[Data Version Control](https://dvc.org/) ([[DVC]]) is an open-source tool designed to make machine learning (ML) projects more manageable by applying version control systems to data science workflows. It builds on Git's versioning capabilities to handle large data files, models, and experiments in ML projects, which Git is not originally designed to manage efficiently due to their size and binary format. Also see [[MLEM]] and [[DagsHub]]. ### Key Features of DVC: 1. **Data Management**: - DVC provides mechanisms for versioning datasets and machine learning models, allowing users to track iterations over time. It enables data files to be stored on various storage systems (like S3, GCS, or NFS) while maintaining metadata and version information in Git. 2. **Reproducibility**: - By linking code and data together, DVC ensures that experiments are reproducible. You can capture all the stages in your pipeline using DVC, making it possible to reproduce any experiment and trace back to see what data, parameters, and code affected the final result. 3. **Pipeline Versioning**: - DVC allows you to define stages in a ML pipeline, and each stage's dependencies and outputs can be tracked. This means changes to data or parameters automatically propagate through the dependent stages when the pipeline is re-run. 4. **Experiment Management**: - It supports tracking ML experiments by logging code, data used, and the computational environment. DVC helps compare different experiments alongside Git version history, making it easier to iterate over models. 5. **Performance Metrics**: - DVC provides commands to track, compare, and visualize performance metrics of different ML models. 6. **Remote Storage Management**: - DVC supports the configuration of multiple remote storage locations for your data, facilitating efficient data sharing and backup among team members, without clogging the Git repository with large files. ### How DVC Works: - **Tracking Data and Models**: DVC adds a layer on top of a Git repository, enabling the handling of large files without storing the file contents in Git. This is done through pointers in the Git repository to the actual data files, which are stored in a separate DVC storage. - **Creating Data Pipelines**: You can describe your data processing stages and their order, capturing the intermediate and final outputs. This helps in creating reproducible and automated workflows. - **Experiment Tracking**: When experimenting with different models and parameters, DVC helps manage the experiments and keep track of all the changes. It also aids in easily switching back and forth between different versions of data and code. ### Common Uses: - **Machine Learning Projects**: DVC is primarily used in ML projects to handle large data sets and models. It is instrumental in teams where different members might be working on different experiments simultaneously. - **Data Versioning in Teams**: For teams working on data analysis or data science projects, DVC helps in managing versions of datasets and models just like source code. - **Collaboration**: By storing data in cloud storage and keeping metadata in Git, DVC makes collaboration more manageable by ensuring team members can access consistent and updated versions of datasets and models. DVC is particularly beneficial for data scientists and machine learning engineers looking to apply software engineering practices like version control, continuous integration, and automated testing to their data-driven projects. This tool significantly enhances organization, tracking, and collaboration in projects, addressing some of the most pressing challenges in managing machine learning projects effectively. # References ```dataview Table title as Title, authors as Authors where contains(subject, "Data Version Control") or contains(subject, "DVC") or contains(subject, "Version Control") sort title, authors, modified, desc ```