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未验证 提交 d66a0016 编写于 作者: T TeslaZhao 提交者： GitHub
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Merge pull request #1348 from HexToString/grpc_update 

update http_session
上级 4cf08d4e 2c2a7218
隐藏空白更改
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Showing with 383 addition and 362 deletion
java/README_CN.md 100644 → 100755
浏览文件 @ d66a0016
...@@ -98,7 +98,7 @@ java -cp paddle-serving-sdk-java-examples-0.0.1-jar-with-dependencies.jar Pipeli ...@@ -98,7 +98,7 @@ java -cp paddle-serving-sdk-java-examples-0.0.1-jar-with-dependencies.jar Pipeli
### 注意事项 ### 注意事项
1.在示例中，端口号都是9393，ip默认设置为了0.0.0.0表示本机，注意ip和port需要与Server端对应。 1.在示例中，端口号都是9393，ip默认设置为了127.0.0.1表示本机，注意ip和port需要与Server端对应。
2.目前Serving已推出Pipeline模式（原理详见[Pipeline Serving](../doc/PIPELINE_SERVING_CN.md)），面向Java的Pipeline Serving Client已发布。 2.目前Serving已推出Pipeline模式（原理详见[Pipeline Serving](../doc/PIPELINE_SERVING_CN.md)），面向Java的Pipeline Serving Client已发布。
......
java/examples/src/main/java/PaddleServingClientExample.java
浏览文件 @ d66a0016
...@@ -25,7 +25,7 @@ public class PaddleServingClientExample { ...@@ -25,7 +25,7 @@ public class PaddleServingClientExample {
List<String> fetch = Arrays.asList("price"); List<String> fetch = Arrays.asList("price");
Client client = new Client(); Client client = new Client();
client.setIP("0.0.0.0"); client.setIP("127.0.0.1");
client.setPort("9393"); client.setPort("9393");
client.loadClientConfig(model_config_path); client.loadClientConfig(model_config_path);
String result = client.predict(feed_data, fetch, true, 0); String result = client.predict(feed_data, fetch, true, 0);
...@@ -49,7 +49,7 @@ public class PaddleServingClientExample { ...@@ -49,7 +49,7 @@ public class PaddleServingClientExample {
Client client = new Client(); Client client = new Client();
//注意：跨docker，需要设置--net-host或直接访问另一个docker的ip //注意：跨docker，需要设置--net-host或直接访问另一个docker的ip
client.setIP("0.0.0.0"); client.setIP("127.0.0.1");
client.setPort("9393"); client.setPort("9393");
client.set_http_proto(false); client.set_http_proto(false);
client.loadClientConfig(model_config_path); client.loadClientConfig(model_config_path);
...@@ -73,7 +73,7 @@ public class PaddleServingClientExample { ...@@ -73,7 +73,7 @@ public class PaddleServingClientExample {
List<String> fetch = Arrays.asList("price"); List<String> fetch = Arrays.asList("price");
Client client = new Client(); Client client = new Client();
client.setIP("0.0.0.0"); client.setIP("127.0.0.1");
client.setPort("9393"); client.setPort("9393");
client.loadClientConfig(model_config_path); client.loadClientConfig(model_config_path);
client.set_use_grpc_client(true); client.set_use_grpc_client(true);
...@@ -97,7 +97,7 @@ public class PaddleServingClientExample { ...@@ -97,7 +97,7 @@ public class PaddleServingClientExample {
List<String> fetch = Arrays.asList("price"); List<String> fetch = Arrays.asList("price");
Client client = new Client(); Client client = new Client();
client.setIP("0.0.0.0"); client.setIP("127.0.0.1");
client.setPort("9393"); client.setPort("9393");
client.loadClientConfig(model_config_path); client.loadClientConfig(model_config_path);
client.use_key(keyFilePath); client.use_key(keyFilePath);
...@@ -125,7 +125,7 @@ public class PaddleServingClientExample { ...@@ -125,7 +125,7 @@ public class PaddleServingClientExample {
List<String> fetch = Arrays.asList("price"); List<String> fetch = Arrays.asList("price");
Client client = new Client(); Client client = new Client();
client.setIP("0.0.0.0"); client.setIP("127.0.0.1");
client.setPort("9393"); client.setPort("9393");
client.loadClientConfig(model_config_path); client.loadClientConfig(model_config_path);
client.set_request_compress(true); client.set_request_compress(true);
...@@ -176,7 +176,7 @@ public class PaddleServingClientExample { ...@@ -176,7 +176,7 @@ public class PaddleServingClientExample {
}}; }};
List<String> fetch = Arrays.asList("save_infer_model/scale_0.tmp_0"); List<String> fetch = Arrays.asList("save_infer_model/scale_0.tmp_0");
Client client = new Client(); Client client = new Client();
client.setIP("0.0.0.0"); client.setIP("127.0.0.1");
client.setPort("9393"); client.setPort("9393");
client.loadClientConfig(model_config_path); client.loadClientConfig(model_config_path);
String result = client.predict(feed_data, fetch, true, 0); String result = client.predict(feed_data, fetch, true, 0);
...@@ -198,7 +198,7 @@ public class PaddleServingClientExample { ...@@ -198,7 +198,7 @@ public class PaddleServingClientExample {
}}; }};
List<String> fetch = Arrays.asList("pooled_output"); List<String> fetch = Arrays.asList("pooled_output");
Client client = new Client(); Client client = new Client();
client.setIP("0.0.0.0"); client.setIP("127.0.0.1");
client.setPort("9393"); client.setPort("9393");
client.loadClientConfig(model_config_path); client.loadClientConfig(model_config_path);
String result = client.predict(feed_data, fetch, true, 0); String result = client.predict(feed_data, fetch, true, 0);
...@@ -268,7 +268,7 @@ public class PaddleServingClientExample { ...@@ -268,7 +268,7 @@ public class PaddleServingClientExample {
}}; }};
List<String> fetch = Arrays.asList("prob"); List<String> fetch = Arrays.asList("prob");
Client client = new Client(); Client client = new Client();
client.setIP("0.0.0.0"); client.setIP("127.0.0.1");
client.setPort("9393"); client.setPort("9393");
client.loadClientConfig(model_config_path); client.loadClientConfig(model_config_path);
String result = client.predict(feed_data, fetch, true, 0); String result = client.predict(feed_data, fetch, true, 0);
......
java/src/main/java/io/paddle/serving/client/Client.java
浏览文件 @ d66a0016
...@@ -134,7 +134,7 @@ public class Client { ...@@ -134,7 +134,7 @@ public class Client {
feedTensorLen_ = null; feedTensorLen_ = null;
feedNameToIndex_ = null; feedNameToIndex_ = null;
timeoutS_ = 200000; timeoutS_ = 200000;
ip = "0.0.0.0"; ip = "127.0.0.1";
port = "9393"; port = "9393";
serverPort = "9393"; serverPort = "9393";
serviceName = "/GeneralModelService/inference"; serviceName = "/GeneralModelService/inference";
......
