cherry-pick #1238

doc/PIPELINE_SERVING.md

@@ -726,6 +726,42 @@ There are two kinds of IDs in the pipeline for concatenating requests, `data_id`
 
 The log printed by the Pipeline framework will carry both data_id and log_id. After auto-batching is turned on, the first `data_id` in the batch will be used to mark the whole batch, and the framework will print all data_ids in the batch in a log.
 
+
+### 5.2 Log Rotating
+Log module of Pipeline Serving is defined in file `logger.py`.`logging.handlers.RotatingFileHandler` is used to support the rotation of disk log files. Set `maxBytes` and `backupCount` according to different file levels and daily quality. When the predetermined size is about to be exceeded, the old file will be closed and a new file will be opened for output.
+
+
+```python
+"handlers": {
+    "f_pipeline.log": {
+        "class": "logging.handlers.RotatingFileHandler",
+        "level": "INFO",
+        "formatter": "normal_fmt",
+        "filename": os.path.join(log_dir, "pipeline.log"),
+        "maxBytes": 512000000,
+        "backupCount": 20,
+    },
+    "f_pipeline.log.wf": {
+        "class": "logging.handlers.RotatingFileHandler",
+        "level": "WARNING",
+        "formatter": "normal_fmt",
+        "filename": os.path.join(log_dir, "pipeline.log.wf"),
+        "maxBytes": 512000000,
+        "backupCount": 10,
+    },
+    "f_tracer.log": {
+        "class": "logging.handlers.RotatingFileHandler",
+        "level": "INFO",
+        "formatter": "tracer_fmt",
+        "filename": os.path.join(log_dir, "pipeline.tracer"),
+        "maxBytes": 512000000,
+        "backupCount": 5,
+    },
+},
+```
+
+***
+
 ## 6.Performance analysis and optimization
 
 

doc/PIPELINE_SERVING_CN.md

@@ -705,9 +705,9 @@ Pipeline Serving支持低精度推理，CPU、GPU和TensoRT支持的精度类型
 
 ## 5.日志追踪
 Pipeline服务日志在当前目录的PipelineServingLogs目录下，有3种类型日志，分别是pipeline.log日志、pipeline.log.wf日志、pipeline.tracer日志。
-- pipeline.log日志 : 记录 debug & info日志信息
-- pipeline.log.wf日志 : 记录 warning & error日志
-- pipeline.tracer日志 : 统计各个阶段耗时、channel堆积信息
+- `pipeline.log` : 记录 debug & info日志信息
+- `pipeline.log.wf` : 记录 warning & error日志
+- `pipeline.tracer` : 统计各个阶段耗时、channel堆积信息
 
 在服务发生异常时，错误信息会记录在pipeline.log.wf日志中。打印tracer日志要求在config.yml的DAG属性中添加tracer配置。
 
@@ -718,6 +718,39 @@ Pipeline中有2种id用以串联请求，分别时data_id和log_id，二者区
 
 通常，Pipeline框架打印的日志会同时带上data_id和log_id。开启auto-batching后，会使用批量中的第一个data_id标记batch整体，同时框架会在一条日志中打印批量中所有data_id。
 
+### 5.2 日志滚动
+Pipeline的日志模块在`logger.py`中定义，使用了`logging.handlers.RotatingFileHandler`支持磁盘日志文件的轮换。根据不同文件级别和日质量分别设置了`maxBytes` 和 `backupCount`，当即将超出预定大小时，将关闭旧文件并打开一个新文件用于输出。
+
+```python
+"handlers": {
+    "f_pipeline.log": {
+        "class": "logging.handlers.RotatingFileHandler",
+        "level": "INFO",
+        "formatter": "normal_fmt",
+        "filename": os.path.join(log_dir, "pipeline.log"),
+        "maxBytes": 512000000,
+        "backupCount": 20,
+    },
+    "f_pipeline.log.wf": {
+        "class": "logging.handlers.RotatingFileHandler",
+        "level": "WARNING",
+        "formatter": "normal_fmt",
+        "filename": os.path.join(log_dir, "pipeline.log.wf"),
+        "maxBytes": 512000000,
+        "backupCount": 10,
+    },
+    "f_tracer.log": {
+        "class": "logging.handlers.RotatingFileHandler",
+        "level": "INFO",
+        "formatter": "tracer_fmt",
+        "filename": os.path.join(log_dir, "pipeline.tracer"),
+        "maxBytes": 512000000,
+        "backupCount": 5,
+    },
+},
+```
+
+***
 
