Merge pull request #1014 from HexToString/fix_grpc_bug

fix grpc_impl_bug and add readme by HexToString

python/examples/grpc_impl_example/imdb/README.md

+## IMDB comment sentiment inference service
+
+([简体中文](./README_CN.md)|English)
+
+### Get model files and sample data
+
+```
+sh get_data.sh
+```
+the package downloaded contains cnn, lstm and bow model config along with their test_data and train_data.
+
+### Start RPC inference service
+
+```
+python -m paddle_serving_server.serve --model imdb_cnn_model/ --thread 10 --port 9393 --use_multilang
+```
+### RPC Infer
+
+The `paddlepaddle` package is used in `test_client.py`, and you may need to download the corresponding package(`pip install paddlepaddle`).
+
+```
+head test_data/part-0 | python test_client.py
+```
+
+it will get predict results of the first 10 test cases.

python/examples/grpc_impl_example/imdb/README_CN.md

+## IMDB评论情绪预测服务
+
+(简体中文|[English](./README.md))
+
+### 获取模型文件和样例数据
+
+```
+sh get_data.sh
+```
+脚本会下载和解压出cnn、lstm和bow三种模型的配置文文件以及test_data和train_data。
+
+### 启动RPC预测服务
+
+```
+python -m paddle_serving_server.serve --model imdb_cnn_model/ --thread 10 --port 9393 --use_multilang
+```
+### 执行预测
+
+`test_client.py`中使用了`paddlepaddle`包，需要进行下载（`pip install paddlepaddle`）。
+
+```
+head test_data/part-0 | python test_client.py
+```
+预测test_data/part-0的前十个样例。

python/examples/imdb/test_ensemble_client.py → python/examples/grpc_impl_example/imdb/test_client.py

@@ -12,14 +12,12 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # pylint: disable=doc-string-missing
-
-from paddle_serving_client import Client
-from imdb_reader import IMDBDataset
+from paddle_serving_client import MultiLangClient as Client
+from paddle_serving_app.reader.imdb_reader import IMDBDataset
+import sys
+import numpy as np
 
 client = Client()
-# If you have more than one model, make sure that the input
-# and output of more than one model are the same.
-client.load_client_config('imdb_bow_client_conf/serving_client_conf.prototxt')
 client.connect(["127.0.0.1:9393"])
 
 # you can define any english sentence or dataset here
@@ -28,11 +26,17 @@ client.connect(["127.0.0.1:9393"])
 imdb_dataset = IMDBDataset()
 imdb_dataset.load_resource('imdb.vocab')
 
-for i in range(3):
-    line = 'i am very sad | 0'
+for line in sys.stdin:
     word_ids, label = imdb_dataset.get_words_and_label(line)
-    feed = {"words": word_ids}
+    word_len = len(word_ids)
+    feed = {
+        "words": np.array(word_ids).reshape(word_len, 1),
+        "words.lod": [0, word_len]
+    }
     fetch = ["prediction"]
-    fetch_maps = client.predict(feed=feed, fetch=fetch)
-    for model, fetch_map in fetch_maps.items():
-        print("step: {}, model: {}, res: {}".format(i, model, fetch_map))
+    fetch_map = client.predict(feed=feed, fetch=fetch, batch=True)
+    if fetch_map["serving_status_code"] == 0:
+        print(fetch_map)
+    else:
+        print(fetch_map["serving_status_code"])
+    #print("{} {}".format(fetch_map["prediction"][0], label[0]))

python/examples/grpc_impl_example/imdb/test_multilang_ensemble_client.py

-# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# pylint: disable=doc-string-missing
-
-from paddle_serving_client import MultiLangClient
-from imdb_reader import IMDBDataset
-
-client = MultiLangClient()
-# If you have more than one model, make sure that the input
-# and output of more than one model are the same.
-client.connect(["127.0.0.1:9393"])
-
-# you can define any english sentence or dataset here
-# This example reuses imdb reader in training, you
-# can define your own data preprocessing easily.
-imdb_dataset = IMDBDataset()
-imdb_dataset.load_resource('imdb.vocab')
-
-for i in range(3):
-    line = 'i am very sad | 0'
-    word_ids, label = imdb_dataset.get_words_and_label(line)
-    feed = {"words": word_ids}
-    fetch = ["prediction"]
-    fetch_maps = client.predict(feed=feed, fetch=fetch)
-    for model, fetch_map in fetch_maps.items():
-        if model == "serving_status_code":
-            continue
-        print("step: {}, model: {}, res: {}".format(i, model, fetch_map))

