DVC, or Data Version Control, is an open-source version control tool for data science and machine learning projects. It helps teams manage datasets, model files, pipelines, metrics, and experiments using a Git-like workflow. DVC is useful because Git is excellent for code, but it is not designed to store large datasets, trained models, feature files, or binary artifacts. DVC keeps lightweight metadata in Git and stores heavy files in local or cloud remote storage. This makes ML projects easier to reproduce, share, audit, and roll back. <\/p>\n\n\n\n
TL;DR<\/strong><\/p>\n\n\n\n DVC, or Data Version Control, is an open-source version control system for ML datasets, model files, pipelines, and experiment outputs. It works with Git by storing lightweight metadata in the repository while keeping large files in local cache or remote storage like S3, Google Drive, Azure Blob Storage, or SSH. DVC helps teams track which data, code, parameters, and model outputs created a specific result. It also supports reproducible pipelines through dvc.yaml, dependency tracking, metrics comparison, experiment management, and remote collaboration.<\/p>\n\n\n\n Install DVC in your Python environment using pip.<\/a>.<\/p>\n\n\n\n For cloud storage support, install the required extra package. For example, use this command for Amazon S3 support.<\/p>\n\n\n\n This prepares your project to track datasets, models, and pipeline outputs.<\/p>\n\n\n\n Create a new ML project<\/a> folder and initialize Git.<\/p>\n\n\n\n Now initialize DVC.<\/p>\n\n\n\n This creates the .dvc directory and adds DVC configuration files to the project. Commit the initial setup to Git.<\/p>\n\n\n\n Create a dataset folder.<\/p>\n\n\n\n Place your dataset inside the data folder. For example:<\/p>\n\n\n\n Track the dataset with DVC.<\/p>\n\n\n\n DVC creates a small metadata file named data\/train.csv.dvc. The actual dataset is stored in the DVC cache, while Git tracks only the metadata file.<\/p>\n\n\n\n DVC remote storage is where large files are stored for sharing with teams or restoring later. DVC supports several storage types, including Amazon S3, SSH, Google Drive, Azure Blob Storage, NFS, and HDFS.<\/p>\n\n\n\n For local testing, create a local remote.<\/p>\n\n\n\n For an S3 remote, use:<\/p>\n\n\n\n Commit the remote configuration.<\/p>\n\n\n\n Upload the DVC-tracked dataset to the configured remote.<\/p>\n\n\n\n The dvc push command uploads tracked data from the local DVC cache to remote storage.<\/p>\n\n\n\n Push Git metadata separately.<\/p>\n\n\n\n This separation is important. Git stores<\/a> code and DVC metadata. DVC remote storage stores datasets, models, and large artifacts.<\/p>\n\n\n\n Clone the Git repository on another system.<\/p>\n\n\n\n Install DVC and pull the dataset.<\/p>\n\n\n\n The dvc pull command downloads tracked files from remote storage into the working directory.<\/p>\n\n\n\n This lets another developer reproduce the same dataset state without manually sharing large files.<\/p>\n\n\n\n Update the dataset when new rows, labels, or features are added.<\/p>\n\n\n\n Each dataset version is now linked to a Git commit. You can move between dataset versions using Git checkout and DVC pull.<\/p>\n\n\n\n This is useful when you need to compare model results across different dataset versions.<\/a><\/p>\n\n\n\n After training a model, store the output in a model folder.<\/p>\n\n\n\n Example model file:<\/p>\n\n\n\n Track the trained model with DVC.<\/p>\n\n\n\n This keeps large model binaries outside Git while keeping every model version traceable.<\/p>\n\n\n\n DVC can define ML pipeline<\/a> stages in dvc.yaml. A pipeline stage connects commands, dependencies, parameters, and outputs.