Using DVC for Version Control and Experiment Management in Machine Learning Projects

DVC for Tracking ML Models and Datasets

DVC was created to make machine‑learning models shareable and reproducible, handling large files, datasets, models, metrics, and code.

ML Project Version Control

Version control for ML models, datasets, and intermediate files is achieved by linking them through code, with storage options including Amazon S3, Azure Blob, Google Drive, Google Cloud Storage, Aliyun OSS, SSH/SFTP, HDFS, HTTP, network‑mounted storage, or even optical media.

Complete code and data provenance helps trace the full evolution of each model, ensuring reproducibility and easy switching between experiments.

ML Experiment Management

Leverage the full power of Git branches to try different ideas instead of ad‑hoc file suffixes and comments, and use automatic metric tracking instead of paper‑and‑pencil notes.

DVC is designed to keep branching as simple and fast as Git, regardless of data size, providing clean project structure, easy comparison of ideas, and cached intermediate artifacts to speed up iteration.

Deployment and Collaboration

Use push/pull commands to move consistent models, data, and code packages to production, remote machines, or teammates' computers, replacing ad‑hoc scripts.

DVC introduces lightweight pipelines as first‑class citizens in Git, language‑agnostic, connecting multiple steps into a DAG to reduce friction in moving code to production.

Features

Git Compatibility

DVC runs on top of any Git repository and works with any standard Git server or provider (GitHub, GitLab, etc.). Data file contents can be shared via network‑accessible storage or any supported cloud solution, offering all benefits of a distributed version‑control system without locks.

Storage Agnostic

Supports Amazon S3, Azure Blob, Google Drive, Google Cloud Storage, Aliyun OSS, SSH/SFTP, HDFS, HTTP, network‑mounted storage, and optical media, with the list of remote storages continuously expanding.

Reproducibility

A single dvc repro command reproduces an experiment end‑to‑end by consistently maintaining the combination of input data, configuration, and the original code.

Low‑Friction Branching

DVC fully supports instant Git branching even for large files, reflecting the non‑linear, highly iterative nature of ML work; a single data version can belong to dozens of experiments, enabling rapid creation and switching of many experiment histories.

Metric Tracking

Metrics are first‑class citizens in DVC; a command lists all branches and their metrics to monitor progress or select the best version.

ML Pipeline Framework

DVC provides a built‑in way to connect ML steps into a DAG and run the entire pipeline end‑to‑end, caching intermediate results so unchanged steps are skipped.

Language & Framework Agnostic

Works with any programming language or library—Python, R, Julia, Scala Spark, custom binaries, notebooks, flat files, TensorFlow, PyTorch, etc.—as reproducibility and pipelines rely only on input and output files or directories.

HDFS, Hive and Apache Spark

DVC can include Spark and Hive jobs in its data‑version‑control cycle or manage them end‑to‑end, breaking heavy cluster jobs into smaller DVC pipeline steps to dramatically reduce feedback loops.

Failure Tracking

Recording failed attempts helps generate new ideas; DVC provides a reproducible, easily accessible way to track everything.

Use Cases

Save and Reproduce Your Experiments

At any time retrieve the full content of your or a teammate's experiment; DVC guarantees all files and metrics are consistent and can be copied to serve as a baseline for new iterations.

Version Control Models and Data

DVC stores meta‑files in Git (instead of Google Docs) to describe and control dataset and model versions, supporting multiple external storage types as remote caches for large files.

Build Workflows for Deployment and Collaboration

DVC defines rules and processes for teams to work efficiently and consistently, serving as a protocol for collaboration, result sharing, and retrieving/running completed models in production.

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