k update annotation -a <YOUR_CLOUD_ARTIFACT_STORE># this must be done separately so that the other required stack components are first registered

zenml stack update annotation -an <YOUR_LABEL_STUDIO_ANNOTATOR>

zenml stack set annotation

# optionally also

zenml stack describe

Now if you run a simple CLI command like zenml annotator dataset list this should work without any errors. You're ready to use your annotator in your ML workflow!

How do you use it?

ZenML assumes that users have registered a cloud artifact store and an annotator as described above. ZenML currently only supports this setup, but we will add in the fully local stack option in the future.

ZenML supports access to your data and annotations via the zenml annotator ... CLI command.

You can access information about the datasets you're using with the zenml annotator dataset list. To work on annotation for a particular dataset, you can run zenml annotator dataset annotate <dataset_name>.

Our computer vision end to end example is the best place to see how all the pieces of making this integration work fit together. What follows is an overview of some key components to the Label Studio integration and how it can be used.

Label Studio Annotator Stack Component

Our Label Studio annotator component inherits from the BaseAnnotator class. There are some methods that are core methods that must be defined, like being able to register or get a dataset. Most annotators handle things like the storage of state and have their own custom features, so there are quite a few extra methods specific to Label Studio.