 ## 6.性能分析与优化
 

python/examples/pipeline/bert/README.md

+# Imagenet Pipeline WebService
+
+This document will takes Imagenet service as an example to introduce how to use Pipeline WebService.
+
+## Get model
+```
+sh get_model.sh
+```
+
+## Start server
+
+```
+python3 web_service.py &>log.txt &
+```
+
+## RPC test
+```
+python3 pipeline_rpc_client.py
+```

python/examples/pipeline/bert/README_CN.md

+# Imagenet Pipeline WebService
+
+这里以 Imagenet 服务为例来介绍 Pipeline WebService 的使用。
+
+## 获取模型
+```
+sh get_model.sh
+```
+
+## 启动服务
+
+```
+python3 web_service.py &>log.txt &
+```
+
+## 测试
+```
+python3 pipeline_rpc_client.py
+```

python/examples/pipeline/imagenet/README.md

@@ -10,10 +10,10 @@ sh get_model.sh
 ## Start server
 
 ```
-python resnet50_web_service.py &>log.txt &
+python3 resnet50_web_service.py &>log.txt &
 ```
 
 ## RPC test
 ```
-python pipeline_rpc_client.py
+python3 pipeline_rpc_client.py
 ```

python/examples/pipeline/imagenet/README_CN.md

@@ -10,11 +10,10 @@ sh get_model.sh
 ## 启动服务
 
 ```
-python resnet50_web_service.py &>log.txt &
+python3 resnet50_web_service.py &>log.txt &
 ```
 
 ## 测试
 ```
-python pipeline_rpc_client.py
+python3 pipeline_rpc_client.py
 ```
-

python/examples/pipeline/imdb_model_ensemble/README.md

@@ -8,12 +8,12 @@ sh get_data.sh
 ## Start servers
 
 ```
-python -m paddle_serving_server.serve --model imdb_cnn_model --port 9292 &> cnn.log &
-python -m paddle_serving_server.serve --model imdb_bow_model --port 9393 &> bow.log &
-python test_pipeline_server.py &>pipeline.log &
+python3 -m paddle_serving_server.serve --model imdb_cnn_model --port 9292 &> cnn.log &
+python3 -m paddle_serving_server.serve --model imdb_bow_model --port 9393 &> bow.log &
+python3 test_pipeline_server.py &>pipeline.log &
 ```
 
 ## Start clients
 ```
-python test_pipeline_client.py
+python3 test_pipeline_client.py
 ```

python/examples/pipeline/imdb_model_ensemble/README_CN.md

@@ -8,12 +8,12 @@ sh get_data.sh
 ## 启动服务
 
 ```
-python -m paddle_serving_server.serve --model imdb_cnn_model --port 9292 &> cnn.log &
-python -m paddle_serving_server.serve --model imdb_bow_model --port 9393 &> bow.log &
-python test_pipeline_server.py &>pipeline.log &
+python3 -m paddle_serving_server.serve --model imdb_cnn_model --port 9292 &> cnn.log &
+python3 -m paddle_serving_server.serve --model imdb_bow_model --port 9393 &> bow.log &
+python3 test_pipeline_server.py &>pipeline.log &
 ```
 
 ## 启动客户端
 ```
-python test_pipeline_client.py
+python3 test_pipeline_client.py
 ```

python/examples/pipeline/ocr/README.md

@@ -4,11 +4,13 @@
 
 This document will take OCR as an example to show how to use Pipeline WebService to start multi-model tandem services.
 
