def run_rpc(thread, batch_size):
client = PipelineClient()
client.connect(['127.0.0.1:9998'])
value = "0.0137, -0.1136, 0.2553, -0.0692, 0.0582, -0.0727, -0.1583, -0.0584, 0.6283, 0.4919, 0.1856, 0.0795, -0.0332"
all_value = ";".join([value for i in range(batch_size)])
data = {"key": "x", "value": all_value}
for i in range(1000):
ret = client.predict(
feed_dict={data["key"]: data["value"]}, fetch=["res"])
print(ret)
def predict_pipeline_rpc(self, batch_size=1):
# 1.prepare feed_data
feed_dict = {'x': '0.0137, -0.1136, 0.2553, -0.0692, 0.0582, -0.0727, -0.1583, -0.0584, 0.6283, 0.4919, 0.1856, 0.0795, -0.0332'}
# 2.init client
client = PipelineClient()
client.connect(['127.0.0.1:9998'])
# 3.predict for fetch_map
ret = client.predict(feed_dict=feed_dict)
# 4.convert dict to numpy
result = {"prob": np.array(eval(ret.value[0]))}
return result
def run_rpc(thread, batch_size):
client = PipelineClient()
client.connect(['127.0.0.1:18080'])
start = time.time()
test_img_dir = "imgs/"
for img_file in os.listdir(test_img_dir):
with open(os.path.join(test_img_dir, img_file), 'rb') as file:
image_data = file.read()
image = cv2_to_base64(image_data)
start_time = time.time()
while True:
ret = client.predict(feed_dict={"image": image}, fetch=["res"])
if time.time() - start_time > 10:
break
end = time.time()
return [[end - start]]
def run_rpc(thread, batch_size):
client = PipelineClient()
client.connect(['127.0.0.1:9998'])
with open("data-c.txt", 'r') as fin:
start = time.time()
lines = fin.readlines()
start_idx = 0
while start_idx < len(lines):
end_idx = min(len(lines), start_idx + batch_size)
feed = {}
for i in range(start_idx, end_idx):
feed[str(i - start_idx)] = lines[i]
ret = client.predict(feed_dict=feed, fetch=["res"])
start_idx += batch_size
if start_idx > 1000:
break
end = time.time()
return [[end - start]]
#
# 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 sys
import os
import yaml
import requests
import time
import json
from paddle_serving_server.pipeline import PipelineClient
import numpy as np
client = PipelineClient()
client.connect(['127.0.0.1:9998'])
batch_size = 101
with open("data-c.txt", 'r') as fin:
lines = fin.readlines()
start_idx = 0
while start_idx < len(lines):
end_idx = min(len(lines), start_idx + batch_size)
feed = {}
for i in range(start_idx, end_idx):
feed[str(i - start_idx)] = lines[i]
ret = client.predict(feed_dict=feed, fetch=["res"])
print(ret)
start_idx += batch_size
for line in results:
for item in line:
idx = item.id
distance = item.distance
text = id2corpus[idx]
print(text, distance)
list_data.append([query_text, text, distance])
df = pd.DataFrame(list_data, columns=['query_text', 'text', 'distance'])
df = df.sort_values(by="distance", ascending=True)
df.to_csv('data/recall_predict.csv',
columns=['text', 'distance'],
sep='\t',
header=None,
index=False)
if __name__ == "__main__":
client = PipelineClient()
client.connect(['127.0.0.1:8080'])
list_data = ["买了社保，是不是就不用买商业保险了？"]
feed = {}
for i, item in enumerate(list_data):
feed[str(i)] = item
start_time = time.time()
ret = client.predict(feed_dict=feed)
end_time = time.time()
print("Extract feature time to cost :{} seconds".format(end_time -
start_time))
result = np.array(eval(ret.value[0]))
search_in_milvus(result, list_data[0])
def init_client():
client = PipelineClient()
client.connect(['127.0.0.1:18090'])
return client
#
# 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.pipeline import PipelineClient
import numpy as np
client = PipelineClient()
client.connect(['127.0.0.1:18070'])
words = 'i am very sad | 0'
futures = []
for i in range(100):
futures.append(
client.predict(feed_dict={
"words": words,
"logid": 10000 + i
},
fetch=["prediction"],
asyn=True,
profile=False))
# 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.
try:
from paddle_serving_server.pipeline import PipelineClient
except ImportError:
from paddle_serving_server.pipeline import PipelineClient
import numpy as np
import requests
import json
import cv2
import base64
import os
client = PipelineClient()
client.connect(['127.0.0.1:18090'])
def cv2_to_base64(image):
return base64.b64encode(image).decode('utf8')
test_img_dir = "imgs/"
for img_file in os.listdir(test_img_dir):
with open(os.path.join(test_img_dir, img_file), 'rb') as file:
image_data = file.read()
image = cv2_to_base64(image_data)
for i in range(1):
ret = client.predict(feed_dict={"image": image}, fetch=["res"])
#
# 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.pipeline import PipelineClient
import numpy as np
import requests
import json
import cv2
import base64
import os
client = PipelineClient()
client.connect(['127.0.0.1:9993'])
def cv2_to_base64(image):
return base64.b64encode(image).decode('utf8')
with open("daisy.jpg", 'rb') as file:
image_data = file.read()
image = cv2_to_base64(image_data)
for i in range(1):
ret = client.predict(feed_dict={"image": image}, fetch=["label", "prob"])
print(ret)
#
# 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.
try:
from paddle_serving_server.pipeline import PipelineClient
except ImportError:
from paddle_serving_server.pipeline import PipelineClient
import numpy as np
import requests
import json
import cv2
import base64
import os
client = PipelineClient()
client.connect(['127.0.0.1:18090'])
video_url = "https://paddle-serving.bj.bcebos.com/model/PaddleVideo/example.avi"
for i in range(1):
ret = client.predict(feed_dict={"video_url": video_url}, fetch=["res"])
print(ret)