Improve resilience and performance of do_bulk_inference

.pre-commit-config.yaml

@@ -2,7 +2,7 @@ default_language_version:
     python: python3.7
 repos:
 -   repo: https://github.com/ambv/black
-    rev: 21.6b0
+    rev: 22.3.0
     hooks:
     - id: black
       language_version: python3.7

.travis.yml

@@ -43,6 +43,8 @@ stages:
 
 language: python
 install:
+  # Fix build on 3.7: https://github.com/pypa/setuptools/issues/3293
+- pip3 install 'setuptools==60.9.0;python_version=="3.7"'
 - pip3 install -r requirements-test.txt
 script: tox
 

CHANGELOG.md

@@ -6,8 +6,24 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ## [Unreleased]
 
+## [0.12.0]
+
+### Changed
+
+* `InferenceClient.do_bulk_inference` is now faster due to processing requests in parallel [#128]
+* `InferenceClient.do_bulk_inference` is more resilient and handles errors internally. Instead of
+   raising an Exception if the inference request to the service fails, the `do_bulk_inference` method
+   will place a special error response object in the returned list. This can be considered a breaking API change,
+   because the special error response object will have a value of `None` for the `labels` key.
+   As this project is still versioned below 1.0.0, the breaking API change does not warrant a major version update.
+   See [#128] for details.
+
+[#128]: https://github.com/SAP/data-attribute-recommendation-python-sdk/pull/128
+
 ## [0.11.0]
 
+### Added
+
 * Support for reading training jobs using model name in `read_job_by_model_name`
 
 [#124]: https://github.com/SAP/data-attribute-recommendation-python-sdk/pull/124

sap/aibus/dar/client/inference_client.py

@@ -1,9 +1,13 @@
 """
 Client API for the Inference microservice.
 """
-from typing import List
+from concurrent.futures import ThreadPoolExecutor
+from typing import List, Union
+
+from requests import RequestException
 
 from sap.aibus.dar.client.base_client import BaseClientWithSession
+from sap.aibus.dar.client.exceptions import DARHTTPException
 from sap.aibus.dar.client.inference_constants import InferencePaths
 from sap.aibus.dar.client.util.lists import split_list
 
@@ -73,30 +77,52 @@ def do_bulk_inference(
         objects: List[dict],
         top_n: int = TOP_N,
         retry: bool = True,
-    ) -> List[dict]:
+    ) -> List[Union[dict, None]]:
         """
         Performs bulk inference for larger collections.
 
         For *objects* collections larger than *LIMIT_OBJECTS_PER_CALL*, splits
         the data into several smaller Inference requests.
 
+        Requests are executed in parallel.
+
         Returns the aggregated values of the *predictions* of the original API response
-        as returned by :meth:`create_inference_request`.
+        as returned by :meth:`create_inference_request`. If one of the inference
+        requests to the service fails, an artificial prediction object is inserted with
+        the `labels` key set to `None` for each of the objects in the failing request.
+
+        Example of a prediction object which indicates an error:
+
+        .. code-block:: python
+
+            {
+                'objectId': 'b5cbcb34-7ab9-4da5-b7ec-654c90757eb9',
+                'labels': None,
+                '_sdk_error': 'RequestException: Request Error'
+            }
+
+        In case the `objects` passed to this method do not contain the `objectId` field,
+        the value is set to `None` in the error prediction object:
+
+        .. code-block:: python
+
+            {
+                'objectId': None,
+                'labels': None,
+                '_sdk_error': 'RequestException: Request Error'
+            }
+
 
         .. note::
 
             This method calls the inference endpoint multiple times to process all data.
             For non-trial service instances, each call will incur a cost.
 
-            If one of the calls fails, this method will raise an Exception and the
-            progress will be lost. In this case, all calls until the Exception happened
-            will be charged.
-
-            To reduce the likelihood of a failed request terminating the bulk inference
-            process, this method will retry failed requests.
+            To reduce the impact of a failed request, this method will retry failed
+            requests.
 
             There is a small chance that even retried requests will be charged, e.g.
-            if a problem occurs with the request on the client side outside of the
+            if a problem occurs with the request on the client side outside the
             control of the service and after the service has processed the request.
             To disable `retry` behavior simply pass `retry=False` to the method.
 
@@ -107,20 +133,62 @@ def do_bulk_inference(
             The default for the `retry` parameter changed from `retry=False` to
             `retry=True` for increased reliability in day-to-day operations.
 