python/examples/bert/README.md 100644 → 100755
浏览文件 @ d66a0016
## Bert as service Http## Bert as service
([简体中文](./README_CN.md)|English) ([简体中文](./README_CN.md)|English)
...@@ -42,7 +42,7 @@ sh get_data.sh ...@@ -42,7 +42,7 @@ sh get_data.sh
``` ```
this script will download Chinese Dictionary File vocab.txt and Chinese Sample Data data-c.txt this script will download Chinese Dictionary File vocab.txt and Chinese Sample Data data-c.txt
### RPC Inference Service ### Inference Service(Support BRPC-Client、GRPC-Client、Http-Client)
start cpu inference service,Run start cpu inference service,Run
``` ```
python -m paddle_serving_server.serve --model bert_seq128_model/ --port 9292 #cpu inference service python -m paddle_serving_server.serve --model bert_seq128_model/ --port 9292 #cpu inference service
...@@ -52,7 +52,7 @@ Or,start gpu inference service,Run ...@@ -52,7 +52,7 @@ Or,start gpu inference service,Run
python -m paddle_serving_server.serve --model bert_seq128_model/ --port 9292 --gpu_ids 0 #launch gpu inference service at GPU 0 python -m paddle_serving_server.serve --model bert_seq128_model/ --port 9292 --gpu_ids 0 #launch gpu inference service at GPU 0
``` ```
### RPC Inference ### BRPC-Client Inference
before prediction we should install paddle_serving_app. This module provides data preprocessing for BERT model. before prediction we should install paddle_serving_app. This module provides data preprocessing for BERT model.
``` ```
...@@ -65,25 +65,13 @@ head data-c.txt | python bert_client.py --model bert_seq128_client/serving_clien ...@@ -65,25 +65,13 @@ head data-c.txt | python bert_client.py --model bert_seq128_client/serving_clien
the client reads data from data-c.txt and send prediction request, the prediction is given by word vector. (Due to massive data in the word vector, we do not print it). the client reads data from data-c.txt and send prediction request, the prediction is given by word vector. (Due to massive data in the word vector, we do not print it).
### HTTP Inference Service #### GRPC-Client/HTTP-Client
start cpu HTTP inference service,Run Run
```
python bert_web_service.py bert_seq128_model/ 9292 #launch cpu inference service
``` ```
head data-c.txt | python bert_httpclient.py --model bert_seq128_client/serving_client_conf.prototxt
Or,start gpu HTTP inference service,Run
```
export CUDA_VISIBLE_DEVICES=0,1
```
set environmental variable to specify which gpus are used, the command above means gpu 0 and gpu 1 is used.
``` ```
python bert_web_service_gpu.py bert_seq128_model/ 9292 #launch gpu inference service
```
### HTTP Inference
```
curl -H "Content-Type:application/json" -X POST -d '{"feed":[{"words": "hello"}], "fetch":["pooled_output"]}' http://127.0.0.1:9292/bert/prediction
```
## Benchmark ## Benchmark
``` shell ``` shell
......
python/examples/bert/README_CN.md 100644 → 100755
浏览文件 @ d66a0016
...@@ -40,7 +40,7 @@ sh get_data.sh ...@@ -40,7 +40,7 @@ sh get_data.sh
``` ```
脚本将下载中文词典vocab.txt和中文样例数据data-c.txt 脚本将下载中文词典vocab.txt和中文样例数据data-c.txt
### 启动RPC预测服务 ### 启动预测服务（支持BRPC-Client、GRPC-Client、HTTP-Client三种方式访问）
启动cpu预测服务，执行 启动cpu预测服务，执行
``` ```
python -m paddle_serving_server.serve --model bert_seq128_model/ --port 9292 #启动cpu预测服务 python -m paddle_serving_server.serve --model bert_seq128_model/ --port 9292 #启动cpu预测服务
...@@ -58,6 +58,8 @@ python -m paddle_serving_server.serve --model bert_seq128_model/ --port 9292 --g ...@@ -58,6 +58,8 @@ python -m paddle_serving_server.serve --model bert_seq128_model/ --port 9292 --g
``` ```
pip install paddle_serving_app pip install paddle_serving_app
``` ```
#### BRPC-Client
执行 执行
``` ```
head data-c.txt | python bert_client.py --model bert_seq128_client/serving_client_conf.prototxt head data-c.txt | python bert_client.py --model bert_seq128_client/serving_client_conf.prototxt
...@@ -65,28 +67,11 @@ head data-c.txt | python bert_client.py --model bert_seq128_client/serving_clien ...@@ -65,28 +67,11 @@ head data-c.txt | python bert_client.py --model bert_seq128_client/serving_clien
``` ```
启动client读取data-c.txt中的数据进行预测，预测结果为文本的向量表示（由于数据较多，脚本中没有将输出进行打印），server端的地址在脚本中修改。 启动client读取data-c.txt中的数据进行预测，预测结果为文本的向量表示（由于数据较多，脚本中没有将输出进行打印），server端的地址在脚本中修改。
#### GRPC-Client/HTTP-Client
执行
### 启动HTTP预测服务
启动cpu HTTP预测服务，执行
```
python bert_web_service.py bert_seq128_model/ 9292 #启动CPU预测服务
```
或者，启动gpu HTTP预测服务，执行
```
export CUDA_VISIBLE_DEVICES=0,1
```
通过环境变量指定gpu预测服务使用的gpu，示例中指定索引为0和1的两块gpu
```
python bert_web_service_gpu.py bert_seq128_model/ 9292 #启动gpu预测服务
``` ```
head data-c.txt | python bert_httpclient.py --model bert_seq128_client/serving_client_conf.prototxt
### 执行预测
```
curl -H "Content-Type:application/json" -X POST -d '{"feed":[{"words": "hello"}], "fetch":["pooled_output"]}' http://127.0.0.1:9292/bert/prediction
``` ```
## 性能测试 ## 性能测试
......
