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DVCLive is a Python library for logging machine learning metrics and other metadata in simple file formats, which is fully compatible with DVC.

• Get Started
• How it Works
• API Reference

You can install dvclive via pip from PyPI:

$ pip install dvclive

Depending on the ML framework you plan to use to train your model, you might need to specify one of the optional dependencies: mmcv, tf, xgb. Or all to include them all. For example, for TensorFlow the command should look like this:

pip install dvclive[tf]

TensorFlow and its dependencies will be installed automatically.

To install the development version, run:

pip install git+https://github.com/iterative/dvclive

DVCLive is an ML Logger, similar to:

• MLFlow
• Weights & Biases
• Neptune

The main difference with those ML Loggers is that DVCLive does not require any additional services or servers to run.

Logged metrics and metadata are stored as plain text files that can be versioned by version control tools (i.e Git) or tracked as pointers to files in DVC storage.

Contributions are very welcome. To learn more, see the Contributor Guide.

Distributed under the terms of the Apache 2.0 license, dvclive is free and open source software.

If you encounter any problems, please file an issue along with a detailed description.