Data Version Control for Machine Learning

Version your data and models, build reproducible ML pipelines, and track experiments — all with Git-like commands you already know.

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Quick Start

Get up and running with DVC in minutes

Install DVC using your preferred package manager: ```bash pip theme={null} pip install dvc ``` ```bash brew theme={null} brew install dvc ``` ```bash conda theme={null} conda install -c conda-forge dvc ``` Navigate to your Git repository and initialize DVC: ```bash theme={null} cd your-ml-project git init dvc init git commit -m "Initialize DVC" ``` This creates a `.dvc` directory with DVC configuration files. Start versioning your data with DVC: ```bash theme={null} dvc add data/dataset.csv git add data/dataset.csv.dvc data/.gitignore git commit -m "Add dataset to DVC" ``` DVC creates a `.dvc` file that tracks your data, while the actual data is stored in the DVC cache. Configure remote storage to share data with your team: ```bash theme={null} dvc remote add -d myremote s3://mybucket/dvcstore dvc push ``` Now your data is backed up and can be accessed by anyone on your team.

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Learn about DVC's core features and capabilities

Store and version your datasets and models alongside your Git repository Build reproducible workflows that connect data, code, and models Run, compare, and manage hundreds of ML experiments locally Back up data to S3, GCS, Azure, SSH, and more cloud providers

User guides

Practical guides for common DVC workflows

Learn how to version datasets and models with DVC Create reproducible ML pipelines with stages Track hyperparameters and compare experiment results Set up cloud storage for data and model sharing Work with your team using Git and DVC together Access DVC functionality programmatically

Command reference

Complete reference for all DVC commands

Data management

Commands for tracking, pushing, pulling, and managing data files

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Pipeline commands

Build and run reproducible ML pipelines and workflows

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Experiment commands

Track, compare, and manage ML experiments locally

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Metrics & parameters

Track and compare hyperparameters, metrics, and plots

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Ready to get started?

Start versioning your data and models, building reproducible ML pipelines, and tracking experiments with DVC today.

Install DVC View on GitHub