python/examples/bert/bert_web_service.py → python/examples/bert/bert_httpclient.py
浏览文件 @ d66a0016
# coding=utf-8 # coding:utf-8
# pylint: disable=doc-string-missing
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. # Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
# #
# Licensed under the Apache License, Version 2.0 (the "License"); # Licensed under the Apache License, Version 2.0 (the "License");
...@@ -12,37 +13,47 @@ ...@@ -12,37 +13,47 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and # See the License for the specific language governing permissions and
# limitations under the License. # limitations under the License.
# pylint: disable=doc-string-missing
from paddle_serving_server.web_service import WebService
from paddle_serving_app.reader import ChineseBertReader
import sys import sys
import os from paddle_serving_client import HttpClient
from paddle_serving_client.utils import benchmark_args
from paddle_serving_app.reader import ChineseBertReader
import numpy as np import numpy as np
args = benchmark_args()
reader = ChineseBertReader({"max_seq_len": 128})
fetch = ["pooled_output"]
class BertService(WebService): client = HttpClient(ip='127.0.0.1', port='9292')
def load(self): client.load_client_config(args.model)
self.reader = ChineseBertReader({ #client.set_ip('127.0.0.1')
"vocab_file": "vocab.txt", #client.set_port('9292')
"max_seq_len": 128 '''
}) if you want use GRPC-client, set_use_grpc_client(True)
or you can directly use client.grpc_client_predict(...)
def preprocess(self, feed=[], fetch=[]): as for HTTP-client,set_use_grpc_client(False)(which is default)
feed_res = [] or you can directly use client.http_client_predict(...)
is_batch = False '''
for ins in feed: #client.set_use_grpc_client(True)
feed_dict = self.reader.process(ins["words"].encode("utf-8")) '''
for key in feed_dict.keys(): if you want to enable Encrypt Module,uncommenting the following line
feed_dict[key] = np.array(feed_dict[key]).reshape( '''
(len(feed_dict[key]), 1)) #client.use_key("./key")
feed_res.append(feed_dict) '''
return feed_res, fetch, is_batch if you want to compress,uncommenting the following line
'''
#client.set_response_compress(True)
#client.set_request_compress(True)
'''
we recommend use Proto data format in HTTP-body, set True(which is default)
if you want use JSON data format in HTTP-body, set False
'''
#client.set_http_proto(True)
bert_service = BertService(name="bert") for line in sys.stdin:
bert_service.load() feed_dict = reader.process(line)
bert_service.load_model_config(sys.argv[1]) for key in feed_dict.keys():
bert_service.prepare_server( feed_dict[key] = np.array(feed_dict[key]).reshape((128, 1))
workdir="workdir", port=int(sys.argv[2]), device="cpu") #print(feed_dict)
bert_service.run_rpc_service() result = client.predict(feed=feed_dict, fetch=fetch, batch=False)
bert_service.run_web_service() print(result)
python/examples/bert/bert_web_service_gpu.py 已删除 100644 → 0
浏览文件 @ 4cf08d4e
# coding=utf-8
# 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
from paddle_serving_app.reader import ChineseBertReader
import sys
import os
import numpy as np
class BertService(WebService):
def load(self):
self.reader = ChineseBertReader({
"vocab_file": "vocab.txt",
"max_seq_len": 128
})
def preprocess(self, feed=[], fetch=[]):
feed_res = []
is_batch = False
for ins in feed:
feed_dict = self.reader.process(ins["words"].encode("utf-8"))
for key in feed_dict.keys():
feed_dict[key] = np.array(feed_dict[key]).reshape(
(len(feed_dict[key]), 1))
feed_res.append(feed_dict)
return feed_res, fetch, is_batch
bert_service = BertService(name="bert")
bert_service.load()
bert_service.load_model_config(sys.argv[1])
bert_service.set_gpus("0")
bert_service.prepare_server(
workdir="workdir", port=int(sys.argv[2]), device="gpu")
bert_service.run_rpc_service()
bert_service.run_web_service()
python/examples/fit_a_line/README_CN.md 100644 → 100755
浏览文件 @ d66a0016
...@@ -9,9 +9,7 @@ sh get_data.sh ...@@ -9,9 +9,7 @@ sh get_data.sh
``` ```
## 开启服务端（支持BRPC-Client/GRPC Client/Http-Client）
## 开启服务端
```shell ```shell
python -m paddle_serving_server.serve --model uci_housing_model --thread 10 --port 9393 python -m paddle_serving_server.serve --model uci_housing_model --thread 10 --port 9393
...@@ -19,7 +17,7 @@ python -m paddle_serving_server.serve --model uci_housing_model --thread 10 --po ...@@ -19,7 +17,7 @@ python -m paddle_serving_server.serve --model uci_housing_model --thread 10 --po
## 客户端预测 ## 客户端预测
### 客户端RPC ### BRPC-Client
`test_client.py`中使用了`paddlepaddle`包，需要进行下载（`pip install paddlepaddle`）。 `test_client.py`中使用了`paddlepaddle`包，需要进行下载（`pip install paddlepaddle`）。
...@@ -27,7 +25,7 @@ python -m paddle_serving_server.serve --model uci_housing_model --thread 10 --po ...@@ -27,7 +25,7 @@ python -m paddle_serving_server.serve --model uci_housing_model --thread 10 --po
python test_client.py uci_housing_client/serving_client_conf.prototxt python test_client.py uci_housing_client/serving_client_conf.prototxt
``` ```
### 客户端Http预测 ### GRPC-Client/Http-Client
``` shell ``` shell
python test_httpclient.py uci_housing_client/serving_client_conf.prototxt python test_httpclient.py uci_housing_client/serving_client_conf.prototxt
......
python/examples/fit_a_line/test_httpclient.py
浏览文件 @ d66a0016
...@@ -13,13 +13,15 @@ ...@@ -13,13 +13,15 @@
# limitations under the License. # limitations under the License.
# pylint: disable=doc-string-missing # pylint: disable=doc-string-missing
from paddle_serving_client.httpclient import GeneralClient from paddle_serving_client.httpclient import HttpClient
import sys import sys
import numpy as np import numpy as np
import time import time
client = GeneralClient() client = HttpClient()
client.load_client_config(sys.argv[1]) client.load_client_config(sys.argv[1])
#client.set_ip('127.0.0.1')
#client.set_port('9393')
''' '''
if you want use GRPC-client, set_use_grpc_client(True) if you want use GRPC-client, set_use_grpc_client(True)
or you can directly use client.grpc_client_predict(...) or you can directly use client.grpc_client_predict(...)
