vi train.py $ dvc repro |
| Share code | $ git add . $ git commit -m 'The baseline model' $ git push |
| Share data and ML models | $ dvc config AWS.StoragePath mybucket/image_cnn $ dvc push |
See more in tutorial.
Installation
Packages
Operating system dependent packages are the recommended way to install DVC. The latest version of the packages can be found at GitHub releases page: https://github.com/dataversioncontrol/dvc/releases
Python Pip
DVC could be installed via the Python Package Index (PyPI).
pip install dvc
Links
Website: https://dataversioncontrol.com
Tutorial: https://blog.dataversioncontrol.com/data-version-control-tutorial-9146715eda46
Documentation: http://dataversioncontrol.com/docs/
Discussion: https://discuss.dataversioncontrol.com/
How DVC works
Copyright
This project is distributed under the Apache license version 2.0 (see the LICENSE file in the project root).
By submitting a pull request for this project, you agree to license your contribution under the Apache license version 2.0 to this project.