dvclive 0.2.1

Metric logger for ML projects.

DVCLive

• Docs

DVCLive is an open-source library for monitoring the progress of metrics during training of machine learning models. It’s built with Git and MLOps principles in mind:

1. Codification of data. Tracked metrics are stored in readable text files that can be versioned by Git or other version control tools.

2. Distributed. No services or servers are required. Metrics are stored in a Git repository as text files, or pointers to files in DVC storage.

3. GitOps API. Plots are generated through DVC using Git commit SHAs or branch names, e.g.: dvc plots diff --target logs master.

4. Automation. DVCLive metrics are easy to use by any automation, DevOps, or MLOps tool such as CI/CD (including CML), custom scripts, or ML platforms.

DVCLive integrates seamlessly with DVC; the logs/summaries it produces can be fed as dvc plots/dvc metrics.

However, DVC is not required to work with dvclive logs/summaries, and since they’re saved as easily parsable .tsv/.json files, you can use your preferred visualization method.

Contents

• DVCLive

• Quick Start

• Installation

• Comparison to related technologies

• Call to collaboration

• Copyright

Quick Start

Please read the Usage guide for a detailed version.

DVCLive is a Python library. The interface consists of three main methods:

1. dvclive.init()

2. dvclive.log()

3. dvclive.next_step()

If you are ussing a ML training framework, check the existing ML Frameworks page.

Installation

pip (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. The command should look like this: pip install dvclive[tf] (in this case TensorFlow and it’s dependencies will be installed automatically).

To install the development version, run:

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

Comparison to related technologies

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 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.

Call to collaboration

Today only Python is supported (while DVC is language agnostic), along with the following ML frameworks:

• Catalyst

• Fast.ai

• Hugging Face

• Keras

• LightGBM

• MMCV

• PyTorch

• Tensorflow

• XGBoost

The DVCLive team is happy to extend the functionality as needed. Please create an issue or check the existing ones to start a discussion!

Copyright

This project is distributed under the Apache license version 2.0 (see the LICENSE file in the project root).

By submitting a pull request to this project, you agree to license your contribution under the Apache license version 2.0 to this project.