......
python/examples/imagenet/README.md 100644 → 100755
浏览文件 @ d66a0016
...@@ -15,23 +15,8 @@ sh get_model.sh ...@@ -15,23 +15,8 @@ sh get_model.sh
pip install paddle_serving_app pip install paddle_serving_app
``` ```
### HTTP Service
launch server side
```
python resnet50_web_service.py ResNet50_vd_model cpu 9696 #cpu inference service
```
```
python resnet50_web_service.py ResNet50_vd_model gpu 9696 #gpu inference service
```
### Inference Service(Support BRPC-Client/GRPC-Client/Http-Client)
client send inference request
```
curl -H "Content-Type:application/json" -X POST -d '{"feed":[{"image": "https://paddle-serving.bj.bcebos.com/imagenet-example/daisy.jpg"}], "fetch": ["score"]}' http://127.0.0.1:9696/image/prediction
```
### RPC Service
launch server side launch server side
``` ```
...@@ -42,8 +27,15 @@ python -m paddle_serving_server.serve --model ResNet50_vd_model --port 9696 #cpu ...@@ -42,8 +27,15 @@ python -m paddle_serving_server.serve --model ResNet50_vd_model --port 9696 #cpu
python -m paddle_serving_server.serve --model ResNet50_vd_model --port 9696 --gpu_ids 0 #gpu inference service python -m paddle_serving_server.serve --model ResNet50_vd_model --port 9696 --gpu_ids 0 #gpu inference service
``` ```
### BRPC-Client
client send inference request client send inference request
``` ```
python resnet50_rpc_client.py ResNet50_vd_client_config/serving_client_conf.prototxt python resnet50_rpc_client.py ResNet50_vd_client_config/serving_client_conf.prototxt
``` ```
*the port of server side in this example is 9696 *the port of server side in this example is 9696
### GRPC-Client/Http-Client
client send inference request
```
python resnet50_http_client.py ResNet50_vd_client_config/serving_client_conf.prototxt
```
python/examples/imagenet/README_CN.md 100644 → 100755
浏览文件 @ d66a0016
...@@ -15,23 +15,7 @@ sh get_model.sh ...@@ -15,23 +15,7 @@ sh get_model.sh
pip install paddle_serving_app pip install paddle_serving_app
``` ```
### HTTP服务 ### 启动服务端（支持BRPC-Client、GRPC-Client、Http-Client）
启动server端
```
python resnet50_web_service.py ResNet50_vd_model cpu 9696 #cpu预测服务
```
```
python resnet50_web_service.py ResNet50_vd_model gpu 9696 #gpu预测服务
```
发送HTTP POST请求
```
curl -H "Content-Type:application/json" -X POST -d '{"feed":[{"image": "https://paddle-serving.bj.bcebos.com/imagenet-example/daisy.jpg"}], "fetch": ["score"]}' http://127.0.0.1:9696/image/prediction
```
### RPC服务
启动server端 启动server端
``` ```
...@@ -42,8 +26,16 @@ python -m paddle_serving_server.serve --model ResNet50_vd_model --port 9696 #cpu ...@@ -42,8 +26,16 @@ python -m paddle_serving_server.serve --model ResNet50_vd_model --port 9696 #cpu
python -m paddle_serving_server.serve --model ResNet50_vd_model --port 9696 --gpu_ids 0 #gpu预测服务 python -m paddle_serving_server.serve --model ResNet50_vd_model --port 9696 --gpu_ids 0 #gpu预测服务
``` ```
### BRPC-Client预测
client端进行预测 client端进行预测
``` ```
python resnet50_rpc_client.py ResNet50_vd_client_config/serving_client_conf.prototxt python resnet50_rpc_client.py ResNet50_vd_client_config/serving_client_conf.prototxt
``` ```
*server端示例中服务端口为9696端口 *server端示例中服务端口为9696端口
### GRPC-Client/Http-Client预测
client端进行预测
```
python resnet50_http_client.py ResNet50_vd_client_config/serving_client_conf.prototxt
```
python/examples/imagenet/resnet50_http_client.py 0 → 100644
浏览文件 @ d66a0016
# 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 sys
from paddle_serving_client import HttpClient
from paddle_serving_app.reader import Sequential, URL2Image, Resize
from paddle_serving_app.reader import CenterCrop, RGB2BGR, Transpose, Div, Normalize
import time
client = HttpClient(ip='127.0.0.1', port='9696')
client.load_client_config(sys.argv[1])
#client.set_ip('127.0.0.1')
#client.set_port('9292')
'''
if you want use GRPC-client, set_use_grpc_client(True)
or you can directly use client.grpc_client_predict(...)
as for HTTP-client,set_use_grpc_client(False)(which is default)
or you can directly use client.http_client_predict(...)
'''
#client.set_use_grpc_client(True)
'''
if you want to enable Encrypt Module,uncommenting the following line
'''
#client.use_key("./key")
'''
if you want to compress,uncommenting the following line
'''
#client.set_response_compress(True)
#client.set_request_compress(True)
'''
we recommend use Proto data format in HTTP-body, set True(which is default)
if you want use JSON data format in HTTP-body, set False
'''
#client.set_http_proto(True)
label_dict = {}
label_idx = 0
with open("imagenet.label") as fin:
for line in fin:
label_dict[label_idx] = line.strip()
label_idx += 1
seq = Sequential([
URL2Image(), Resize(256), CenterCrop(224), RGB2BGR(), Transpose((2, 0, 1)),
Div(255), Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225], True)
])
start = time.time()
image_file = "https://paddle-serving.bj.bcebos.com/imagenet-example/daisy.jpg"
for i in range(10):
img = seq(image_file)
fetch_map = client.predict(
feed={"image": img}, fetch=["score"], batch=False)
print(fetch_map)
end = time.time()
print(end - start)
python/examples/imagenet/resnet50_web_service.py 已删除 100644 → 0
浏览文件 @ 4cf08d4e
# 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 sys
from paddle_serving_client import Client
import numpy as np
from paddle_serving_app.reader import Sequential, URL2Image, Resize, CenterCrop, RGB2BGR, Transpose, Div, Normalize, Base64ToImage
if len(sys.argv) != 4:
print("python resnet50_web_service.py model device port")
sys.exit(-1)
device = sys.argv[2]
if device == "cpu":
from paddle_serving_server.web_service import WebService
else:
from paddle_serving_server.web_service import WebService
class ImageService(WebService):
def init_imagenet_setting(self):
self.seq = Sequential([
URL2Image(), Resize(256), CenterCrop(224), RGB2BGR(), Transpose(
(2, 0, 1)), Div(255), Normalize([0.485, 0.456, 0.406],
[0.229, 0.224, 0.225], True)
])
self.label_dict = {}
label_idx = 0
with open("imagenet.label") as fin:
for line in fin:
self.label_dict[label_idx] = line.strip()
label_idx += 1
def preprocess(self, feed=[], fetch=[]):
feed_batch = []
is_batch = True
for ins in feed:
if "image" not in ins:
raise ("feed data error!")
img = self.seq(ins["image"])
feed_batch.append({"image": img[np.newaxis, :]})
return feed_batch, fetch, is_batch
def postprocess(self, feed=[], fetch=[], fetch_map={}):
score_list = fetch_map["score"]
result = {"label": [], "prob": []}
for score in score_list:
score = score.tolist()
max_score = max(score)
result["label"].append(self.label_dict[score.index(max_score)]
.strip().replace(",", ""))
result["prob"].append(max_score)
return result
image_service = ImageService(name="image")
image_service.load_model_config(sys.argv[1])
image_service.init_imagenet_setting()
if device == "gpu":
image_service.set_gpus("0")
image_service.prepare_server(
workdir="workdir", port=int(sys.argv[3]), device=device)
image_service.run_rpc_service()
image_service.run_web_service()
python/examples/imdb/README.md 100644 → 100755
浏览文件 @ d66a0016
...@@ -9,24 +9,20 @@ sh get_data.sh ...@@ -9,24 +9,20 @@ sh get_data.sh
``` ```
the package downloaded contains cnn, lstm and bow model config along with their test_data and train_data. the package downloaded contains cnn, lstm and bow model config along with their test_data and train_data.
