Edit on GitHub

Experiment tracking and management

You can submit your experiments from your favorite interface - whether it is Jupyter Notebooks, a code editor or IDE like VS Code, the Python cli, the bash terminal, etc.

You can track live as well as completed experiments in DVC Studio. First, click on Add a project to connect DVC Studio to your ML project's Git repository. Then, follow the instructions presented below.

Track experiments in real-time

To quickly start tracking your experiments with DVC Studio:

  • In your model training environment, install DVCLive:

    $ pip install dvclive
  • Set your DVC Studio client access token:

    $ dvc studio login
  • Use the DVCLive log_metric() method in your model training code:

    from dvclive import Live
    with Live(save_dvc_exp=True) as live:
      for epoch in range(epochs):
        live.log_metric("accuracy", accuracy)
        live.log_metric("loss", loss)

    DVCLive has implemented callbacks for several popular ML frameworks which simplify adding experiment tracking capabilities to your projects.

  • Run the training job:

    $ python train.py
  • The metrics and plots will be tracked live in the project in DVC Studio.

Track reproducible pipelines

To set up, run and track reproducible pipelines:

Visualize, compare and run experiments

Within a project, you can:


๐Ÿ› Found an issue? Let us know! Or fix it:

Edit on GitHub

โ“ Have a question? Join our chat, we will help you:

Discord Chat