refine imdb demo

python/examples/imdb/benchmark.py

@@ -37,26 +37,39 @@ def single_func(idx, resource):
         client.load_client_config(args.model)
         client.connect([args.endpoint])
         for i in range(1000):
-            if args.batch_size == 1:
-                word_ids, label = imdb_dataset.get_words_and_label(line)
-                fetch_map = client.predict(
-                    feed={"words": word_ids}, fetch=["prediction"])
+            if args.batch_size >= 1:
+                feed_batch = []
+                for bi in range(args.batch_size):
+                    word_ids, label = imdb_dataset.get_words_and_label(dataset[
+                        bi])
+                    feed_batch.append({"words": word_ids})
+                result = client.predict(feed=feed_batch, fetch=["prediction"])
+                if result is None:
+                    raise ("predict failed.")
             else:
                 print("unsupport batch size {}".format(args.batch_size))
 
     elif args.request == "http":
-        for fn in filelist:
-            fin = open(fn)
-            for line in fin:
-                word_ids, label = imdb_dataset.get_words_and_label(line)
-                r = requests.post(
-                    "http://{}/imdb/prediction".format(args.endpoint),
-                    data={"words": word_ids,
-                          "fetch": ["prediction"]})
+        if args.batch_size >= 1:
+            feed_batch = []
+            for bi in range(args.batch_size):
+                feed_batch.append({"words": dataset[bi]})
+            r = requests.post(
+                "http://{}/imdb/prediction".format(args.endpoint),
+                json={"feed": feed_batch,
+                      "fetch": ["prediction"]})
+            if r.status_code != 200:
+                print('HTTP status code -ne 200')
+                raise ("predict failed.")
+        else:
+            print("unsupport batch size {}".format(args.batch_size))
     end = time.time()
     return [[end - start]]
 
 
 multi_thread_runner = MultiThreadRunner()
 result = multi_thread_runner.run(single_func, args.thread, {})
-print(result)
+avg_cost = 0
+for cost in result[0]:
+    avg_cost += cost
+print("total cost {} s of each thread".format(avg_cost / args.thread))

python/examples/imdb/benchmark.sh

 rm profile_log
 for thread_num in 1 2 4 8 16
 do
-    $PYTHONROOT/bin/python benchmark.py --thread $thread_num --model imdbo_bow_client_conf/serving_client_conf.prototxt --request rpc > profile 2>&1
+for batch_size in 1 2 4 8 16 32 64 128 256 512
+do
+    $PYTHONROOT/bin/python benchmark.py --thread $thread_num --batch_size $batch_size --model imdb_bow_client_conf/serving_client_conf.prototxt --request rpc > profile 2>&1
     echo "========================================"
     echo "batch size : $batch_size" >> profile_log
     $PYTHONROOT/bin/python ../util/show_profile.py profile $thread_num >> profile_log
     tail -n 1 profile >> profile_log
 done
+done

python/examples/imdb/benchmark_batch.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
-
-import sys
-import time
-import requests
-from imdb_reader import IMDBDataset
-from paddle_serving_client import Client
-from paddle_serving_client.utils import MultiThreadRunner
-from paddle_serving_client.utils import benchmark_args
-
-args = benchmark_args()
-
-
-def single_func(idx, resource):
-    imdb_dataset = IMDBDataset()
-    imdb_dataset.load_resource("./imdb.vocab")
-    dataset = []
-    with open("./test_data/part-0") as fin:
-        for line in fin:
-            dataset.append(line.strip())
-    start = time.time()
-    if args.request == "rpc":
-        client = Client()
-        client.load_client_config(args.model)
-        client.connect([args.endpoint])
-        for i in range(1000):
-            if args.batch_size >= 1:
-                feed_batch = []
-                for bi in range(args.batch_size):
-                    word_ids, label = imdb_dataset.get_words_and_label(dataset[
-                        bi])
-                    feed_batch.append({"words": word_ids})
-                result = client.predict(feed=feed_batch, fetch=["prediction"])
-                if result is None:
-                    raise ("predict failed.")
-            else:
-                print("unsupport batch size {}".format(args.batch_size))
-
-    elif args.request == "http":
-        if args.batch_size >= 1:
-            feed_batch = []
-            for bi in range(args.batch_size):
-                feed_batch.append({"words": dataset[bi]})
-            r = requests.post(
-                "http://{}/imdb/prediction".format(args.endpoint),
-                json={"feed": feed_batch,
-                      "fetch": ["prediction"]})
-            if r.status_code != 200:
-                print('HTTP status code -ne 200')
-                raise ("predict failed.")
-        else:
-            print("unsupport batch size {}".format(args.batch_size))
-    end = time.time()
-    return [[end - start]]
-
-
-multi_thread_runner = MultiThreadRunner()
-result = multi_thread_runner.run(single_func, args.thread, {})
-avg_cost = 0
-for cost in result[0]:
-    avg_cost += cost
-print("total cost {} s of each thread".format(avg_cost / args.thread))

python/examples/imdb/benchmark_batch.sh

-rm profile_log
-for thread_num in 1 2 4 8 16
-do
-for batch_size in 1 2 4 8 16 32 64 128 256 512
-do
-    $PYTHONROOT/bin/python benchmark_batch.py --thread $thread_num --batch_size $batch_size --model imdb_bow_client_conf/serving_client_conf.prototxt --request rpc > profile 2>&1
-    echo "========================================"
-    echo "batch size : $batch_size" >> profile_log
-    $PYTHONROOT/bin/python ../util/show_profile.py profile $thread_num >> profile_log
-    tail -n 1 profile >> profile_log
-done
-done

python/examples/imdb/test_client_batch.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 Client
-import sys
-import subprocess
-from multiprocessing import Pool
-import time
-
-
-def batch_predict(batch_size=4):
-    client = Client()
-    client.load_client_config(conf_file)
-    client.connect(["127.0.0.1:9292"])
-    fetch = ["acc", "cost", "prediction"]
-    feed_batch = []
-    for line in sys.stdin:
-        group = line.strip().split()
-        words = [int(x) for x in group[1:int(group[0])]]
-        label = [int(group[-1])]
-        feed = {"words": words, "label": label}
-        feed_batch.append(feed)
-        if len(feed_batch) == batch_size:
-            fetch_batch = client.batch_predict(
-                feed_batch=feed_batch, fetch=fetch)
-            for i in range(batch_size):
-                print("{} {}".format(fetch_batch[i]["prediction"][1],
-                                     feed_batch[i]["label"][0]))
-            feed_batch = []
-    if len(feed_batch) > 0:
-        fetch_batch = client.batch_predict(feed_batch=feed_batch, fetch=fetch)
-        for i in range(len(feed_batch)):
-            print("{} {}".format(fetch_batch[i]["prediction"][1], feed_batch[i][
-                "label"][0]))
-
-
-if __name__ == '__main__':
-    conf_file = sys.argv[1]
-    batch_size = int(sys.argv[2])
-    batch_predict(batch_size)