+        .. versionchanged:: 0.12.0
+           Requests are now executed in parallel with up to four threads.
+
+           Errors are now handled in this method instead of raising an exception and
+           discarding inference results from previous requests. For objects where the
+           inference request did not succeed, a replacement `dict` object is placed in
+           the returned `list`.
+           This `dict` follows the format of the `ObjectPrediction` object sent by the
+           service. To indicate that this is a client-side generated placeholder, the
+           `labels` key for all ObjectPrediction dicts of the failed inference request
+           has value `None`.
+           A `_sdk_error` key is added with the Exception details.
+
 
         :param model_name: name of the model used for inference
         :param objects: Objects to be classified
         :param top_n: How many predictions to return per object
         :param retry: whether to retry on errors. Default: True
         :return: the aggregated ObjectPrediction dictionaries
         """
-        result = []  # type: List[dict]
-        for work_package in split_list(objects, LIMIT_OBJECTS_PER_CALL):
-            response = self.create_inference_request(
-                model_name, work_package, top_n=top_n, retry=retry
+
+        def predict_call(work_package):
+            try:
+                response = self.create_inference_request(
+                    model_name, work_package, top_n=top_n, retry=retry
+                )
+                return response["predictions"]
+            except (DARHTTPException, RequestException) as exc:
+                self.log.warning(
+                    "Caught %s during bulk inference. "
+                    "Setting results to None for this batch!",
+                    exc,
+                    exc_info=True,
+                )
+
+                prediction_error = [
+                    {
+                        "objectId": inference_object.get("objectId", None),
+                        "labels": None,
+                        "_sdk_error": "{}: {}".format(exc.__class__.__name__, str(exc)),
+                    }
+                    for inference_object in work_package
+                ]
+                return prediction_error
+
+        results = []
+
+        with ThreadPoolExecutor(max_workers=4) as pool:
+            results_iterator = pool.map(
+                predict_call, split_list(objects, LIMIT_OBJECTS_PER_CALL)
             )
-            result.extend(response["predictions"])
-        return result
+
+            for predictions in results_iterator:
+                results.extend(predictions)
+
+        return results
 
     def create_inference_request_with_url(
         self,

sap/aibus/dar/client/model_manager_client.py

@@ -174,7 +174,7 @@ def create_job(
         :param model_template_id: Model template ID for training
         :param business_blueprint_id: Business Blueprint template ID for training
         :raises CreateTrainingJobFailed: When business_blueprint_id
-        and model_template_id are provided or when both are not provided
+            and model_template_id are provided or when both are not provided
         :return: newly created Job as dict
         """
         self.log.info(

tests/sap/aibus/dar/client/test_exceptions.py

@@ -8,22 +8,28 @@
 
 url = "http://localhost:4321/test/"
 
+correlation_id = "412d84ae-0eb5-4421-863d-956570c2da54"
+vcap_request_id = "d9cd7dec-4d74-4a7a-a953-4ca583c8d912"
+
+
+def create_mock_response_404():
+    mock_response = create_mock_response()
+
+    mock_response.headers["X-Correlation-ID"] = correlation_id
+    mock_response.headers["X-Vcap-Request-Id"] = vcap_request_id
+    mock_response.headers["Server"] = "Gunicorn"
+    mock_response.headers["X-Cf-Routererror"] = "unknown_route"
+    mock_response.status_code = 404
+    mock_response.request.method = "GET"
+    mock_response.reason = b"\xc4\xd6\xdc Not Found"
+    return mock_response
+
 
 class TestDARHTTPException:
     url = "http://localhost:4321/test/"
 
     def test_basic(self):
-        mock_response = create_mock_response()
-
-        correlation_id = "412d84ae-0eb5-4421-863d-956570c2da54"
-        mock_response.headers["X-Correlation-ID"] = correlation_id
-        vcap_request_id = "d9cd7dec-4d74-4a7a-a953-4ca583c8d912"
-        mock_response.headers["X-Vcap-Request-Id"] = vcap_request_id
-        mock_response.headers["Server"] = "Gunicorn"
-        mock_response.headers["X-Cf-Routererror"] = "unknown_route"
-        mock_response.status_code = 404
-        mock_response.request.method = "GET"
-        mock_response.reason = b"\xc4\xd6\xdc Not Found"
+        mock_response = create_mock_response_404()
 
         exception = DARHTTPException.create_from_response(url, mock_response)
 
@@ -130,7 +136,6 @@ class TestDARHTTPExceptionReason:
     # status line: https://tools.ietf.org/html/rfc7230#section-3.1.2
 
     def test_reason_works_iso8859_1(self):
-
         mock_response = create_mock_response()
         # ÄÖÜ encoded as ISO-8859-1
         mock_response.reason = b"\xc4\xd6\xdc"