### Start RPC inference service ### Start inference service(Support BRPC-Client/GRPC-Client/Http-Client)
``` ```
python -m paddle_serving_server.serve --model imdb_cnn_model/ --port 9292 python -m paddle_serving_server.serve --model imdb_cnn_model/ --port 9292
``` ```
### RPC Infer ### BRPC-Client Infer
``` ```
head test_data/part-0 | python test_client.py imdb_cnn_client_conf/serving_client_conf.prototxt imdb.vocab head test_data/part-0 | python test_client.py imdb_cnn_client_conf/serving_client_conf.prototxt imdb.vocab
``` ```
it will get predict results of the first 10 test cases. it will get predict results of the first 10 test cases.
### Start HTTP inference service
```
python text_classify_service.py imdb_cnn_model/ workdir/ 9292 imdb.vocab
```
### HTTP Infer
### GRPC-Client/Http-Client Infer
``` ```
curl -H "Content-Type:application/json" -X POST -d '{"feed":[{"words": "i am very sad | 0"}], "fetch":["prediction"]}' http://127.0.0.1:9292/imdb/prediction head test_data/part-0 | python test_http_client.py imdb_cnn_client_conf/serving_client_conf.prototxt imdb.vocab
``` ```
python/examples/imdb/README_CN.md 100644 → 100755
浏览文件 @ d66a0016
...@@ -9,23 +9,18 @@ sh get_data.sh ...@@ -9,23 +9,18 @@ sh get_data.sh
``` ```
脚本会下载和解压出cnn、lstm和bow三种模型的配置文文件以及test_data和train_data。 脚本会下载和解压出cnn、lstm和bow三种模型的配置文文件以及test_data和train_data。
### 启动RPC预测服务 ### 启动预测服务(支持BRPC-Client/GRPC-Client/Http-Client)
``` ```
python -m paddle_serving_server.serve --model imdb_cnn_model/ --port 9292 python -m paddle_serving_server.serve --model imdb_cnn_model/ --port 9292
``` ```
### 执行预测 ### BRPC-Client预测
``` ```
head test_data/part-0 | python test_client.py imdb_cnn_client_conf/serving_client_conf.prototxt imdb.vocab head test_data/part-0 | python test_client.py imdb_cnn_client_conf/serving_client_conf.prototxt imdb.vocab
``` ```
预测test_data/part-0的前十个样例。 预测test_data/part-0的前十个样例。
### 启动HTTP预测服务 ### BRPC-Client预测
``` ```
python text_classify_service.py imdb_cnn_model/ workdir/ 9292 imdb.vocab head test_data/part-0 | python test_http_client.py imdb_cnn_client_conf/serving_client_conf.prototxt imdb.vocab
```
### 执行预测
```
curl -H "Content-Type:application/json" -X POST -d '{"feed":[{"words": "i am very sad | 0"}], "fetch":["prediction"]}' http://127.0.0.1:9292/imdb/prediction
``` ```
python/examples/imdb/imdb_web_service_demo.sh 已删除 100644 → 0
浏览文件 @ 4cf08d4e
wget https://paddle-serving.bj.bcebos.com/imdb-demo/imdb_service.tar.gz
tar -xzf imdb_service.tar.gz
wget --no-check-certificate https://fleet.bj.bcebos.com/text_classification_data.tar.gz
tar -zxvf text_classification_data.tar.gz
python text_classify_service.py serving_server_model/ workdir imdb.vocab
python/examples/imdb/text_classify_service.py → python/examples/imdb/test_http_client.py 100644 → 100755
浏览文件 @ d66a0016
...@@ -12,37 +12,51 @@ ...@@ -12,37 +12,51 @@
# See the License for the specific language governing permissions and # See the License for the specific language governing permissions and
# limitations under the License. # limitations under the License.
# pylint: disable=doc-string-missing # pylint: disable=doc-string-missing
from paddle_serving_client import HttpClient
from paddle_serving_server.web_service import WebService
from paddle_serving_app.reader.imdb_reader import IMDBDataset from paddle_serving_app.reader.imdb_reader import IMDBDataset
import sys import sys
import numpy as np import numpy as np
client = HttpClient(ip='127.0.0.1', port='9292')
client.load_client_config(sys.argv[1])
#client.set_ip('127.0.0.1')
#client.set_port('9292')
'''
if you want use GRPC-client, set_use_grpc_client(True)
or you can directly use client.grpc_client_predict(...)
as for HTTP-client,set_use_grpc_client(False)(which is default)
or you can directly use client.http_client_predict(...)
'''
#client.set_use_grpc_client(True)
'''
if you want to enable Encrypt Module,uncommenting the following line
'''
#client.use_key("./key")
'''
if you want to compress,uncommenting the following line
'''
#client.set_response_compress(True)
#client.set_request_compress(True)
'''
we recommend use Proto data format in HTTP-body, set True(which is default)
if you want use JSON data format in HTTP-body, set False
'''
#client.set_http_proto(True)
class IMDBService(WebService): # you can define any english sentence or dataset here
def prepare_dict(self, args={}): # This example reuses imdb reader in training, you
if len(args) == 0: # can define your own data preprocessing easily.
