[batch_inference] Remove last_in_batch_set parameter (#783)

batch_inference_v2/batch_inference_v2.py

@@ -12,8 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from inspect import signature
-from typing import Any, Dict, List, Optional, Union
+from typing import Any, Dict, List, Union
 
 import mlrun
 
@@ -78,28 +77,6 @@ def _prepare_result_set(
     )
 
 
-def _parse_record_results_kwarg(
-    last_in_batch_set: Optional[bool],
-) -> dict[str, bool]:
-    """
-    Check if `last_in_batch_set` is provided and expected as a parameter.
-    Return it as a dictionary.
-    """
-    kwarg = "last_in_batch_set"
-    if last_in_batch_set is None:
-        return {}
-    if (
-        signature(mlrun.model_monitoring.api.record_results).parameters.get(kwarg)
-        is None
-    ):
-        raise mlrun.errors.MLRunInvalidArgumentError(
-            f"Unexpected parameter `{kwarg}` for function: "
-            "`mlrun.model_monitoring.api.record_results`. "
-            "Please make sure that you are using `mlrun>=1.6.0` version."
-        )
-    return {kwarg: last_in_batch_set}
-
-
 def infer(
     context: mlrun.MLClientCtx,
     dataset: Union[mlrun.DataItem, list, dict, pd.DataFrame, pd.Series, np.ndarray],
@@ -125,7 +102,6 @@ def infer(
     model_endpoint_sample_set: Union[
         mlrun.DataItem, list, dict, pd.DataFrame, pd.Series, np.ndarray
     ] = None,
-    last_in_batch_set: Optional[bool] = None,
     **predict_kwargs: Dict[str, Any],
 ):
     """
@@ -185,17 +161,8 @@ 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 last_in_batch_set:                       Relevant only when `perform_drift_analysis` is `True`.
-                                                    This flag can (and should only) be used when the model endpoint does not have
-                                                    model-monitoring set.
-                                                    If set to `True` (the default), this flag marks the current monitoring window
-                                                    (on this monitoring endpoint) as completed - the data inferred so far is assumed
-                                                    to be the complete data for this monitoring window.
-                                                    You may want to set this flag to `False` if you want to record multiple results in
-                                                    close time proximity ("batch set"). In this case, set this flag to `False` on all
-                                                    but the last batch in the set.
-    raises MLRunInvalidArgumentError: if both `model_path` and `endpoint_id` are not provided, or if `last_in_batch_set` is
-                                      provided for an unsupported `mlrun` version.
+
+    raises MLRunInvalidArgumentError: if both `model_path` and `endpoint_id` are not provided
     """
 
     # Loading the model:
@@ -272,5 +239,4 @@ def infer(
             artifacts_tag=artifacts_tag,
             trigger_monitoring_job=trigger_monitoring_job,
             default_batch_image=batch_image_job,
-            **_parse_record_results_kwarg(last_in_batch_set=last_in_batch_set),
         )