t of changes.

Changes in our integrations changesMuch like ZenML, pydantic is an important dependency in many other Python packages. That’s why conducting this upgrade helped us unlock a new version for several ZenML integration dependencies. Additionally, in some instances, we had to adapt the functionality of the integration to keep it compatible with pydantic. So, if you are using any of these integrations, please go through the changes.

Airflow

right here.

AWS

Evidently

The old version of our evidently integration was not compatible with Pydantic v2. They started supporting it starting from version 0.4.16. As their latest version is 0.4.22, the new dependency of the integration is limited between these two versions.

Feast

Our previous implementation of the feast integration was not compatible with Pydantic v2 due to the extra redis dependency we were using. This extra dependency is now removed and the feast integration is working as intended.

GCP

The previous version of the Kubeflow dependency (kfp==1.8.22) in our GCP integration required Pydantic V1 to be installed. While we were upgrading our Pydantic dependency, we saw this as an opportunity and wanted to use this chance to upgrade the kfp dependency to v2 (which has no dependencies on the Pydantic library). This is why you may see some functional changes in the vertex step operator and orchestrator. If you would like to go through the changes in the kfp library, you can find the migration guide here.

Great Expectations

Great Expectations started supporting Pydantic v2 starting from version 0.17.15 and they are closing in on their 1.0 release. Since this release might include a lot of big changes, we adjusted the dependency in our integration to great-expectations>=0.17.15,<1.0. We will try to keep it updated in the future once they release the 1.0 version

Kubeflow

the migration guide here. ( We also are considering adding an alternative version of this integration so our users can keep using

MLflow

Label Studio