<\/p>\n\n\n\n Example training stage:<\/p>\n\n\n\n Run the pipeline.<\/p>\n\n\n\n The dvc repro command checks what changed and reruns only the required pipeline stages. DVC uses dvc.yaml and dvc.lock to track pipeline structure and exact output states.<\/p>\n\n\n\n Create a params.yaml file for model parameters.<\/p>\n\n\n\n Create a metrics file after model evaluation.<\/p>\n\n\n\n Track metrics in Git.<\/p>\n\n\n\n DVC supports metrics in JSON<\/a>, YAML, and TOML formats, and these metrics are usually produced by training or evaluation code for experiment comparison.<\/p>\n\n\n\n\n
What is DVC?<\/strong><\/h2>\n\n\n\n
Top Benefits of DVC<\/strong><\/h2>\n\n\n\n
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\n DVC<\/strong> keeps your Git repo lightweight by storing only small metadata files (.dvc)<\/strong>, while the actual datasets and models stay in remote storage like S3<\/strong> or Google Drive<\/strong>.\n<\/div>\n\n\n\nKey Use Cases of DVC<\/strong><\/h2>\n\n\n\n
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DVC Data Version Control Tutorial: Key Steps<\/h2>\n\n\n\n
Step 1: Install DVC<\/h3>\n\n\n\n
pip install dvc\n<\/code><\/pre>\n\n\n\npip install \"dvc[s3]\"\n<\/code><\/pre>\n\n\n\nStep 2: Initialize Git and DVC<\/h3>\n\n\n\n
mkdir dvc-ml-project\ncd dvc-ml-project\ngit init\n<\/code><\/pre>\n\n\n\ndvc init\n<\/code><\/pre>\n\n\n\ngit add .dvc .gitignore\ngit commit -m \"Initialize DVC\"\n<\/code><\/pre>\n\n\n\n
\n With DVC<\/strong>, switching between dataset versions is as simple as a git checkout<\/strong> followed by dvc pull<\/strong>, with no manual file hunting required.\n<\/div>\n\n\n\nStep 3: Add a Dataset to DVC<\/h3>\n\n\n\n
mkdir data\n<\/code><\/pre>\n\n\n\ndata\/train.csv\n<\/code><\/pre>\n\n\n\ndvc add data\/train.csv\n<\/code><\/pre>\n\n\n\ngit add data\/train.csv.dvc data\/.gitignore\ngit commit -m \"Track training dataset with DVC\"\n<\/code><\/pre>\n\n\n\nStep 4: Configure Remote Storage<\/h3>\n\n\n\n
mkdir \/tmp\/dvcstore\ndvc remote add -d storage \/tmp\/dvcstore\n<\/code><\/pre>\n\n\n\ndvc remote add -d storage s3:\/\/mybucket\/dvcstore\n<\/code><\/pre>\n\n\n\ngit add .dvc\/config\ngit commit -m \"Configure DVC remote storage\"\n<\/code><\/pre>\n\n\n\nStep 5: Push Data to Remote Storage<\/h3>\n\n\n\n
dvc push\n<\/code><\/pre>\n\n\n\ngit push\n<\/code><\/pre>\n\n\n\nStep 6: Pull Data on Another Machine<\/h3>\n\n\n\n
git clone <repo-url>\ncd dvc-ml-project\n<\/code><\/pre>\n\n\n\npip install dvc\ndvc pull\n<\/code><\/pre>\n\n\n\nStep 7: Version Dataset Changes<\/h3>\n\n\n\n
dvc add data\/train.csv\ngit add data\/train.csv.dvc\ngit commit -m \"Update training dataset\"\ndvc push\n<\/code><\/pre>\n\n\n\ngit checkout <commit-id>\ndvc pull\n<\/code><\/pre>\n\n\n\nStep 8: Track ML Model Files<\/h3>\n\n\n\n
mkdir models\n<\/code><\/pre>\n\n\n\nmodels\/model.pkl\n<\/code><\/pre>\n\n\n\ndvc add models\/model.pkl\ngit add models\/model.pkl.dvc models\/.gitignore\ngit commit -m \"Track trained model with DVC\"\ndvc push\n<\/code><\/pre>\n\n\n\nStep 9: Create a Reproducible DVC Pipeline<\/h3>\n\n\n\n
dvc stage add -n train \\\n -d src\/train.py \\\n -d data\/train.csv \\\n -o models\/model.pkl \\\n python src\/train.py\n<\/code><\/pre>\n\n\n\ndvc repro\n<\/code><\/pre>\n\n\n\nStep 10: Track Metrics and Parameters<\/h3>\n\n\n\n
train:\n learning_rate: 0.01\n max_depth: 5\n n_estimators: 100\n<\/code><\/pre>\n\n\n\n{\n \"accuracy\": 0.91,\n \"f1_score\": 0.88\n}\n<\/code><\/pre>\n\n\n\ngit add params.yaml metrics.json\ngit commit -m \"Add training parameters and metrics\"\n<\/code><\/pre>\n\n\n\n