Release v0.7.0

* Let `make_serving_endpoint` reference a valid model version ([#106](#106)). The `make_serving_endpoint` fixture has been updated to address an issue where it stopped working due to the absence of a model version in the models created by the `make_model` fixture. It now defaults to a UC model version and allows users to provide input parameters for the model name and version. The `create` and `remove` methods have also been updated to accept optional arguments for endpoint name, model name, and model version. Additionally, new tests have been added to the `test_ml.py` file to handle the change where models no longer come with a model version. These changes provide better flexibility and control to users when creating and deleting endpoints, and ensure that the fixture works correctly even when no model version is present.

Author: gueniai

CHANGELOG.md

@@ -1,5 +1,10 @@
 # Version changelog
 
+## 0.7.0
+
+* Let `make_serving_endpoint` reference a valid model version ([#106](https://github.com/databrickslabs/pytester/issues/106)). The `make_serving_endpoint` fixture has been updated to address an issue where it stopped working due to the absence of a model version in the models created by the `make_model` fixture. It now defaults to a UC model version and allows users to provide input parameters for the model name and version. The `create` and `remove` methods have also been updated to accept optional arguments for endpoint name, model name, and model version. Additionally, new tests have been added to the `test_ml.py` file to handle the change where models no longer come with a model version. These changes provide better flexibility and control to users when creating and deleting endpoints, and ensure that the fixture works correctly even when no model version is present.
+
+
 ## 0.6.0
 
 *  Added serverless support to spark fixture ([#91](https://github.com/databrickslabs/pytester/issues/90). This release introduces serverless support to the `spark` fixture, enabling the creation of a Databricks Connect Spark session with serverless capabilities by leveraging `DATABRICKS_SERVERLESS_COMPUTE_ID` environment variable, which when set to `auto` enables the Spark session to run on serverless compute.

src/databricks/labs/pytester/__about__.py

@@ -1 +1 @@
-__version__ = "0.6.0"
+__version__ = "0.7.0"