Add async pipeline example (--engine custom_async)

examples/pipeline/async_preprocess.py

@@ -0,0 +1,40 @@
+from typing import Any, List
+
+
+# register with --engine custom_async
+# Notice Preprocess class Must be named "Preprocess"
+class Preprocess(object):
+    def __init__(self):
+        pass
+
+    async def preprocess(self, body: dict, state: dict, collect_custom_statistics_fn=None) -> Any:
+        # we expect to get two valid on the dict x0, and x1
+        return body
+
+    async def postprocess(self, data: List[dict], state: dict, collect_custom_statistics_fn=None) -> dict:
+        # we will here average the results and return the new value
+        # assume data is a list of dicts greater than 1
+
+        # average result
+        return dict(y=0.5 * data[0]['y'][0] + 0.5 * data[1]['y'][0])
+
+    async def process(self, data: Any, state: dict, collect_custom_statistics_fn=None) -> Any:
+        """
+        do something with the actual data, return any type of object.
+        The returned object will be passed as is to the postprocess function engine
+        """
+        predict_a = self.send_request(endpoint="/test_model_sklearn_a/", version=None, data=data)
+        predict_b = self.send_request(endpoint="/test_model_sklearn_b/", version=None, data=data)
+
+        predict_a = await predict_a
+        predict_b = await predict_b
+
+        if not predict_b or not predict_a:
+            raise ValueError("Error requesting inference endpoint test_model_sklearn a/b")
+
+        return [predict_a, predict_b]
+
+    async def send_request(self, endpoint, version, data) -> List[dict]:
+        # Mock Function!
+        # replaced by real send request function when constructed by the inference service
+        pass