DVCLive
What it is
DVCLive is a Python API module for logging machine learning parameters, metrics, artifacts, images, and plots. It saves run data in a dvclive directory and tracks it as a DVC experiment for analysis and comparison. Instead of using a central database, experiment tracking relies on Git and DVC. Large artifacts can be cached with DVC, with a .dvc metadata file tracked in Git. Optional system monitoring records GPU, CPU, RAM, and disk usage.
DVCLive lists integrations for frameworks including Fast.ai, Hugging Face, Keras, PyTorch, TensorFlow, Scikit-learn, and XGBoost, among others; optional dependencies are needed for framework integrations and several logging and reporting features. The latest version requires Python 3.9 or later. Each run writes to the same path, overwriting prior results there. It can also be used in DVC pipelines for structured experiments. The plan is listed as free, and DVC says the project will remain open source without paywalled features.
For and against
For
- Logs parameters, metrics, artifacts, images, and plots.
- Supports run analysis and comparison as DVC experiments.
- Can cache large artifacts with DVC.
- Optional monitoring records GPU, CPU, RAM, and disk use.
- Can run in DVC pipelines.
Against
- Latest version requires Python 3.9 or later.
- Each run overwrites results at the same path.
- Some logging and framework integrations require optional dependencies.
The verdict
DVCLive offers experiment logging and comparison within a Git-and-DVC workflow, with optional system monitoring and framework integrations. Account for the shared output path, which overwrites previous results, when organizing runs.
Who it suits
DVCLive suits Python-based machine learning practitioners who track experiments with Git and DVC. It may also fit teams using DVC pipelines or the listed framework integrations.
Plans and prices
DVCLive
Open source Python library · Python 3.9+ for latest version
Read 7 Oct 2026 · s.dvc.org
On the record
- DVC pipeline use
- DVCLive can be used with DVC pipelines to run structured and reproducible ML experiments.s.dvc.org · read 7 Oct 2026
- Experiment tracking
- Logged data is stored in a dvclive directory and tracked as a DVC experiment for analysis and comparison.doc.dvc.org · read 7 Oct 2026
- Framework integrations
- The supported ML frameworks listed include Fast.ai, Hugging Face, Keras, LightGBM, MMCV, Optuna, PyTorch, PyTorch Lightning, Scikit-learn, TensorFlow, XGBoost, and Ultralytics YOLO v8.origin-doc.dvc.org · read 7 Oct 2026
- Large artifacts
- Logging an artifact can cache the file with DVC and create a .dvc metadata file tracked in Git.s.dvc.org · read 7 Oct 2026
- Notable behavior
- DVCLive saves each run to the same path and overwrites previous results there.s.dvc.org · read 7 Oct 2026
- Open source status
- DVC says it will remain 100% open source under the same license, with no plans to paywall features or restrict access.dvc.org · read 7 Oct 2026
- Optional dependencies
- Install extras for image logging, plots, Scikit-learn plots, Markdown reports, and supported ML framework integrations.s.dvc.org · read 7 Oct 2026
- Purpose
- DVCLive is a Python API module for logging machine learning parameters, metrics, artifacts, images, and plots.doc.dvc.org · read 7 Oct 2026
- Python requirement
- Python 3.9+ is required for the latest version of DVCLive.s.dvc.org · read 7 Oct 2026
- Stewardship
- lakeFS says it has taken over stewardship and active development of DVC and is committed to maintaining DVCLive.dvc.org · read 7 Oct 2026
- Support
- DVC directs users to its Discord community, GitHub issues, and forum for help and feedback.dvc.org · read 7 Oct 2026
- System metrics
- When enabled, system monitoring logs GPU, CPU, RAM, and disk usage.origin-doc.dvc.org · read 7 Oct 2026
- Version control
- DVCLive relies on Git and DVC for experiment tracking instead of a central database.s.dvc.org · read 7 Oct 2026
