[简体中文](high_performance_deploy.md) | English

# PaddleX High-Performance Deployment Guide

In real-world production environments, many applications have stringent standards for deployment strategy performance metrics, particularly response speed, to ensure efficient system operation and smooth user experience. To this end, PaddleX provides high-performance inference plugins designed to deeply optimize model inference and pre/post-processing, achieving significant speedups in the end-to-end process. This document will first introduce the installation and usage of the high-performance inference plugins, followed by a list of pipelines and models currently supporting the use of these plugins.

## 1. Installation and Usage of High-Performance Inference Plugins

Before using the high-performance inference plugins, ensure you have completed the installation of PaddleX according to the [PaddleX Local Installation Tutorial](../installation/installation_en.md), and have successfully run the basic inference of the pipeline using either the PaddleX pipeline command line instructions or the Python script instructions.

### 1.1 Installing High-Performance Inference Plugins

Find the corresponding installation command based on your processor architecture, operating system, device type, and Python version in the table below and execute it in your deployment environment:

<table>
  <tr>
    <th>Processor Architecture</th>
    <th>Operating System</th>
    <th>Device Type</th>
    <th>Python Version</th>
    <th>Installation Command</th>
  </tr>
  <tr>
    <td rowspan="7">x86-64</td>
    <td rowspan="7">Linux</td>
    <td rowspan="4">CPU</td>
  </tr>
  <tr>
    <td>3.8</td>
    <td>curl -s https://paddle-model-ecology.bj.bcebos.com/paddlex/PaddleX3.0/deploy/paddlex_hpi/install_script/latest/install_paddlex_hpi.py | python3 - --arch x86_64 --os linux --device cpu --py 38</td>
  </tr>
  <tr>
    <td>3.9</td>
    <td>curl -s https://paddle-model-ecology.bj.bcebos.com/paddlex/PaddleX3.0/deploy/paddlex_hpi/install_script/latest/install_paddlex_hpi.py | python3 - --arch x86_64 --os linux --device cpu --py 39</td>
  </tr>
  <tr>
    <td>3.10</td>
    <td>curl -s https://paddle-model-ecology.bj.bcebos.com/paddlex/PaddleX3.0/deploy/paddlex_hpi/install_script/latest/install_paddlex_hpi.py | python3 - --arch x86_64 --os linux --device cpu --py 310</td>
  </tr>
  <tr>
    <td rowspan="3">GPU</td>
    <td>3.8</td>
    <td>curl -s https://paddle-model-ecology.bj.bcebos.com/paddlex/PaddleX3.0/deploy/paddlex_hpi/install_script/latest/install_paddlex_hpi.py | python3 - --arch x86_64 --os linux --device gpu_cuda118_cudnn86 --py 38</td>
  </tr>
  <tr>
    <td>3.9</td>
    <td>curl -s https://paddle-model-ecology.bj.bcebos.com/paddlex/PaddleX3.0/deploy/paddlex_hpi/install_script/latest/install_paddlex_hpi.py | python3 - --arch x86_64 --os linux --device gpu_cuda118_cudnn86 --py 39</td>
  </tr>
  <tr>
    <td>3.10</td>
    <td>curl -s https://paddle-model-ecology.bj.bcebos.com/paddlex/PaddleX3.0/deploy/paddlex_hpi/install_script/latest/install_paddlex_hpi.py | python3 - --arch x86_64 --os linux --device gpu_cuda118_cudnn86 --py 310</td>
  </tr>
</table>

### 1.2 Obtaining Serial Numbers and Activation

On the [Baidu AIStudio Community - AI Learning and Training Platform](https://aistudio.baidu.com/paddlex/commercialization) page, under the "Open-source Pipeline Deployment Serial Number Inquiry and Acquisition" section, select "Acquire Now" as shown in the following image:

![](https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/main/images/pipeline_deploy/image-1.png)

Select the pipeline you wish to deploy and click "Acquire". Afterwards, you can find the acquired serial number in the "Open-source Pipeline Deployment SDK Serial Number Management" section at the bottom of the page:

![](https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/main/images/pipeline_deploy/image-2.png)

After using the serial number to complete activation, you can utilize high-performance inference plugins. PaddleX provides both online and offline activation methods (both only support Linux systems):

* Online Activation: When using the inference API or CLI, specify the serial number and enable online activation to automatically complete the process.
* Offline Activation: Follow the instructions in the serial number management interface (click "Offline Activation" under "Operations") to obtain the device fingerprint of your machine. Bind the serial number with the device fingerprint to obtain a certificate and complete the activation. For this activation method, you need to manually store the certificate in the `${HOME}/.baidu/paddlex/licenses` directory on the machine (create the directory if it does not exist) and specify the serial number when using the inference API or CLI.

