提交 cf4a432e 编写于 作者: Z zhangjun

fix, remove webservcie for xpu example

上级 3661f972
......@@ -11,8 +11,8 @@ python3 -m paddle_serving_client.convert --dirname ernie
```
### or, you can get the serving saved model directly
```
wget https://paddle-serving.bj.bcebos.com/models/xpu/bert.tar.gz
tar zxvf bert.tar.gz
wget https://paddle-serving.bj.bcebos.com/models/xpu/ernie.tar.gz
tar zxvf ernie.tar.gz
```
### Getting Dict and Sample Dataset
......
......@@ -23,7 +23,7 @@ args = benchmark_args()
reader = ChineseErnieReader({"max_seq_len": 128})
fetch = ["save_infer_model/scale_0"]
endpoint_list = ['127.0.0.1:7704']
endpoint_list = ['127.0.0.1:12000']
client = Client()
client.load_client_config(args.model)
client.connect(endpoint_list)
......
......@@ -29,19 +29,3 @@ The `paddlepaddle` package is used in `test_client.py`, and you may need to down
python test_client.py uci_housing_client/serving_client_conf.prototxt
```
## HTTP service
### Start server
Start a web service with default web service hosting modules:
``` shell
python test_server.py
```
### Client prediction
``` shell
curl -H "Content-Type:application/json" -X POST -d '{"feed":[{"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]}], "fetch":["price"]}' http://127.0.0.1:9292/uci/prediction
```
......@@ -32,20 +32,3 @@ python -m paddle_serving_server.serve --model uci_housing_model --thread 10 --po
python test_client.py uci_housing_client/serving_client_conf.prototxt
```
## HTTP服务
### 开启服务端
通过下面的一行代码开启默认web服务:
``` shell
python test_server.py
```
### 客户端预测
``` shell
curl -H "Content-Type:application/json" -X POST -d '{"feed":[{"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]}], "fetch":["price"]}' http://127.0.0.1:9292/uci/prediction
```
# 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_client import Client
from paddle_serving_client.utils import MultiThreadRunner
import paddle
import numpy as np
def single_func(idx, resource):
client = Client()
client.load_client_config(
"./uci_housing_client/serving_client_conf.prototxt")
client.connect(["127.0.0.1:9293", "127.0.0.1:9292"])
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
]
x = np.array(x)
for i in range(1000):
fetch_map = client.predict(feed={"x": x}, fetch=["price"])
if fetch_map is None:
return [[None]]
return [[0]]
multi_thread_runner = MultiThreadRunner()
thread_num = 4
result = multi_thread_runner.run(single_func, thread_num, {})
if None in result[0]:
exit(1)
# 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.web_service import WebService
import numpy as np
class UciService(WebService):
def preprocess(self, feed=[], fetch=[]):
feed_batch = []
is_batch = True
new_data = np.zeros((len(feed), 1, 13)).astype("float32")
for i, ins in enumerate(feed):
nums = np.array(ins["x"]).reshape(1, 1, 13)
new_data[i] = nums
feed = {"x": new_data}
return feed, fetch, is_batch
uci_service = UciService(name="uci")
uci_service.load_model_config("uci_housing_model")
uci_service.prepare_server(
workdir="workdir", port=9393, use_lite=True, use_xpu=True, ir_optim=True)
uci_service.run_rpc_service()
uci_service.run_web_service()
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