python/examples/grpc_impl_example/imdb/test_multilang_ensemble_server.py

-# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# pylint: disable=doc-string-missing
-
-from paddle_serving_server import OpMaker
-from paddle_serving_server import OpGraphMaker
-from paddle_serving_server import MultiLangServer
-
-op_maker = OpMaker()
-read_op = op_maker.create('general_reader')
-cnn_infer_op = op_maker.create(
-    'general_infer', engine_name='cnn', inputs=[read_op])
-bow_infer_op = op_maker.create(
-    'general_infer', engine_name='bow', inputs=[read_op])
-response_op = op_maker.create(
-    'general_response', inputs=[cnn_infer_op, bow_infer_op])
-
-op_graph_maker = OpGraphMaker()
-op_graph_maker.add_op(read_op)
-op_graph_maker.add_op(cnn_infer_op)
-op_graph_maker.add_op(bow_infer_op)
-op_graph_maker.add_op(response_op)
-
-server = MultiLangServer()
-server.set_op_graph(op_graph_maker.get_op_graph())
-model_config = {cnn_infer_op: 'imdb_cnn_model', bow_infer_op: 'imdb_bow_model'}
-server.load_model_config(model_config)
-server.prepare_server(workdir="work_dir1", port=9393, device="cpu")
-server.run_server()

python/examples/imdb/test_ensemble_server.py

-# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# pylint: disable=doc-string-missing
-
-from paddle_serving_server import OpMaker
-from paddle_serving_server import OpGraphMaker
-from paddle_serving_server import Server
-
-op_maker = OpMaker()
-read_op = op_maker.create('general_reader')
-cnn_infer_op = op_maker.create(
-    'general_infer', engine_name='cnn', inputs=[read_op])
-bow_infer_op = op_maker.create(
-    'general_infer', engine_name='bow', inputs=[read_op])
-response_op = op_maker.create(
-    'general_response', inputs=[cnn_infer_op, bow_infer_op])
-
-op_graph_maker = OpGraphMaker()
-op_graph_maker.add_op(read_op)
-op_graph_maker.add_op(cnn_infer_op)
-op_graph_maker.add_op(bow_infer_op)
-op_graph_maker.add_op(response_op)
-
-server = Server()
-server.set_op_graph(op_graph_maker.get_op_graph())
-model_config = {cnn_infer_op: 'imdb_cnn_model', bow_infer_op: 'imdb_bow_model'}
-server.load_model_config(model_config)
-server.prepare_server(workdir="work_dir1", port=9393, device="cpu")
-server.run_server()

