DVC combines a number of existing ideas into a single tool, with the goal of
bringing best practices from software engineering into the data science field
(refer to What is DVC? for more details).
- DVC builds upon Git by introducing the concept of data files – large files
that should not be stored in a Git repository, but still need to be tracked
and versioned. It leverages Git's features to enable managing different
versions of data, data pipelines, and experiments.
- DVC is not fundamentally bound to Git, and can work without it (except
Git-LFS (Large File Storage)
- DVC does not require special servers like Git-LFS demands. Any cloud storage
like S3, Google Cloud Storage, or even an SSH server can be used as a
remote storage. No additional databases,
servers, or infrastructure are required.
- DVC does not add any hooks to the Git repo by default (although they are
- Git-LFS was not made with data science in mind, so it doesn't provide related
features (e.g. pipelines,
- GitHub (most common Git hosting service) has a limit of 2 GB per repository.
- DVC can use reflinks* or hardlinks (depending on the system) instead of
symlinks to improve performance and the user experience.
- Git-annex is a datafile-centric system whereas DVC focuses on providing a
workflow for machine learning and reproducible experiments. When a DVC or
Git-annex repository is cloned via
git clone, data files won't be copied to
the local machine, as file contents are stored in separate
remotes. With DVC however,
which provide the reproducible workflow, are always included in the Git
repository. Hence, they can be executed locally with minimal effort.
- DVC optimizes file hash calculation.
* copy-on-write links or "reflinks" are a relatively new way to link
files in UNIX-style file systems. Unlike hardlinks or symlinks, they support
transparent copy on write. This
means that editing a reflinked file is always safe as all the other links to
the file will reflect the changes.
Git workflows/methodologies such as Gitflow
- DVC enables a new experimentation methodology that integrates easily with
existing Git workflows. For example, a separate branch can be created for each
experiment, with a subsequent merge of the branch if the experiment is
- DVC innovates by giving users the ability to easily navigate through past
experiments without recomputing them each time.
Workflow management systems
Pipelines and dependency graphs
(DAG) such as Airflow,
- DVC is focused on data science and modeling. As a result, DVC pipelines are
lightweight and easy to create and modify. However, DVC lacks advanced
pipeline execution features like execution monitoring, error handling, and
dvc is purely a command line tool without a graphical user interface (GUI)
and doesn't run any daemons or servers. Nevertheless, DVC can generate images
with pipeline and experiment workflow visualizations.
- See also our sister project, CML, that helps fill some of
Experiment management software
- DVC uses Git as the underlying version control layer for data, pipelines, and
experiments. Data versions exist as metadata in Git, as opposed to using
external databases or APIs, so no additional services are required.
- DVC doesn't need to run any services. There's no GUI as a result, but we
expect some GUI services will be created on top of DVC.
- DVC can generate images with experiment workflow
- DVC has transparent design. Its
internal files and directories
have a human-readable format and can be easily reused by external tools.
Make and others.
* Inodes are metadata file records to locate and store permissions to the
actual file contents. See Linking files in
this doc for
technical details (Linux).