add gpu web service scripts

python/examples/imagenet/get_model.sh

-wget https://paddle-serving.bj.bcebos.com/imagenet-example/conf_and_model.tar.gz
+wget --no-check-certificate https://paddle-serving.bj.bcebos.com/imagenet-example/conf_and_model.tar.gz
 tar -xzvf conf_and_model.tar.gz

python/examples/imagenet/image_classification_service_gpu.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.
+
+from paddle_serving_server_gpu.web_service import WebService
+import sys
+import os
+import base64
+from image_reader import ImageReader
+
+class ImageService(WebService):
+    def preprocess(self, feed={}, fetch=[]):
+        reader = ImageReader()
+        if "image" not in feed:
+            raise ("feed data error!")
+        sample = base64.b64decode(feed["image"])
+        img = reader.process_image(sample)
+        res_feed = {}
+        res_feed["image"] = img.reshape(-1)
+        return res_feed, fetch
+
+
+image_service = ImageService(name="image")
+image_service.load_model_config(sys.argv[1])
+gpu_ids = os.environ["CUDA_VISIBLE_DEVICES"]
+gpus = [int(x) for x in gpu_ids.split(",")]
+image_service.set_gpus(gpus)
+image_service.prepare_server(
+    workdir=sys.argv[2], port=int(sys.argv[3]), device="gpu")
+image_service.run_server()

python/examples/imagenet/image_http_client.py

@@ -15,17 +15,22 @@
 import requests
 import base64
 import json
-
+import time
 
 def predict(image_path, server):
     image = base64.b64encode(open(image_path).read())
     req = json.dumps({"image": image, "fetch": ["score"]})
     r = requests.post(
         server, data=req, headers={"Content-Type": "application/json"})
-    print(r.json()["score"])
+    #print(r.json()["score"])
 
 
 if __name__ == "__main__":
-    server = "http://127.0.0.1:9393/image/prediction"
+    server = "http://127.0.0.1:9292/image/prediction"
     image_path = "./data/n01440764_10026.JPEG"
-    predict(image_path, server)
+    start = time.time()
+    for i in range(1000):
+        predict(image_path, server)
+        print(i)
+    end = time.time()
+    print(end - start)

python/examples/imagenet/image_rpc_client.py

@@ -15,15 +15,21 @@
 import sys
 from image_reader import ImageReader
 from paddle_serving_client import Client
+import time
 
 client = Client()
 client.load_client_config(sys.argv[1])
-client.connect(["127.0.0.1:9393"])
+client.connect(["127.0.0.1:9292"])
 reader = ImageReader()
-with open("./data/n01440764_10026.JPEG") as f:
-    img = f.read()
 
-img = reader.process_image(img).reshape(-1)
-fetch_map = client.predict(feed={"image": img}, fetch=["score"])
+start = time.time()
+for i in range(1000):
+    with open("./data/n01440764_10026.JPEG") as f:
+        img = f.read()
+    img = reader.process_image(img).reshape(-1)
+    fetch_map = client.predict(feed={"image": img}, fetch=["score"])
+    print(i)
+end = time.time()
+print(end - start)
 
-print(fetch_map["score"])
+#print(fetch_map["score"])