Added example for torchserve grpc v1 and v2. (#307)

* Added example for torchserve grpc v1 and v2.

Signed-off-by: Andrews Arokiam <andrews.arokiam@ideas2it.com>

* Schema order changed.

Signed-off-by: Andrews Arokiam <andrews.arokiam@ideas2it.com>

* corrected v2 REST input.

Signed-off-by: Andrews Arokiam <andrews.arokiam@ideas2it.com>

* Updated grpc-v2 protocolVersion.

Signed-off-by: Andrews Arokiam <andrews.arokiam@ideas2it.com>

* Update README.md

* Update README.md

* Update README.md

---------

Signed-off-by: Andrews Arokiam <andrews.arokiam@ideas2it.com>
Co-authored-by: Dan Sun <dsun20@bloomberg.net>

.gitignore

@@ -3,6 +3,7 @@
 
 # python
 .venv
+*.pyc
 
 # editor
 .vscode

docs/modelserving/v1beta1/torchserve/inference_pb2_grpc.py

@@ -0,0 +1,136 @@
+# Generated by the gRPC Python protocol compiler plugin. DO NOT EDIT!
+"""Client and server classes corresponding to protobuf-defined services."""
+import grpc
+
+from google.protobuf import empty_pb2 as google_dot_protobuf_dot_empty__pb2
+import inference_pb2 as inference__pb2
+
+
+class InferenceAPIsServiceStub(object):
+    """Missing associated documentation comment in .proto file."""
+
+    def __init__(self, channel):
+        """Constructor.
+
+        Args:
+            channel: A grpc.Channel.
+        """
+        self.Ping = channel.unary_unary(
+                '/org.pytorch.serve.grpc.inference.InferenceAPIsService/Ping',
+                request_serializer=google_dot_protobuf_dot_empty__pb2.Empty.SerializeToString,
+                response_deserializer=inference__pb2.TorchServeHealthResponse.FromString,
+                )
+        self.Predictions = channel.unary_unary(
+                '/org.pytorch.serve.grpc.inference.InferenceAPIsService/Predictions',
+                request_serializer=inference__pb2.PredictionsRequest.SerializeToString,
+                response_deserializer=inference__pb2.PredictionResponse.FromString,
+                )
+        self.StreamPredictions = channel.unary_stream(
+                '/org.pytorch.serve.grpc.inference.InferenceAPIsService/StreamPredictions',
+                request_serializer=inference__pb2.PredictionsRequest.SerializeToString,
+                response_deserializer=inference__pb2.PredictionResponse.FromString,
+                )
+
+
+class InferenceAPIsServiceServicer(object):
+    """Missing associated documentation comment in .proto file."""
+
+    def Ping(self, request, context):
+        """Check health status of the TorchServe server.
+        """
+        context.set_code(grpc.StatusCode.UNIMPLEMENTED)
+        context.set_details('Method not implemented!')
+        raise NotImplementedError('Method not implemented!')
+
+    def Predictions(self, request, context):
+        """Predictions entry point to get inference using default model version.
+        """
+        context.set_code(grpc.StatusCode.UNIMPLEMENTED)
+        context.set_details('Method not implemented!')
+        raise NotImplementedError('Method not implemented!')
+
+    def StreamPredictions(self, request, context):
+        """Streaming response for an inference request.
+        """
+        context.set_code(grpc.StatusCode.UNIMPLEMENTED)
+        context.set_details('Method not implemented!')
+        raise NotImplementedError('Method not implemented!')
+
+
+def add_InferenceAPIsServiceServicer_to_server(servicer, server):
+    rpc_method_handlers = {
+            'Ping': grpc.unary_unary_rpc_method_handler(
+                    servicer.Ping,
+                    request_deserializer=google_dot_protobuf_dot_empty__pb2.Empty.FromString,
+                    response_serializer=inference__pb2.TorchServeHealthResponse.SerializeToString,
+            ),
+            'Predictions': grpc.unary_unary_rpc_method_handler(
+                    servicer.Predictions,
+                    request_deserializer=inference__pb2.PredictionsRequest.FromString,
+                    response_serializer=inference__pb2.PredictionResponse.SerializeToString,
+            ),
+            'StreamPredictions': grpc.unary_stream_rpc_method_handler(
+                    servicer.StreamPredictions,
+                    request_deserializer=inference__pb2.PredictionsRequest.FromString,
+                    response_serializer=inference__pb2.PredictionResponse.SerializeToString,
+            ),
+    }
+    generic_handler = grpc.method_handlers_generic_handler(
+            'org.pytorch.serve.grpc.inference.InferenceAPIsService', rpc_method_handlers)
+    server.add_generic_rpc_handlers((generic_handler,))
+
+
+ # This class is part of an EXPERIMENTAL API.
+class InferenceAPIsService(object):
+    """Missing associated documentation comment in .proto file."""
+
+    @staticmethod
+    def Ping(request,
+            target,
+            options=(),
+            channel_credentials=None,
+            call_credentials=None,
+            insecure=False,
+            compression=None,
+            wait_for_ready=None,
+            timeout=None,
+            metadata=None):
+        return grpc.experimental.unary_unary(request, target, '/org.pytorch.serve.grpc.inference.InferenceAPIsService/Ping',
+            google_dot_protobuf_dot_empty__pb2.Empty.SerializeToString,
+            inference__pb2.TorchServeHealthResponse.FromString,
+            options, channel_credentials,
+            insecure, call_credentials, compression, wait_for_ready, timeout, metadata)
+
+    @staticmethod
+    def Predictions(request,
+            target,
+            options=(),
+            channel_credentials=None,
+            call_credentials=None,
+            insecure=False,
+            compression=None,
+            wait_for_ready=None,
+            timeout=None,
+            metadata=None):
+        return grpc.experimental.unary_unary(request, target, '/org.pytorch.serve.grpc.inference.InferenceAPIsService/Predictions',
+            inference__pb2.PredictionsRequest.SerializeToString,
+            inference__pb2.PredictionResponse.FromString,
+            options, channel_credentials,
+            insecure, call_credentials, compression, wait_for_ready, timeout, metadata)
+
+    @staticmethod
+    def StreamPredictions(request,
+            target,
+            options=(),
+            channel_credentials=None,
+            call_credentials=None,
+            insecure=False,
+            compression=None,
+            wait_for_ready=None,
+            timeout=None,
+            metadata=None):
+        return grpc.experimental.unary_stream(request, target, '/org.pytorch.serve.grpc.inference.InferenceAPIsService/StreamPredictions',
+            inference__pb2.PredictionsRequest.SerializeToString,
+            inference__pb2.PredictionResponse.FromString,
+            options, channel_credentials,
+            insecure, call_credentials, compression, wait_for_ready, timeout, metadata)