add use_mkl && fix imdb script

python/examples/imdb/local_train.py

@@ -21,6 +21,7 @@ logging.basicConfig(format='%(asctime)s - %(levelname)s - %(message)s')
 logger = logging.getLogger("fluid")
 logger.setLevel(logging.INFO)
 
+
 def load_vocab(filename):
     vocab = {}
     with open(filename) as f:
@@ -31,17 +32,19 @@ def load_vocab(filename):
     vocab["<unk>"] = len(vocab)
     return vocab
 
+
 if __name__ == "__main__":
     vocab = load_vocab('imdb.vocab')
     dict_dim = len(vocab)
 
-    data = fluid.layers.data(name="words", shape=[1], dtype="int64", lod_level=1)
+    data = fluid.layers.data(
+        name="words", shape=[1], dtype="int64", lod_level=1)
     label = fluid.layers.data(name="label", shape=[1], dtype="int64")
 
     dataset = fluid.DatasetFactory().create_dataset()
     filelist = ["train_data/%s" % x for x in os.listdir("train_data")]
     dataset.set_use_var([data, label])
-    pipe_command = "/home/users/dongdaxiang/paddle_whls/custom_op/paddle_release_home/python/bin/python imdb_reader.py"
+    pipe_command = "python imdb_reader.py"
     dataset.set_pipe_command(pipe_command)
     dataset.set_batch_size(4)
     dataset.set_filelist(filelist)
@@ -59,16 +62,14 @@ if __name__ == "__main__":
     import paddle_serving_client.io as serving_io
 
     for i in range(epochs):
-        exe.train_from_dataset(program=fluid.default_main_program(),
-                               dataset=dataset, debug=False)
+        exe.train_from_dataset(
+            program=fluid.default_main_program(), dataset=dataset, debug=False)
         logger.info("TRAIN --> pass: {}".format(i))
         if i == 5:
-            serving_io.save_model("serving_server_model",
-                                  "serving_client_conf",
-                                  {"words": data, "label": label},
-                                  {"cost": avg_cost, "acc": acc,
-                                   "prediction": prediction},
-                                  fluid.default_main_program())
-
-
-
+            serving_io.save_model("serving_server_model", "serving_client_conf",
+                                  {"words": data,
+                                   "label": label}, {
+                                       "cost": avg_cost,
+                                       "acc": acc,
+                                       "prediction": prediction
+                                   }, fluid.default_main_program())

python/examples/imdb/test_client_batch.py

@@ -23,7 +23,6 @@ def batch_predict(batch_size=4):
     client = Client()
     client.load_client_config(conf_file)
     client.connect(["127.0.0.1:9292"])
-    start = time.time()
     fetch = ["acc", "cost", "prediction"]
     feed_batch = []
     for line in sys.stdin:
@@ -44,8 +43,6 @@ def batch_predict(batch_size=4):
         for i in range(len(feed_batch)):
             print("{} {}".format(fetch_batch[i]["prediction"][1], feed_batch[i][
                 "label"][0]))
-    cost = time.time() - start
-    print("total cost : {}".format(cost))
 
 
 if __name__ == '__main__':

python/examples/imdb/test_gpu_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.
+
+import os
+import sys
+from paddle_serving_server_gpu import OpMaker
+from paddle_serving_server_gpu import OpSeqMaker
+from paddle_serving_server_gpu import Server
+
+op_maker = OpMaker()
+read_op = op_maker.create('general_reader')
+general_infer_op = op_maker.create('general_infer')
+
+op_seq_maker = OpSeqMaker()
+op_seq_maker.add_op(read_op)
+op_seq_maker.add_op(general_infer_op)
+
+server = Server()
+server.set_op_sequence(op_seq_maker.get_op_sequence())
+server.set_num_threads(12)
+server.load_model_config(sys.argv[1])
+port = int(sys.argv[2])
+server.prepare_server(workdir="work_dir1", port=port, device="gpu")
+server.run_server()

python/examples/imdb/test_server.py

@@ -29,7 +29,6 @@ op_seq_maker.add_op(general_infer_op)
 op_seq_maker.add_op(general_response_op)
 
 server = Server()
-server.set_vlog_level(3)
 server.set_op_sequence(op_seq_maker.get_op_sequence())
 server.set_num_threads(4)
 

python/paddle_serving_server/__init__.py

@@ -89,6 +89,7 @@ class Server(object):
         self.module_path = os.path.dirname(paddle_serving_server.__file__)
         self.cur_path = os.getcwd()
         self.use_local_bin = False
+        self.mkl_flag = False
 
     def set_max_concurrency(self, concurrency):
         self.max_concurrency = concurrency
@@ -172,16 +173,16 @@ class Server(object):
         # check config here
         # print config here
 
+    def use_mkl(self):
+        self.mkl_flag = True
+
     def get_device_version(self):
         avx_flag = False
-        mkl_flag = False
+        mkl_flag = self.mkl_flag
         openblas_flag = False
         r = os.system("cat /proc/cpuinfo | grep avx > /dev/null 2>&1")
         if r == 0:
             avx_flag = True
-        r = os.system("which mkl")
-        if r == 0:
-            mkl_flag = True
         if avx_flag:
             if mkl_flag:
                 device_version = "serving-cpu-avx-mkl-"