DVCLive
DVCLive is a free Python API module for recording machine-learning parameters, metrics, artifacts, images, and plots.
What DVCLive is
DVCLive is a free Python API module for recording machine-learning parameters, metrics, artifacts, images, and plots. It saves run data in a `dvclive` directory and tracks it as a DVC experiment, where runs can be analyzed and compared. Tracking uses Git and DVC rather than a central database. Large artifacts can be cached with DVC, with metadata files tracked in Git, and optional system monitoring records GPU, CPU, RAM, and disk usage. DVCLive works with frameworks including PyTorch, TensorFlow, Hugging Face, Keras, Scikit-learn, XGBoost, and others; optional dependencies are needed for integrations and some image, plot, and report features. It can also run within DVC pipelines for structured experiments. The latest version requires Python 3.9 or later. Note that each run uses the same save path and replaces the previous results there. The project is open source, and help is available through DVC’s Discord, GitHub issues, and forum.
Good
- Free and open source.
- Compares runs as DVC experiments.
- Optional GPU, CPU, RAM, and disk monitoring.
- Supports numerous machine-learning frameworks.
- Can be used in DVC pipelines.
Watch out
- Latest version requires Python 3.9 or later.
- Each run overwrites results at the same path.
- Some features require optional dependencies.
Who it suits
DVCLive suits Python machine-learning practitioners who want experiment tracking tied to Git and DVC, including users running structured experiments in DVC pipelines. It is a fit for those comfortable managing optional dependencies and the shared run path.
Verdict
DVCLive offers experiment logging and comparison through Git and DVC, with integrations for many ML frameworks. Plan around its Python requirement, optional dependencies, and run-path overwrites.
Plans and prices
From DVCLive's own pricing page, checked 7 Oct 2026.
| Plan | Price | What it gives you |
|---|---|---|
| DVCLive | Free | Open source Python library · Python 3.9+ for latest version |
The facts
Each one read from DVCLive's own pages, with the date it was checked.
What it does
- DVC pipeline use
- DVCLive can be used with DVC pipelines to run structured and reproducible ML experiments.checked 7 Oct 2026
- Experiment tracking
- Logged data is stored in a dvclive directory and tracked as a DVC experiment for analysis and comparison.checked 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.checked 7 Oct 2026
- Large artifacts
- Logging an artifact can cache the file with DVC and create a .dvc metadata file tracked in Git.checked 7 Oct 2026
- Notable behavior
- DVCLive saves each run to the same path and overwrites previous results there.checked 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.checked 7 Oct 2026
- Optional dependencies
- Install extras for image logging, plots, Scikit-learn plots, Markdown reports, and supported ML framework integrations.checked 7 Oct 2026
- Purpose
- DVCLive is a Python API module for logging machine learning parameters, metrics, artifacts, images, and plots.checked 7 Oct 2026
- Python requirement
- Python 3.9+ is required for the latest version of DVCLive.checked 7 Oct 2026
- Stewardship
- lakeFS says it has taken over stewardship and active development of DVC and is committed to maintaining DVCLive.checked 7 Oct 2026
- Support
- DVC directs users to its Discord community, GitHub issues, and forum for help and feedback.checked 7 Oct 2026
- System metrics
- When enabled, system monitoring logs GPU, CPU, RAM, and disk usage.checked 7 Oct 2026
- Version control
- DVCLive relies on Git and DVC for experiment tracking instead of a central database.checked 7 Oct 2026
The company
- Founded
- 2018checked 28 Sep 2026
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