python/paddle_serving_server/__init__.py

@@ -537,26 +537,37 @@ class MultiLangServerServiceServicer(multi_lang_general_model_service_pb2_grpc.
         fetch_names = list(request.fetch_var_names)
         is_python = request.is_python
         log_id = request.log_id
-        feed_dict = {}
-        feed_inst = request.insts[0]
-        for idx, name in enumerate(feed_names):
-            var = feed_inst.tensor_array[idx]
-            v_type = self.feed_types_[name]
-            data = None
-            if is_python:
-                if v_type == 0:  # int64
-                    data = np.frombuffer(var.data, dtype="int64")
-                elif v_type == 1:  # float32
-                    data = np.frombuffer(var.data, dtype="float32")
-                elif v_type == 2:  # int32
-                    data = np.frombuffer(var.data, dtype="int32")
+        feed_batch = []
+        for feed_inst in request.insts:
+            feed_dict = {}
+            for idx, name in enumerate(feed_names):
+                var = feed_inst.tensor_array[idx]
+                v_type = self.feed_types_[name]
+                data = None
+                if is_python:
+                    if v_type == 0:  # int64
+                        data = np.frombuffer(var.data, dtype="int64")
+                    elif v_type == 1:  # float32
+                        data = np.frombuffer(var.data, dtype="float32")
+                    elif v_type == 2:  # int32
+                        data = np.frombuffer(var.data, dtype="int32")
+                    else:
+                        raise Exception("error type.")
                 else:
-                    raise Exception("error type.")
-            data.shape = list(feed_inst.tensor_array[idx].shape)
-            feed_dict[name] = data
-            if len(var.lod) > 0:
-                feed_dict["{}.lod".format()] = var.lod
-        return feed_dict, fetch_names, is_python, log_id
+                    if v_type == 0:  # int64
+                        data = np.array(list(var.int64_data), dtype="int64")
+                    elif v_type == 1:  # float32
+                        data = np.array(list(var.float_data), dtype="float32")
+                    elif v_type == 2:  # int32
+                        data = np.array(list(var.int_data), dtype="int32")
+                    else:
+                        raise Exception("error type.")
+                data.shape = list(feed_inst.tensor_array[idx].shape)
+                feed_dict[name] = data
+                if len(var.lod) > 0:
+                    feed_dict["{}.lod".format(name)] = var.lod
+            feed_batch.append(feed_dict)
+        return feed_batch, fetch_names, is_python, log_id
 
     def _pack_inference_response(self, ret, fetch_names, is_python):
         resp = multi_lang_general_model_service_pb2.InferenceResponse()
@@ -608,10 +619,10 @@ class MultiLangServerServiceServicer(multi_lang_general_model_service_pb2_grpc.
         return resp
 
     def Inference(self, request, context):
-        feed_dict, fetch_names, is_python, log_id = \
+        feed_batch, fetch_names, is_python, log_id = \
                 self._unpack_inference_request(request)
         ret = self.bclient_.predict(
-            feed=feed_dict,
+            feed=feed_batch,
             fetch=fetch_names,
             batch=True,
             need_variant_tag=True,
@@ -649,6 +660,9 @@ class MultiLangServer(object):
                 "max_body_size is less than default value, will use default value in service."
             )
 
+    def use_encryption_model(self, flag=False):
+        self.encryption_model = flag
+
     def set_port(self, port):
         self.gport_ = port
 

python/paddle_serving_server_gpu/__init__.py

@@ -244,6 +244,9 @@ class Server(object):
                 "max_body_size is less than default value, will use default value in service."
             )
 
+    def use_encryption_model(self, flag=False):
+        self.encryption_model = flag
+
     def set_port(self, port):
         self.port = port
 
@@ -690,6 +693,8 @@ class MultiLangServerServiceServicer(multi_lang_general_model_service_pb2_grpc.
                         raise Exception("error type.")
                 data.shape = list(feed_inst.tensor_array[idx].shape)
                 feed_dict[name] = data
+                if len(var.lod) > 0:
+                    feed_dict["{}.lod".format(name)] = var.lod
             feed_batch.append(feed_dict)
         return feed_batch, fetch_names, is_python, log_id
 
@@ -744,11 +749,12 @@ class MultiLangServerServiceServicer(multi_lang_general_model_service_pb2_grpc.
         return resp
 
     def Inference(self, request, context):
-        feed_dict, fetch_names, is_python, log_id \
+        feed_batch, fetch_names, is_python, log_id \
                 = self._unpack_inference_request(request)
         ret = self.bclient_.predict(
-            feed=feed_dict,
+            feed=feed_batch,
             fetch=fetch_names,
+            batch=True,
             need_variant_tag=True,
             log_id=log_id)
         return self._pack_inference_response(ret, fetch_names, is_python)
@@ -787,6 +793,9 @@ class MultiLangServer(object):
                 "max_body_size is less than default value, will use default value in service."
             )
 
+    def use_encryption_model(self, flag=False):
+        self.encryption_model = flag
+
     def set_port(self, port):
         self.gport_ = port