bring back deprecated params and add warn (#834) (#835)

batch_inference_v2/batch_inference_v2.py

@@ -120,7 +120,17 @@ def infer(
         model_endpoint_sample_set: Union[
             mlrun.DataItem, list, dict, pd.DataFrame, pd.Series, np.ndarray
         ] = None,
+
+        # the following parameters are deprecated and will be removed once the versioning mechanism is implemented
+        # TODO: Remove the following parameters once FHUB-13 is resolved
+        trigger_monitoring_job: Optional[bool] = None,
+        batch_image_job: Optional[str] = None,
+        model_endpoint_drift_threshold: Optional[float] = None,
+        model_endpoint_possible_drift_threshold: Optional[float] = None,
+
+        # prediction kwargs to pass to the model predict function
         **predict_kwargs: Dict[str, Any],
+
 ):
     """
     Perform a prediction on the provided dataset using the specified model.
@@ -173,10 +183,33 @@ def infer(
     :param model_endpoint_sample_set:               A sample dataset to give to compare the inputs in the drift analysis.
                                                     Can be provided as an input (DataItem) or as a parameter (e.g. string, list, DataFrame).
                                                     The default chosen sample set will always be the one who is set in the model artifact itself.
+    :param trigger_monitoring_job:                  Whether to trigger the batch drift analysis after the infer job.
+    :param batch_image_job:                         The image that will be used to register the monitoring batch job if not exist.
+                                                    By default, the image is mlrun/mlrun.
+    :param model_endpoint_drift_threshold:          The threshold of which to mark drifts. Defaulted to 0.7.
+    :param model_endpoint_possible_drift_threshold: The threshold of which to mark possible drifts. Defaulted to 0.5.
 
     raises MLRunInvalidArgumentError: if both `model_path` and `endpoint_id` are not provided
     """
 
+
+    if trigger_monitoring_job:
+        context.logger.warning("The `trigger_monitoring_job` parameter is deprecated and will be removed once the versioning mechanism is implemented. "
+                               "if you are using mlrun<1.7.0, please import the previous version of this function, for example "
+                               "'hub://batch_inference_v2:2.5.0'.")
+    if batch_image_job:
+        context.logger.warning("The `batch_image_job` parameter is deprecated and will be removed once the versioning mechanism is implemented. "
+                               "if you are using mlrun<1.7.0, please import the previous version of this function, for example "
+                               "'hub://batch_inference_v2:2.5.0'.")
+    if model_endpoint_drift_threshold:
+        context.logger.warning("The `model_endpoint_drift_threshold` parameter is deprecated and will be removed once the versioning mechanism is implemented. "
+                               "if you are using mlrun<1.7.0, please import the previous version of this function, for example "
+                               "'hub://batch_inference_v2:2.5.0'.")
+    if model_endpoint_possible_drift_threshold:
+        context.logger.warning("The `model_endpoint_possible_drift_threshold` parameter is deprecated and will be removed once the versioning mechanism is implemented. "
+                               "if you are using mlrun<1.7.0, please import the previous version of this function, for example "
+                               "'hub://batch_inference_v2:2.5.0'.")
+
     # Loading the model:
     context.logger.info(f"Loading model...")
     if isinstance(model_path, mlrun.DataItem):
@@ -250,4 +283,4 @@ def infer(
             model_endpoint_name=model_endpoint_name,
             infer_results_df=result_set.copy(),
             sample_set_statistics=sample_set_statistics,
-        )
+        )