exit(-1) imdb_dataset = IMDBDataset()
self.dataset = IMDBDataset() imdb_dataset.load_resource(sys.argv[2])
self.dataset.load_resource(args["dict_file_path"])
def preprocess(self, feed={}, fetch=[]):
feed_batch = []
words_lod = [0]
is_batch = True
for ins in feed:
words = self.dataset.get_words_only(ins["words"])
words = np.array(words).reshape(len(words), 1)
words_lod.append(words_lod[-1] + len(words))
feed_batch.append(words)
feed = {"words": np.concatenate(feed_batch), "words.lod": words_lod}
return feed, fetch, is_batch
imdb_service = IMDBService(name="imdb") for line in sys.stdin:
imdb_service.load_model_config(sys.argv[1]) word_ids, label = imdb_dataset.get_words_and_label(line)
imdb_service.prepare_server( word_len = len(word_ids)
workdir=sys.argv[2], port=int(sys.argv[3]), device="cpu") feed = {
imdb_service.prepare_dict({"dict_file_path": sys.argv[4]}) "words": np.array(word_ids).reshape(word_len, 1),
imdb_service.run_rpc_service() "words.lod": [0, word_len]
imdb_service.run_web_service() }
#print(feed)
fetch = ["prediction"]
fetch_map = client.predict(feed=feed, fetch=fetch, batch=True)
print(fetch_map)
python/examples/lac/README.md 100644 → 100755
浏览文件 @ d66a0016
...@@ -8,24 +8,19 @@ python -m paddle_serving_app.package --get_model lac ...@@ -8,24 +8,19 @@ python -m paddle_serving_app.package --get_model lac
tar -xzvf lac.tar.gz tar -xzvf lac.tar.gz
``` ```
#### Start RPC inference service #### Start inference service(Support BRPC-Client/GRPC-Client/Http-Client)
``` ```
python -m paddle_serving_server.serve --model lac_model/ --port 9292 python -m paddle_serving_server.serve --model lac_model/ --port 9292
``` ```
### RPC Infer ### BRPC Infer
``` ```
echo "我爱北京天安门" | python lac_client.py lac_client/serving_client_conf.prototxt echo "我爱北京天安门" | python lac_client.py lac_client/serving_client_conf.prototxt
``` ```
It will get the segmentation result. It will get the segmentation result.
### Start HTTP inference service ### GRPC/Http Infer
``` ```
python lac_web_service.py lac_model/ lac_workdir 9292 echo "我爱北京天安门" | python lac_http_client.py lac_client/serving_client_conf.prototxt
```
### HTTP Infer
```
curl -H "Content-Type:application/json" -X POST -d '{"feed":[{"words": "我爱北京天安门"}], "fetch":["word_seg"]}' http://127.0.0.1:9292/lac/prediction
``` ```
python/examples/lac/README_CN.md 100644 → 100755
浏览文件 @ d66a0016
...@@ -8,24 +8,19 @@ python -m paddle_serving_app.package --get_model lac ...@@ -8,24 +8,19 @@ python -m paddle_serving_app.package --get_model lac
tar -xzvf lac.tar.gz tar -xzvf lac.tar.gz
``` ```
#### 开启RPC预测服务 #### 开启预测服务(支持BRPC-Client/GRPC-Client/Http-Client)
``` ```
python -m paddle_serving_server.serve --model lac_model/ --port 9292 python -m paddle_serving_server.serve --model lac_model/ --port 9292
``` ```
### 执行RPC预测 ### 执行BRPC预测
``` ```
echo "我爱北京天安门" | python lac_client.py lac_client/serving_client_conf.prototxt echo "我爱北京天安门" | python lac_client.py lac_client/serving_client_conf.prototxt
``` ```
我们就能得到分词结果 我们就能得到分词结果
### 开启HTTP预测服务 ### 执行GRPC/Http预测
``` ```
python lac_web_service.py lac_model/ lac_workdir 9292 echo "我爱北京天安门" | python lac_http_client.py lac_client/serving_client_conf.prototxt
```
### 执行HTTP预测
```
curl -H "Content-Type:application/json" -X POST -d '{"feed":[{"words": "我爱北京天安门"}], "fetch":["word_seg"]}' http://127.0.0.1:9292/lac/prediction
``` ```
python/examples/lac/lac_http_client.py 100644 → 100755
浏览文件 @ d66a0016
# encoding=utf-8
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. # Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
# #
# Licensed under the Apache License, Version 2.0 (the "License"); # Licensed under the Apache License, Version 2.0 (the "License");
...@@ -11,17 +12,56 @@ ...@@ -11,17 +12,56 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and # See the License for the specific language governing permissions and
# limitations under the License. # limitations under the License.
#coding=utf-8 # pylint: disable=doc-string-missing
import requests
import json
import time
if __name__ == "__main__": from paddle_serving_client import HttpClient
server = "http://127.0.0.1:9280/lac/prediction" from paddle_serving_app.reader import LACReader
fin = open("jieba_test.txt", "r") import sys
start = time.time() import os
for line in fin: import io
req_data = {"words": line.strip(), "fetch": ["crf_decode"]} import numpy as np
r = requests.post(server, json=req_data)
end = time.time() client = HttpClient(ip='127.0.0.1', port='9292')
print(end - start) client.load_client_config(sys.argv[1])
#client.set_ip('127.0.0.1')
#client.set_port('9292')
'''
if you want use GRPC-client, set_use_grpc_client(True)
or you can directly use client.grpc_client_predict(...)
as for HTTP-client,set_use_grpc_client(False)(which is default)
or you can directly use client.http_client_predict(...)
'''
#client.set_use_grpc_client(True)
'''
if you want to enable Encrypt Module,uncommenting the following line
'''
#client.use_key("./key")
'''
if you want to compress,uncommenting the following line
'''
#client.set_response_compress(True)
#client.set_request_compress(True)
'''
we recommend use Proto data format in HTTP-body, set True(which is default)
if you want use JSON data format in HTTP-body, set False
'''
#client.set_http_proto(True)
reader = LACReader()
for line in sys.stdin:
if len(line) <= 0:
continue
feed_data = reader.process(line)
if len(feed_data) <= 0:
continue
print(feed_data)
#fetch_map = client.predict(feed={"words": np.array(feed_data).reshape(len(feed_data), 1), "words.lod": [0, len(feed_data)]}, fetch=["crf_decode"], batch=True)
fetch_map = client.predict(
feed={
"words": np.array(feed_data + feed_data).reshape(
len(feed_data) * 2, 1),
"words.lod": [0, len(feed_data), 2 * len(feed_data)]
},
fetch=["crf_decode"],
batch=True)
print(fetch_map)
python/examples/lac/lac_web_service.py 已删除 100644 → 0
浏览文件 @ 4cf08d4e
# 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.web_service import WebService
import sys
from paddle_serving_app.reader import LACReader
import numpy as np
class LACService(WebService):
def load_reader(self):
self.reader = LACReader()
def preprocess(self, feed={}, fetch=[]):
feed_batch = []
fetch = ["crf_decode"]
lod_info = [0]
is_batch = True
for ins in feed:
if "words" not in ins:
raise ("feed data error!")
