_studio --api_key={{label_studio_secrets.api_key}}# for deployed instances of Label Studio, you can also pass in the URL as follows, for example:

# zenml annotator register label_studio --flavor label_studio --authentication_secret="<LABEL_STUDIO_SECRET_NAME>" --instance_url="<your_label_studio_url>" --port=80

When using a deployed instance of Label Studio, the instance URL must be specified without any trailing / at the end. You should specify the port, for example, port 80 for a standard HTTP connection.

Finally, add all these components to a stack and set it as your active stack. For example:

zenml stack copy annotation

zenml stack 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