Iterative Studio provides a model registry that you can access by clicking on
Models tab. Your project's
dvc.yaml files are used to identify ML models and
specially formatted Git tags are used to
identify model versions and stage assignments.
To quickly start tracking your models in the Iterative Studio model registry:
Add a projectto 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_artifact()method in your model training code:
from dvclive import Live with Live(save_dvc_exp=True) as live: live.log_artifact("model.pt", type="model", name="mymodel")
Run the training job:
Once the training completes, commit and push the resultant
dvc.yamlfile to your Git repository remote.
The model will get added to the model registry, and you can click on the model's name to open its details page.
Iterative Studio offers more ways to add models to the model registry - you can:
dvc.yamldirectly and add your model to artifacts section, or
- add models from the Iterative Studio interface.
Find the complete tutorial here.
After adding a model, you can perform the following actions to manage its lifecycle:
- Register new versions of the models
- Assign stages to model versions
- Use models: download them or set up CI/CD to publish or deploy models
For more details, check out the
model registry user guide.