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API Status Changelog

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DVC

#15in Version Control

Overview Discussions1 Adoption

What is DVC?

It is an open-source Version Control System for data science and machine learning projects. It is designed to handle large files, data sets, machine learning models, and metrics as well as code.

DVC is a tool in the Version Control category of a tech stack.

Key Features

Git-compatibleStorage agnosticReproducibleLow friction branchingMetric trackingML pipeline frameworkLanguage- & framework-agnosticHDFS, Hive & Apache SparkTrack failures

DVC Pros & Cons

Pros of DVC

✓Full reproducibility

Cons of DVC

✗Coupling between orchestration and version control
✗Doesn't scale for big data
✗Requires working locally with the data

DVC Alternatives & Comparisons

What are some alternatives to DVC?

Git

Git is a free and open source distributed version control system designed to handle everything from small to very large projects with speed and efficiency.

SVN (Subversion)

Subversion exists to be universally recognized and adopted as an open-source, centralized version control system characterized by its reliability as a safe haven for valuable data; the simplicity of its model and usage; and its ability to support the needs of a wide variety of users and projects, from individuals to large-scale enterprise operations.

Mercurial

Mercurial is dedicated to speed and efficiency with a sane user interface. It is written in Python. Mercurial's implementation and data structures are designed to be fast. You can generate diffs between revisions, or jump back in time within seconds.

DVC Integrations

Google Cloud Storage, Amazon S3, Google Drive, PyTorch, Git and 7 more are some of the popular tools that integrate with DVC. Here's a list of all 12 tools that integrate with DVC.

Google Cloud Storage

Continuous Machine Learning

Replicate

It lets you run machine learning models with a few lines of code, without needing to understand how machine learning works.

Plastic SCM

Plastic SCM is a distributed version control designed for big projects. It excels on branching and merging, graphical user interfaces, and can also deal with large files and even file-locking (great for game devs). It includes "semantic" features like refactor detection to ease diffing complex refactors.

isomorphic-git

It is a pure JavaScript reimplementation of git that works in both Node.js and browser JavaScript environments. It can read and write to git repositories, fetch from and push to git remotes (such as GitHub), all without any native C++ module dependencies.

Try It

Adoption

On StackShare

DVC Discussions

Discover why developers choose DVC. Read real-world technical decisions and stack choices from the StackShare community.

Jun 17, 2021

Needs adviceon

I already use DVC to keep track and store my datasets in my machine learning pipeline. I have also started to use MLflow to keep track of my experiments. However, I still don't know whether to use DVC for my model files or I use the MLflow artifact store for this purpose. Or maybe these two serve different purposes, and it may be good to do both! Can anyone help, please?

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