Automatically generated by github-worflow[bot] for commit: c4c558e (#387

)

README.md

@@ -1,3 +1,43 @@
+### Change log [2024-10-10 07:25:57]
+1. Item Updated: `sklearn_classifier` (from version: `1.1.1` to `1.1.1`)
+2. Item Updated: `noise_reduction` (from version: `1.0.0` to `1.0.0`)
+3. Item Updated: `auto_trainer` (from version: `1.7.0` to `1.7.0`)
+4. Item Updated: `batch_inference_v2` (from version: `2.6.0` to `2.6.0`)
+5. Item Updated: `open_archive` (from version: `1.1.0` to `1.1.0`)
+6. Item Updated: `v2_model_server` (from version: `1.2.0` to `1.2.0`)
+7. Item Updated: `model_server` (from version: `1.1.0` to `1.1.0`)
+8. Item Updated: `question_answering` (from version: `0.4.0` to `0.4.0`)
+9. Item Updated: `feature_selection` (from version: `1.5.0` to `1.5.0`)
+10. Item Updated: `validate_great_expectations` (from version: `1.1.0` to `1.1.0`)
+11. Item Updated: `arc_to_parquet` (from version: `1.4.1` to `1.4.1`)
+12. Item Updated: `mlflow_utils` (from version: `1.0.0` to `1.0.0`)
+13. Item Updated: `batch_inference` (from version: `1.7.0` to `1.7.0`)
+14. Item Updated: `model_server_tester` (from version: `1.1.0` to `1.1.0`)
+15. Item Updated: `pyannote_audio` (from version: `1.2.0` to `1.2.0`)
+16. Item Updated: `hugging_face_serving` (from version: `1.1.0` to `1.1.0`)
+17. Item Updated: `azureml_serving` (from version: `1.1.0` to `1.1.0`)
+18. Item Updated: `azureml_utils` (from version: `1.3.0` to `1.3.0`)
+19. Item Updated: `v2_model_tester` (from version: `1.1.0` to `1.1.0`)
+20. Item Updated: `describe_dask` (from version: `1.1.0` to `1.1.0`)
+21. Item Updated: `text_to_audio_generator` (from version: `1.2.0` to `1.2.0`)
+22. Item Updated: `translate` (from version: `0.1.0` to `0.1.0`)
+23. Item Updated: `sklearn_classifier_dask` (from version: `1.1.1` to `1.1.1`)
+24. Item Updated: `gen_class_data` (from version: `1.2.0` to `1.2.0`)
+25. Item Updated: `structured_data_generator` (from version: `1.5.0` to `1.5.0`)
+26. Item Updated: `transcribe` (from version: `1.1.0` to `1.1.0`)
+27. Item Updated: `model_monitoring_batch` (from version: `1.1.0` to `1.1.0`)
+28. Item Updated: `describe` (from version: `1.3.0` to `1.3.0`)
+29. Item Updated: `pii_recognizer` (from version: `0.3.0` to `0.3.0`)
+30. Item Updated: `silero_vad` (from version: `1.3.0` to `1.3.0`)
+31. Item Updated: `describe_spark` (from version: `1.1.0` to `1.1.0`)
+32. Item Updated: `github_utils` (from version: `1.1.0` to `1.1.0`)
+33. Item Updated: `send_email` (from version: `1.2.0` to `1.2.0`)
+34. Item Updated: `onnx_utils` (from version: `1.2.0` to `1.2.0`)
+35. Item Updated: `load_dataset` (from version: `1.2.0` to `1.2.0`)
+36. Item Updated: `test_classifier` (from version: `1.1.0` to `1.1.0`)
+37. Item Updated: `aggregate` (from version: `1.3.0` to `1.3.0`)
+38. Item Updated: `tf2_serving` (from version: `1.1.0` to `1.1.0`)
+
 ### Change log [2024-10-09 07:18:31]
 1. Item Updated: `sklearn_classifier` (from version: `1.1.1` to `1.1.1`)
 2. Item Updated: `noise_reduction` (from version: `1.0.0` to `1.0.0`)

functions/development/batch_inference_v2/2.6.0/src/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,
-        )
+        )