Please note: Each serial number can only be bound to a unique device fingerprint and can only be bound once. This means that if users deploy models on different machines, they must prepare separate serial numbers for each machine.

### 1.3 Enabling High-Performance Inference Plugins

Before enabling high-performance plugins, please ensure that the `LD_LIBRARY_PATH` of the current environment does not specify the TensorRT directory, as the plugins already integrate TensorRT to avoid conflicts caused by different TensorRT versions that may prevent the plugins from functioning properly.

For PaddleX CLI, specify `--use_hpip` and set the serial number to enable the high-performance inference plugin. If you wish to activate the license online, specify `--update_license` when using the serial number for the first time. Taking the general image classification pipeline as an example:

```diff
paddlex \
    --pipeline image_classification \
    --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_image_classification_001.jpg \
    --device gpu:0 \
+   --use_hpip \
+   --serial_number {serial_number}

# If you wish to activate the license online
paddlex \
    --pipeline image_classification \
    --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_image_classification_001.jpg \
    --device gpu:0 \
+   --use_hpip \
+   --serial_number {serial_number} \
+   --update_license
```

For PaddleX Python API, enabling the high-performance inference plugin is similar. Still taking the general image classification pipeline as an example:

```diff
from paddlex import create_pipeline

pipeline = create_pipeline(
    pipeline="image_classification",
+   use_hpip=True,
+   serial_number="{serial_number}",
)
 
output = pipeline.predict("https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_image_classification_001.jpg")
```

The inference results obtained with the high-performance inference plugin enabled are consistent with those without the plugin enabled. For some models, enabling the high-performance inference plugin for the first time may take a longer time to complete the construction of the inference engine. PaddleX will cache the relevant information in the model directory after the first construction of the inference engine and reuse the cached content in subsequent runs to improve initialization speed.

### 1.4 Modifying High-Performance Inference Configurations

PaddleX provides default high-performance inference configurations for each model and stores them in the model's configuration file. Due to the diversity of actual deployment environments, using the default configurations may not achieve ideal performance in specific environments or may even result in inference failures. For situations where the default configurations cannot meet requirements, you can try changing the model's inference backend as follows:

1. Locate the `inference.yml` file in the model directory and find the Hpi field.

2. Modify the value of `selected_backends`. Specifically, `selected_backends` may be set as follows:

    ```yaml
    selected_backends:
        cpu: paddle_infer
        gpu: onnx_runtime
    ```

    Each entry is formatted as `{device_type}: {inference_backend_name}`. The default selects the backend with the shortest inference time in the official test environment. `supported_backends` lists the inference backends supported by the model in the official test environment for reference.

    The currently available inference backends are:

    * `paddle_infer`: The standard Paddle Inference engine. Supports CPU and GPU.
    * `paddle_tensorrt`: [Paddle-TensorRT](https://www.paddlepaddle.org.cn/lite/v2.10/optimize/paddle_trt.html), a high-performance deep learning inference library produced by Paddle, which integrates TensorRT in the form of subgraphs for further optimization and acceleration. Supports GPU only.
    * `openvino`: [OpenVINO](https://github.com/openvinotoolkit/openvino), a deep learning inference tool provided by Intel, optimized for model inference performance on various Intel hardware. Supports CPU only.
    * `onnx_runtime`: [ONNX Runtime](https://onnxruntime.ai/), a cross-platform, high-performance inference engine. Supports CPU and GPU.
    * `tensorrt`: [TensorRT](https://developer.nvidia.com/tensorrt), a high-performance deep learning inference library provided by NVIDIA, optimized for NVIDIA GPUs to improve speed. Supports GPU only.

    Here are some key details of the current official test environment:

    * CPU: Intel Xeon Gold 5117
    * GPU: NVIDIA Tesla T4
    * CUDA Version: 11.8
    * cuDNN Version: 8.6
    * Docker

