dvc

Initialize DVC repository

$ dvc init

$ dvc add [data/dataset.csv]

$ dvc push

$ dvc pull

$ dvc repro

$ dvc dag

$ dvc remote add -d [myremote] [s3://bucket/path]

dvc command [options]

DVC (Data Version Control) is a version control system for machine learning projects. It tracks large files, datasets, and models alongside Git, without storing them in the Git repository.
DVC stores file metadata (.dvc files) in Git while the actual data goes to configurable remote storage (S3, GCS, Azure, SSH, etc.). This enables versioning large files and sharing datasets across teams.
The pipeline feature defines reproducible ML workflows, tracking dependencies and outputs for experiment management.

COMMAND

DVC operation to perform.

Initialize DVC in repository.

Track file or directory.

Upload tracked data to remote.

Download tracked data from remote.

Reproduce pipeline.

Add remote storage.

Display help information.

.dvc/config

Repository-level DVC configuration including remote storage settings.

Global user configuration for DVC defaults and preferences.

Local repository config for machine-specific settings not committed to Git.

Requires Git repository. Large data transfers depend on network speed. Remote storage may incur costs. Pipeline reproduction needs matching environment.

DVC was created by iterative.ai and released in 2017. It addresses the challenge of versioning large datasets and ML models that don't fit well in Git, enabling reproducible machine learning workflows.

git(1), mlflow(1)