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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 also submit new experiments from Iterative Studio.

You can track live as well as completed experiments in Iterative Studio. First, click on Add a project to connect Iterative 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 Iterative Studio:

  • In your model training environment, install DVCLive:

    pip install dvclive
  • Copy your DVC Studio token and configure your model training environment to use the token:

    dvc config --global studio.token ***
  • 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 Iterative Studio.

Track reproducible pipelines

To set up, run and track reproducible pipelines:

Visualize, compare and run experiments

Within a project, you can:


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