@@ -101,20 +101,20 @@ For Paddle Serving developers, we provide extended documents such as custom OP,
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@@ -101,20 +101,20 @@ For Paddle Serving developers, we provide extended documents such as custom OP,
Paddle Serving works closely with the Paddle model suite, and implements a large number of service deployment examples, including image classification, object detection, language and text recognition, Chinese part of speech, sentiment analysis, content recommendation and other types of examples, for a total of 42 models.
Paddle Serving works closely with the Paddle model suite, and implements a large number of service deployment examples, including image classification, object detection, language and text recognition, Chinese part of speech, sentiment analysis, content recommendation and other types of examples, for a total of 42 models.
@@ -134,11 +143,12 @@ If you want to communicate with developers and other users? Welcome to join us,
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@@ -134,11 +143,12 @@ If you want to communicate with developers and other users? Welcome to join us,
> Contribution
> Contribution
If you want to contribute code to Paddle Serving, please reference [Contribution Guidelines](doc/Contribute_EN.md)
If you want to contribute code to Paddle Serving, please reference [Contribution Guidelines](doc/Contribute_EN.md)
- Thanks to [@loveululu](https://github.com/loveululu) for providing python API of Cube.
- Special Thanks to [@BeyondYourself](https://github.com/BeyondYourself) in complementing the gRPC tutorial, updating the FAQ doc and modifying the mdkir command
- Thanks to [@EtachGu](https://github.com/EtachGu) in updating run docker codes.
- Special Thanks to [@mcl-stone](https://github.com/mcl-stone) in updating faster_rcnn benchmark
- Thanks to [@BeyondYourself](https://github.com/BeyondYourself) in complementing the gRPC tutorial, updating the FAQ doc and modifying the mdkir command
- Special Thanks to [@cg82616424](https://github.com/cg82616424) in updating the unet benchmark and modifying resize comment error
- Thanks to [@mcl-stone](https://github.com/mcl-stone) in updating faster_rcnn benchmark
- Special Thanks to [@cuicheng01](https://github.com/cuicheng01) for providing 11 PaddleClas models
- Thanks to [@cg82616424](https://github.com/cg82616424) in updating the unet benchmark modifying resize comment error
- Thanks to [@cuicheng01](https://github.com/cuicheng01) for providing 11 PaddleClas models
In order to better optimize the performance, PipelineServing provides a timeline tool to monitor the time of each stage of the whole service.
### 1.2 Output profile information on server side
The server is controlled by the `use_profile` field in yaml:
```yaml
dag:
use_profile:true
```
After the function is enabled, the server will print the corresponding log information to the standard output in the process of prediction. In order to show the time consumption of each stage more intuitively, Analyst module is provided for further analysis and processing of log files.
The output of the server is first saved to a file. Taking `profile.txt` as an example, the script converts the time monitoring information in the log into JSON format and saves it to the `trace` file. The `trace` file can be visualized through the tracing function of Chrome browser.
```shell
from paddle_serving_server.pipeline import Analyst
import json
import sys
if __name__ =="__main__":
log_filename ="profile.txt"
trace_filename ="trace"
analyst = Analyst(log_filename)
analyst.save_trace(trace_filename)
```
Specific operation: open Chrome browser, input in the address bar `chrome://tracing/` , jump to the tracing page, click the load button, open the saved `trace` file, and then visualize the time information of each stage of the prediction service.
### 1.3 Output profile information on client side
The profile function can be enabled by setting `profile=True` in the `predict` interface on the client side.
After the function is enabled, the client will print the log information corresponding to the prediction to the standard output during the prediction process, and the subsequent analysis and processing are the same as that of the server.
### 1.4 Analytical methods
According to the time consumption of each stage in the pipeline.tracer log, the following formula is used to gradually analyze which stage is the main time consumption.
