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94 lines
4.4 KiB
Markdown
94 lines
4.4 KiB
Markdown
English | [简体中文](README_CN.md)
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# PaddleClas Service Deployment Example
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Before the service deployment, please confirm
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- 1. Refer to [FastDeploy Service Deployment](../../../../../serving/README.md) for software and hardware environment requirements and image pull commands.
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## Start the Service
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```bash
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# Download the example code for deployment
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git clone https://github.com/PaddlePaddle/FastDeploy.git
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cd FastDeploy/examples/vision/classification/paddleclas/serving
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# Download ResNet50_vd model files and test images
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wget https://bj.bcebos.com/paddlehub/fastdeploy/ResNet50_vd_infer.tgz
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tar -xvf ResNet50_vd_infer.tgz
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wget https://gitee.com/paddlepaddle/PaddleClas/raw/release/2.4/deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg
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# Put the configuration file into the preprocessing directory
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mv ResNet50_vd_infer/inference_cls.yaml models/preprocess/1/inference_cls.yaml
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# Place the model under models/runtime/1 and rename them to model.pdmodel和model.pdiparams
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mv ResNet50_vd_infer/inference.pdmodel models/runtime/1/model.pdmodel
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mv ResNet50_vd_infer/inference.pdiparams models/runtime/1/model.pdiparams
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# Pull the fastdeploy image (x.y.z represent the image version. Refer to the serving document to replace them with numbers)
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# GPU image
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docker pull registry.baidubce.com/paddlepaddle/fastdeploy:x.y.z-gpu-cuda11.4-trt8.4-21.10
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# CPU image
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docker pull registry.baidubce.com/paddlepaddle/fastdeploy:x.y.z-cpu-only-21.10
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# Run the container named fd_serving and mount it in the /serving directory of the container
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nvidia-docker run -it --net=host --name fd_serving -v `pwd`/:/serving registry.baidubce.com/paddlepaddle/fastdeploy:x.y.z-gpu-cuda11.4-trt8.4-21.10 bash
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# Start the service (The CUDA_VISIBLE_DEVICES environment variable is not set, which entitles the scheduling authority of all GPU cards)
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CUDA_VISIBLE_DEVICES=0 fastdeployserver --model-repository=/serving/models --backend-config=python,shm-default-byte-size=10485760
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```
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>> **Attention**:
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>> To pull images from other hardware, refer to [Service Deployment Master Document](../../../../../serving/README.md)
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>> If "Address already in use" appears when running fastdeployserver to start the service, use `--grpc-port` to specify the port number and change the request port number in the client demo.
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>> Other startup parameters can be checked by fastdeployserver --help
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Successful service start brings the following output:
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```
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......
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I0928 04:51:15.784517 206 grpc_server.cc:4117] Started GRPCInferenceService at 0.0.0.0:8001
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I0928 04:51:15.785177 206 http_server.cc:2815] Started HTTPService at 0.0.0.0:8000
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I0928 04:51:15.826578 206 http_server.cc:167] Started Metrics Service at 0.0.0.0:8002
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```
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## Client Request
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Execute the following command in the physical machine to send the grpc request and output the result
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```
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# Download test images
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wget https://gitee.com/paddlepaddle/PaddleClas/raw/release/2.4/deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg
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# Install client dependencies
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python3 -m pip install tritonclient\[all\]
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# Send the request
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python3 paddlecls_grpc_client.py
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```
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The result is returned in json format and printed after sending the request:
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```
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output_name: CLAS_RESULT
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{'label_ids': [153], 'scores': [0.6862289905548096]}
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```
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## Configuration Change
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The current default configuration runs the TensorRT engine on GPU. If you want to run it on CPU or other inference engines, please modify the configuration in `models/runtime/config.pbtxt`. Refer to [Configuration Document](../../../../../serving/docs/EN/model_configuration-en.md) for more information.
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## Use VisualDL for serving deployment visualization
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You can use VisualDL for [serving deployment visualization](../../../../../serving/docs/EN/vdl_management-en.md) , the above model preparation, deployment, configuration modification and client request operations can all be performed based on VisualDL.
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The serving deployment of PaddleClas by VisualDL only needs the following three steps:
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```text
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1. Load the model repository: ./vision/classification/paddleclas/serving/models
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2. Download the model resource file: click the runtime model, click the version number 1 to add the pre-training model, and select the image classification model ResNet50_vd to download.
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3. Start the service: Click the "launch server" button and input the launch parameters.
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```
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<p align="center">
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<img src="https://user-images.githubusercontent.com/22424850/211708702-828d8ad8-4e85-457f-9c62-12f53fc81853.gif" width="100%"/>
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</p>
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