Git for data scientists - manage your code and data together
Project description
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 model.p.dvc
|
Share code |
$ git add .
$ git commit -m 'The baseline model'
$ git push
|
Share data and ML models |
$ dvc remote add myremote s3://mybucket/image_cnn
$ dvc core.remote myremote
$ 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/iterative/dvc/releases
Python Pip
DVC could be installed via the Python Package Index (PyPI).
pip install dvc
Homebrew (Mac OS)
Formula:
brew install iterative/homebrew-dvc/dvc
Cask:
brew cask install iterative/homebrew-dvc/dvc
Links
Website: https://dvc.org
Tutorial: https://dvc.org/doc/tutorial
Documentation: http://dvc.org/doc
Discussion: https://discuss.dvc.org/
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.
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