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. To quickly start tracking your experiments with Iterative Studio:
Add a projectto connect Iterative Studio to your ML project's Git repository.
In your model training environment, install DVCLive:
pip install dvclive
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) live.next_step()
Run the training job:
The metrics and plots will be tracked live in the project in Iterative Studio.
Iterative Studio offers more ways to run and track experiments - you can:
- set up reproducible pipelines with DVC,
- submit new experiments from the VS Code IDE,
- submit new experiments from Iterative Studio, and have them run in your own cloud infrastructure.
For details on all these, check out the
experiment management user guide.