提交 d01ebf0e 编写于 作者: D Dong Daxiang 提交者: GitHub

Update README.md

上级 addc9376
...@@ -67,7 +67,7 @@ Chinese word segmentation HTTP service that can be deployed with one line comman ...@@ -67,7 +67,7 @@ Chinese word segmentation HTTP service that can be deployed with one line comman
- **Demo**: - **Demo**:
``` shell ``` shell
> python -m paddle_serving_app.package -get lac > python -m paddle_serving_app.package -get_model lac
> tar -xzf lac.tar.gz > tar -xzf lac.tar.gz
> python lac_web_service.py 9292 & > python lac_web_service.py 9292 &
> curl -H "Content-Type:application/json" -X POST -d '{"feed":[{"words": "我爱北京天安门"}], "fetch":["word_seg"]}' http://127.0.0.1:9393/lac/prediction > curl -H "Content-Type:application/json" -X POST -d '{"feed":[{"words": "我爱北京天安门"}], "fetch":["word_seg"]}' http://127.0.0.1:9393/lac/prediction
...@@ -82,33 +82,23 @@ Image classification trained with Imagenet dataset. A label and corresponding pr ...@@ -82,33 +82,23 @@ Image classification trained with Imagenet dataset. A label and corresponding pr
Note: This demo needs paddle-serving-server-gpu. Note: This demo needs paddle-serving-server-gpu.
``` ```
- **Download Servable Package**: - **Demo**:
``` shell
wget --no-check-certificate https://paddle-serving.bj.bcebos.com/imagenet-example/imagenet_demo.tar.gz
```
- **Host web service**:
``` shell
tar -xzf imagenet_demo.tar.gz
python image_classification_service_demo.py resnet50_serving_model
```
- **Request sample**:
<p align="center"> <p align="center">
<br> <br>
<img src='https://paddle-serving.bj.bcebos.com/imagenet-example/daisy.jpg' width = "200" height = "200"> <img src='https://paddle-serving.bj.bcebos.com/imagenet-example/daisy.jpg' width = "200" height = "200">
<br> <br>
<p> <p>
``` shell
curl -H "Content-Type:application/json" -X POST -d '{"feed":[{"url": "https://paddle-serving.bj.bcebos.com/imagenet-example/daisy.jpg"}], "fetch": ["score"]}' http://127.0.0.1:9292/image/prediction
```
- **Request result**:
``` shell ``` shell
{"label":"daisy","prob":0.9341403245925903} > python -m paddle_serving_app.package -get_model resnet_v2_50_imagenet
> tar -xzf resnet_v2_50_imagenet.tar.gz
> python resnet50_imagenet_classify.py resnet50_serving_model &
> curl -H "Content-Type:application/json" -X POST -d '{"feed":[{"image": "https://paddle-serving.bj.bcebos.com/imagenet-example/daisy.jpg"}], "fetch": ["score"]}' http://127.0.0.1:9292/image/prediction
{"result":{"label":["daisy"],"prob":[0.9341403245925903]}}
``` ```
<h2 align="center">Some Key Features</h2> <h2 align="center">Some Key Features</h2>
- Integrate with Paddle training pipeline seamlessly, most paddle models can be deployed **with one line command**. - Integrate with Paddle training pipeline seamlessly, most paddle models can be deployed **with one line command**.
......
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