+This OCR example only supports Process OP.
+
 ## Get Model
 ```
-python -m paddle_serving_app.package --get_model ocr_rec
+python3 -m paddle_serving_app.package --get_model ocr_rec
 tar -xzvf ocr_rec.tar.gz
-python -m paddle_serving_app.package --get_model ocr_det
+python3 -m paddle_serving_app.package --get_model ocr_det
 tar -xzvf ocr_det.tar.gz
 ```
 
@@ -18,14 +20,16 @@ wget --no-check-certificate https://paddle-serving.bj.bcebos.com/ocr/test_imgs.t
 tar xf test_imgs.tar
 ```
 
-## Start Service
+## Run services
+
+### 1.Start a single server and client.
 ```
-python web_service.py &>log.txt &
+python3 web_service.py &>log.txt &
 ```
 
-## Test
+Test
 ```
-python pipeline_http_client.py
+python3 pipeline_http_client.py
 ```
 
 <!--
@@ -35,11 +39,22 @@ python pipeline_http_client.py
 
 ### RPC
 ```
-python pipeline_rpc_client.py
+python3 pipeline_rpc_client.py
 ```
 
 ### HTTP
 ```
-python pipeline_http_client.py
+python3 pipeline_http_client.py
 ```
 -->
+
+
+### 2.Run benchmark
+```
+python3 web_service.py &>log.txt &
+```
+
+Test
+```
+sh benchmark.sh
+```

python/examples/pipeline/ocr/README_CN.md

@@ -3,12 +3,13 @@
 ([English](./README.md)|简体中文)
 
 本文档将以 OCR 为例，介绍如何使用 Pipeline WebService 启动多模型串联的服务。
+本示例仅支持进程OP模式。
 
 ## 获取模型
 ```
-python -m paddle_serving_app.package --get_model ocr_rec
+python3 -m paddle_serving_app.package --get_model ocr_rec
 tar -xzvf ocr_rec.tar.gz
-python -m paddle_serving_app.package --get_model ocr_det
+python3 -m paddle_serving_app.package --get_model ocr_det
 tar -xzvf ocr_det.tar.gz
 ```
 
@@ -19,13 +20,15 @@ tar xf test_imgs.tar
 ```
 
 ## 启动 WebService
+
+### 1.启动单server、单client
 ```
-python web_service.py &>log.txt &
+python3 web_service.py &>log.txt &
 ```
 
 ## 测试
 ```
-python pipeline_http_client.py
+python3 pipeline_http_client.py
 ```
 
 <!--
@@ -36,12 +39,22 @@ python pipeline_http_client.py
 ### RPC
 
 ```
-python pipeline_rpc_client.py
+python3 pipeline_rpc_client.py
 ```
 
 ### HTTP
 
 ```
-python pipeline_http_client.py
+python3 pipeline_http_client.py
 ```
 -->
+
+### 2.启动 benchmark
+```
+python3 web_service.py &>log.txt &
+```
+
+Test
+```
+sh benchmark.sh
+```

python/pipeline/logger.py

@@ -42,22 +42,28 @@ logger_config = {
     },
     "handlers": {
         "f_pipeline.log": {
-            "class": "logging.FileHandler",
+            "class": "logging.handlers.RotatingFileHandler",
             "level": "INFO",
             "formatter": "normal_fmt",
             "filename": os.path.join(log_dir, "pipeline.log"),
+            "maxBytes": 512000000,
+            "backupCount": 20,
         },
         "f_pipeline.log.wf": {
-            "class": "logging.FileHandler",
+            "class": "logging.handlers.RotatingFileHandler",
             "level": "WARNING",
             "formatter": "normal_fmt",
             "filename": os.path.join(log_dir, "pipeline.log.wf"),
+            "maxBytes": 512000000,
+            "backupCount": 10,
         },
         "f_tracer.log": {
-            "class": "logging.FileHandler",
+            "class": "logging.handlers.RotatingFileHandler",
             "level": "INFO",
             "formatter": "tracer_fmt",
             "filename": os.path.join(log_dir, "pipeline.tracer"),
+            "maxBytes": 512000000,
+            "backupCount": 5,
         },
     },
     "loggers": {