feed_data = self.reader.process(ins["words"])
feed_batch.append(np.array(feed_data).reshape(len(feed_data), 1))
lod_info.append(lod_info[-1] + len(feed_data))
feed_dict = {
"words": np.concatenate(
feed_batch, axis=0),
"words.lod": lod_info
}
return feed_dict, fetch, is_batch
def postprocess(self, feed={}, fetch=[], fetch_map={}):
batch_ret = []
for idx, ins in enumerate(feed):
begin = fetch_map['crf_decode.lod'][idx]
end = fetch_map['crf_decode.lod'][idx + 1]
segs = self.reader.parse_result(ins["words"],
fetch_map["crf_decode"][begin:end])
batch_ret.append({"word_seg": "|".join(segs)})
return batch_ret
lac_service = LACService(name="lac")
lac_service.load_model_config(sys.argv[1])
lac_service.load_reader()
lac_service.prepare_server(
workdir=sys.argv[2], port=int(sys.argv[3]), device="cpu")
lac_service.run_rpc_service()
lac_service.run_web_service()
python/paddle_serving_client/client.py
浏览文件 @ d66a0016
...@@ -304,16 +304,20 @@ class Client(object): ...@@ -304,16 +304,20 @@ class Client(object):
if isinstance(feed, dict): if isinstance(feed, dict):
feed_batch.append(feed) feed_batch.append(feed)
elif isinstance(feed, list): elif isinstance(feed, list):
# if input is a list and the number of feed_var is 1. # feed = [dict]
# create a temp_dict { key = feed_var_name, value = list} if len(feed) == 1 and isinstance(feed[0], dict):
# put the temp_dict into the feed_batch. feed_batch = feed
if len(self.feed_names_) != 1: else:
raise ValueError( # if input is a list and the number of feed_var is 1.
"input is a list, but we got 0 or 2+ feed_var, don`t know how to divide the feed list" # create a temp_dict { key = feed_var_name, value = list}
) # put the temp_dict into the feed_batch.
temp_dict = {} if len(self.feed_names_) != 1:
temp_dict[self.feed_names_[0]] = feed raise ValueError(
feed_batch.append(temp_dict) "input is a list, but we got 0 or 2+ feed_var, don`t know how to divide the feed list"
)
temp_dict = {}
temp_dict[self.feed_names_[0]] = feed
feed_batch.append(temp_dict)
else: else:
raise ValueError("Feed only accepts dict and list of dict") raise ValueError("Feed only accepts dict and list of dict")
......
python/paddle_serving_client/httpclient.py
浏览文件 @ d66a0016
...@@ -73,9 +73,9 @@ def data_bytes_number(datalist): ...@@ -73,9 +73,9 @@ def data_bytes_number(datalist):
# 可以直接调用需要的http_client_predict/grpc_client_predict # 可以直接调用需要的http_client_predict/grpc_client_predict
# 例如，如果想使用GRPC方式，set_use_grpc_client(True) # 例如，如果想使用GRPC方式，set_use_grpc_client(True)
# 或者直接调用grpc_client_predict() # 或者直接调用grpc_client_predict()
class GeneralClient(object): class HttpClient(object):
def __init__(self, def __init__(self,
ip="0.0.0.0", ip="127.0.0.1",
port="9393", port="9393",
service_name="/GeneralModelService/inference"): service_name="/GeneralModelService/inference"):
self.feed_names_ = [] self.feed_names_ = []
...@@ -84,7 +84,7 @@ class GeneralClient(object): ...@@ -84,7 +84,7 @@ class GeneralClient(object):
self.feed_shapes_ = {} self.feed_shapes_ = {}
self.feed_types_ = {} self.feed_types_ = {}
self.feed_names_to_idx_ = {} self.feed_names_to_idx_ = {}
self.timeout_ms = 200000 self.timeout_ms = 20000
self.ip = ip self.ip = ip
self.port = port self.port = port
self.server_port = port self.server_port = port
...@@ -96,6 +96,17 @@ class GeneralClient(object): ...@@ -96,6 +96,17 @@ class GeneralClient(object):
self.http_proto = True self.http_proto = True
self.max_body_size = 512 * 1024 * 1024 self.max_body_size = 512 * 1024 * 1024
self.use_grpc_client = False self.use_grpc_client = False
# 使用连接池能够不用反复建立连接
self.requests_session = requests.session()
# 初始化grpc_stub
options = [('grpc.max_receive_message_length', self.max_body_size),
('grpc.max_send_message_length', self.max_body_size)]
endpoints = [self.ip + ":" + self.server_port]
g_endpoint = 'ipv4:{}'.format(','.join(endpoints))
self.channel_ = grpc.insecure_channel(g_endpoint, options=options)
self.stub_ = general_model_service_pb2_grpc.GeneralModelServiceStub(
self.channel_)
def load_client_config(self, model_config_path_list): def load_client_config(self, model_config_path_list):
if isinstance(model_config_path_list, str): if isinstance(model_config_path_list, str):
...@@ -155,6 +166,7 @@ class GeneralClient(object): ...@@ -155,6 +166,7 @@ class GeneralClient(object):
def set_max_body_size(self, max_body_size): def set_max_body_size(self, max_body_size):
self.max_body_size = max_body_size self.max_body_size = max_body_size
self.init_grpc_stub()
def set_timeout_ms(self, timeout_ms): def set_timeout_ms(self, timeout_ms):
if not isinstance(timeout_ms, int): if not isinstance(timeout_ms, int):
...@@ -162,14 +174,23 @@ class GeneralClient(object): ...@@ -162,14 +174,23 @@ class GeneralClient(object):
else: else:
self.timeout_ms = timeout_ms self.timeout_ms = timeout_ms
def set_max_retries(self, retry_times=3):
if not isinstance(retry_times, int):
raise ValueError("retry_times must be int type.")
else:
self.requests_session.mount(
'http://', HTTPAdapter(max_retries=retry_times))
def set_ip(self, ip): def set_ip(self, ip):
self.ip = ip self.ip = ip
self.init_grpc_stub()
def set_service_name(self, service_name): def set_service_name(self, service_name):
self.service_name = service_name self.service_name = service_name
def set_port(self, port): def set_port(self, port):
self.port = port self.port = port
self.init_grpc_stub()
def set_request_compress(self, try_request_gzip): def set_request_compress(self, try_request_gzip):
self.try_request_gzip = try_request_gzip self.try_request_gzip = try_request_gzip
...@@ -195,13 +216,14 @@ class GeneralClient(object): ...@@ -195,13 +216,14 @@ class GeneralClient(object):
req = json.dumps({"key": base64.b64encode(self.key).decode()}) req = json.dumps({"key": base64.b64encode(self.key).decode()})
else: else:
req = json.dumps({}) req = json.dumps({})
r = requests.post(encrypt_url, req) with requests.post(
result = r.json() encrypt_url, data=req, timeout=self.timeout_ms / 1000) as r:
if "endpoint_list" not in result: result = r.json()
raise ValueError("server not ready") if "endpoint_list" not in result:
else: raise ValueError("server not ready")
self.server_port = str(result["endpoint_list"][0]) else:
print("rpc port is ", self.server_port) self.server_port = str(result["endpoint_list"][0])
print("rpc port is ", self.server_port)
def get_feed_names(self): def get_feed_names(self):
return self.feed_names_ return self.feed_names_
...@@ -239,6 +261,10 @@ class GeneralClient(object): ...@@ -239,6 +261,10 @@ class GeneralClient(object):
if isinstance(feed, dict): if isinstance(feed, dict):
feed_dict = feed feed_dict = feed
elif isinstance(feed, (list, str, tuple)): elif isinstance(feed, (list, str, tuple)):
# feed = [dict]
if len(feed) == 1 and isinstance(feed[0], dict):
feed_dict = feed[0]
return feed_dict
# if input is a list or str or tuple, and the number of feed_var is 1. # if input is a list or str or tuple, and the number of feed_var is 1.
