Run and track experiments

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:

Click on Add a project to connect Iterative Studio to your ML project's Git repository.
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)
    live.next_step()

Run the training job:
```
python train.py
```
The metrics and plots will be tracked live in the project in Iterative Studio.

More ways to run and track experiments

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.

Run and track experiments

More ways to run and track experiments

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