functions/development/batch_inference_v2/2.6.0/static/batch_inference_v2.html

@@ -236,7 +236,17 @@ <h1>Source code for batch_inference_v2.batch_inference_v2</h1><div class="highli
         <span class="n">model_endpoint_sample_set</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span>
             <span class="n">mlrun</span><span class="o">.</span><span class="n">DataItem</span><span class="p">,</span> <span class="nb">list</span><span class="p">,</span> <span class="nb">dict</span><span class="p">,</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">,</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span>
         <span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+
+        <span class="c1"># the following parameters are deprecated and will be removed once the versioning mechanism is implemented</span>
+        <span class="c1"># TODO: Remove the following parameters once FHUB-13 is resolved</span>
+        <span class="n">trigger_monitoring_job</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">bool</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">batch_image_job</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">model_endpoint_drift_threshold</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">model_endpoint_possible_drift_threshold</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+
+        <span class="c1"># prediction kwargs to pass to the model predict function</span>
         <span class="o">**</span><span class="n">predict_kwargs</span><span class="p">:</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Any</span><span class="p">],</span>
+
 <span class="p">):</span>
 <span class="w">    </span><span class="sd">"""</span>
 <span class="sd">    Perform a prediction on the provided dataset using the specified model.</span>
@@ -289,10 +299,33 @@ <h1>Source code for batch_inference_v2.batch_inference_v2</h1><div class="highli
 <span class="sd">    :param model_endpoint_sample_set:               A sample dataset to give to compare the inputs in the drift analysis.</span>
 <span class="sd">                                                    Can be provided as an input (DataItem) or as a parameter (e.g. string, list, DataFrame).</span>
 <span class="sd">                                                    The default chosen sample set will always be the one who is set in the model artifact itself.</span>
+<span class="sd">    :param trigger_monitoring_job:                  Whether to trigger the batch drift analysis after the infer job.</span>
+<span class="sd">    :param batch_image_job:                         The image that will be used to register the monitoring batch job if not exist.</span>
+<span class="sd">                                                    By default, the image is mlrun/mlrun.</span>
+<span class="sd">    :param model_endpoint_drift_threshold:          The threshold of which to mark drifts. Defaulted to 0.7.</span>
+<span class="sd">    :param model_endpoint_possible_drift_threshold: The threshold of which to mark possible drifts. Defaulted to 0.5.</span>
 
 <span class="sd">    raises MLRunInvalidArgumentError: if both `model_path` and `endpoint_id` are not provided</span>
 <span class="sd">    """</span>
 
+
+    <span class="k">if</span> <span class="n">trigger_monitoring_job</span><span class="p">:</span>
+        <span class="n">context</span><span class="o">.</span><span class="n">logger</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span><span class="s2">"The `trigger_monitoring_job` parameter is deprecated and will be removed once the versioning mechanism is implemented. "</span>
+                               <span class="s2">"if you are using mlrun&lt;1.7.0, please import the previous version of this function, for example "</span>
+                               <span class="s2">"'hub://batch_inference_v2:2.5.0'."</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">batch_image_job</span><span class="p">:</span>
+        <span class="n">context</span><span class="o">.</span><span class="n">logger</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span><span class="s2">"The `batch_image_job` parameter is deprecated and will be removed once the versioning mechanism is implemented. "</span>
+                               <span class="s2">"if you are using mlrun&lt;1.7.0, please import the previous version of this function, for example "</span>
+                               <span class="s2">"'hub://batch_inference_v2:2.5.0'."</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">model_endpoint_drift_threshold</span><span class="p">:</span>
+        <span class="n">context</span><span class="o">.</span><span class="n">logger</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span><span class="s2">"The `model_endpoint_drift_threshold` parameter is deprecated and will be removed once the versioning mechanism is implemented. "</span>
+                               <span class="s2">"if you are using mlrun&lt;1.7.0, please import the previous version of this function, for example "</span>
+                               <span class="s2">"'hub://batch_inference_v2:2.5.0'."</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">model_endpoint_possible_drift_threshold</span><span class="p">:</span>
+        <span class="n">context</span><span class="o">.</span><span class="n">logger</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span><span class="s2">"The `model_endpoint_possible_drift_threshold` parameter is deprecated and will be removed once the versioning mechanism is implemented. "</span>
+                               <span class="s2">"if you are using mlrun&lt;1.7.0, please import the previous version of this function, for example "</span>
+                               <span class="s2">"'hub://batch_inference_v2:2.5.0'."</span><span class="p">)</span>
+
     <span class="c1"># Loading the model:</span>
     <span class="n">context</span><span class="o">.</span><span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Loading model..."</span><span class="p">)</span>
     <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">model_path</span><span class="p">,</span> <span class="n">mlrun</span><span class="o">.</span><span class="n">DataItem</span><span class="p">):</span>