# create a feed_dict { key = feed_var_name, value = list} # create a feed_dict { key = feed_var_name, value = list}
if len(self.feed_names_) == 1: if len(self.feed_names_) == 1:
...@@ -443,8 +469,11 @@ class GeneralClient(object): ...@@ -443,8 +469,11 @@ class GeneralClient(object):
# 当数据区长度大于512字节时才压缩. # 当数据区长度大于512字节时才压缩.
try: try:
if self.try_request_gzip and self.total_data_number > 512: if self.try_request_gzip and self.total_data_number > 512:
origin_data = postData
postData = gzip.compress(bytes(postData, 'utf-8')) if self.http_proto:
postData = gzip.compress(postData)
else:
postData = gzip.compress(bytes(postData, 'utf-8'))
headers["Content-Encoding"] = "gzip" headers["Content-Encoding"] = "gzip"
if self.try_response_gzip: if self.try_response_gzip:
headers["Accept-encoding"] = "gzip" headers["Accept-encoding"] = "gzip"
...@@ -452,11 +481,14 @@ class GeneralClient(object): ...@@ -452,11 +481,14 @@ class GeneralClient(object):
except: except:
print("compress error, we will use the no-compress data") print("compress error, we will use the no-compress data")
headers.pop("Content-Encoding", "nokey") headers.pop("Content-Encoding", "nokey")
postData = origin_data
# requests支持自动识别解压 # requests支持自动识别解压
try: try:
result = requests.post(url=web_url, headers=headers, data=postData) result = self.requests_session.post(
url=web_url,
headers=headers,
data=postData,
timeout=self.timeout_ms / 1000)
result.raise_for_status()
except: except:
print("http post error") print("http post error")
return None return None
...@@ -484,6 +516,16 @@ class GeneralClient(object): ...@@ -484,6 +516,16 @@ class GeneralClient(object):
postData = self.process_proto_data(feed_dict, fetch_list, batch, log_id) postData = self.process_proto_data(feed_dict, fetch_list, batch, log_id)
try:
resp = self.stub_.inference(
postData, timeout=self.timeout_ms / 1000)
except:
print("Grpc inference error occur")
return None
else:
return resp
def init_grpc_stub(self):
# https://github.com/tensorflow/serving/issues/1382 # https://github.com/tensorflow/serving/issues/1382
options = [('grpc.max_receive_message_length', self.max_body_size), options = [('grpc.max_receive_message_length', self.max_body_size),
('grpc.max_send_message_length', self.max_body_size)] ('grpc.max_send_message_length', self.max_body_size)]
...@@ -493,10 +535,7 @@ class GeneralClient(object): ...@@ -493,10 +535,7 @@ class GeneralClient(object):
self.channel_ = grpc.insecure_channel(g_endpoint, options=options) self.channel_ = grpc.insecure_channel(g_endpoint, options=options)
self.stub_ = general_model_service_pb2_grpc.GeneralModelServiceStub( self.stub_ = general_model_service_pb2_grpc.GeneralModelServiceStub(
self.channel_) self.channel_)
try:
resp = self.stub_.inference(postData, timeout=self.timeout_ms) def __del__(self):
except: self.requests_session.close()
print("Grpc inference error occur") self.channel_.close()
return None
else:
return resp
python/paddle_serving_server/serve.py
浏览文件 @ d66a0016
...@@ -37,7 +37,7 @@ import socket ...@@ -37,7 +37,7 @@ import socket
def port_is_available(port): def port_is_available(port):
with closing(socket.socket(socket.AF_INET, socket.SOCK_STREAM)) as sock: with closing(socket.socket(socket.AF_INET, socket.SOCK_STREAM)) as sock:
sock.settimeout(2) sock.settimeout(2)
result = sock.connect_ex(('0.0.0.0', port)) result = sock.connect_ex(('127.0.0.1', port))
if result != 0: if result != 0:
return True return True
else: else:
......
python/paddle_serving_server/server.py 100644 → 100755
浏览文件 @ d66a0016
...@@ -537,7 +537,7 @@ class Server(object): ...@@ -537,7 +537,7 @@ class Server(object):
def port_is_available(self, port): def port_is_available(self, port):
with closing(socket.socket(socket.AF_INET, socket.SOCK_STREAM)) as sock: with closing(socket.socket(socket.AF_INET, socket.SOCK_STREAM)) as sock:
sock.settimeout(2) sock.settimeout(2)
result = sock.connect_ex(('0.0.0.0', port)) result = sock.connect_ex(('127.0.0.1', port))
if result != 0: if result != 0:
return True return True
else: else:
......
python/paddle_serving_server/web_service.py 100644 → 100755
浏览文件 @ d66a0016
...@@ -33,7 +33,7 @@ from paddle_serving_server.serve import format_gpu_to_strlist ...@@ -33,7 +33,7 @@ from paddle_serving_server.serve import format_gpu_to_strlist
def port_is_available(port): def port_is_available(port):
with closing(socket.socket(socket.AF_INET, socket.SOCK_STREAM)) as sock: with closing(socket.socket(socket.AF_INET, socket.SOCK_STREAM)) as sock:
sock.settimeout(2) sock.settimeout(2)
result = sock.connect_ex(('0.0.0.0', port)) result = sock.connect_ex(('127.0.0.1', port))
if result != 0: if result != 0:
return True return True
else: else:
......
python/pipeline/util.py 100644 → 100755
浏览文件 @ d66a0016
...@@ -39,7 +39,7 @@ class AvailablePortGenerator(object): ...@@ -39,7 +39,7 @@ class AvailablePortGenerator(object):
def port_is_available(port): def port_is_available(port):
with closing(socket.socket(socket.AF_INET, socket.SOCK_STREAM)) as sock: with closing(socket.socket(socket.AF_INET, socket.SOCK_STREAM)) as sock:
sock.settimeout(2) sock.settimeout(2)
result = sock.connect_ex(('0.0.0.0', port)) result = sock.connect_ex(('127.0.0.1', port))
if result != 0: if result != 0:
return True return True
else: else:
......
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