## 2. Pipelines and Models Supporting High-Performance Inference Plugins

<table>
  <tr>
    <th>Pipeline</th>
    <th>Pipeline Module</th>
    <th>Specific Models</th>
  </tr>
  <tr>
    <td>General Image Classification</td>
    <td>Image Classification</td>
    <td>ResNet18<br/>ResNet34<details>
    <summary><b>more</b></summary>ResNet50<br/>ResNet101<br/>ResNet152<br/>ResNet18_vd<br/>ResNet34_vd<br/>ResNet50_vd<br/>ResNet101_vd<br/>ResNet152_vd<br/>ResNet200_vd<br/>PP-LCNet_x0_25<br/>PP-LCNet_x0_35<br/>PP-LCNet_x0_5<br/>PP-LCNet_x0_75<br/>PP-LCNet_x1_0<br/>PP-LCNet_x1_5<br/>PP-LCNet_x2_0<br/>PP-LCNet_x2_5<br/>PP-LCNetV2_small<br/>PP-LCNetV2_base<br/>PP-LCNetV2_large<br/>MobileNetV3_large_x0_35<br/>MobileNetV3_large_x0_5<br/>MobileNetV3_large_x0_75<br/>MobileNetV3_large_x1_0<br/>MobileNetV3_large_x1_25<br/>MobileNetV3_small_x0_35<br/>MobileNetV3_small_x0_5<br/>MobileNetV3_small_x0_75<br/>MobileNetV3_small_x1_0<br/>MobileNetV3_small_x1_25<br/>ConvNeXt_tiny<br/>ConvNeXt_small<br/>ConvNeXt_base_224<br/>ConvNeXt_base_384<br/>ConvNeXt_large_224<br/>ConvNeXt_large_384<br/>MobileNetV1_x0_25<br/>MobileNetV1_x0_5<br/>MobileNetV1_x0_75<br/>MobileNetV1_x1_0<br/>MobileNetV2_x0_25<br/>MobileNetV2_x0_5<br/>MobileNetV2_x1_0<br/>MobileNetV2_x1_5<br/>MobileNetV2_x2_0<br/>SwinTransformer_tiny_patch4_window7_224<br/>SwinTransformer_small_patch4_window7_224<br/>SwinTransformer_base_patch4_window7_224<br/>SwinTransformer_base_patch4_window12_384<br/>SwinTransformer_large_patch4_window7_224<br/>SwinTransformer_large_patch4_window12_384<br/>PP-HGNet_small<br/>PP-HGNet_tiny<br/>PP-HGNet_base<br/>PP-HGNetV2-B0<br/>PP-HGNetV2-B1<br/>PP-HGNetV2-B2<br/>PP-HGNetV2-B3<br/>PP-HGNetV2-B4<br/>PP-HGNetV2-B5<br/>PP-HGNetV2-B6<br/>CLIP_vit_base_patch16_224<br/>CLIP_vit_large_patch14_224</details></td>
  </tr>

  <tr>
    <td>General Object Detection</td>
    <td>Object Detection</td>
    <td>PP-YOLOE_plus-S<br/>PP-YOLOE_plus-M<details>
        <summary><b>more</b></summary>PP-YOLOE_plus-L<br/>PP-YOLOE_plus-X<br/>YOLOX-N<br/>YOLOX-T<br/>YOLOX-S<br/>YOLOX-M<br/>YOLOX-L<br/>YOLOX-X<br/>YOLOv3-DarkNet53<br/>YOLOv3-ResNet50_vd_DCN<br/>YOLOv3-MobileNetV3<br/>RT-DETR-R18<br/>RT-DETR-R50<br/>RT-DETR-L<br/>RT-DETR-H<br/>RT-DETR-X<br/>PicoDet-S<br/>PicoDet-L</details></td>
  </tr>

  <tr>
    <td>General Semantic Segmentation</td>
    <td>Semantic Segmentation</td>
    <td>Deeplabv3-R50<br/>Deeplabv3-R101<details>
    <summary><b>more</b></summary>Deeplabv3_Plus-R50<br/>Deeplabv3_Plus-R101<br/>PP-LiteSeg-T<br/>OCRNet_HRNet-W48<br/>OCRNet_HRNet-W18<br/>SeaFormer_tiny<br/>SeaFormer_small<br/>SeaFormer_base<br/>SeaFormer_large<br/>SegFormer-B0<br/>SegFormer-B1<br/>SegFormer-B2<br/>SegFormer-B3<br/>SegFormer-B4<br/>SegFormer-B5</details></td>
  </tr>

  <tr>
    <td>General Instance Segmentation</td>
    <td>Instance Segmentation</td>
    <td>Mask-RT-DETR-L<br/>Mask-RT-DETR-H</td>
  </tr>

  <tr>
    <td rowspan="2">General OCR</td>
    <td>Text Detection</td>
    <td>PP-OCRv4_server_det<br/>PP-OCRv4_mobile_det</td>
  </tr>

  <tr>
    <td>Text Recognition</td>
    <td>PP-OCRv4_server_rec<br/>PP-OCRv4_mobile_rec<br/>LaTeX_OCR_rec<br/>ch_RepSVTR_rec<br/>ch_SVTRv2_rec</td>
  </tr>

  <tr>
    <td rowspan="5">General Table Recognition</td>
    <td>Layout Detection</td>
    <td>PicoDet_layout_1x</td>
  </tr>

  <tr>
    <td rowspan="2">Table Recognition</td>
    <td>SLANet</td>
  </tr>

  <tr>
    <td>SLANet_plus</td>
  </tr>

  <tr>
    <td>Text Detection</td>
    <td>PP-OCRv4_server_det<br/>PP-OCRv4_mobile_det</td>
  </tr>

  <tr>
    <td>Text Recognition</td>
    <td>PP-OCRv4_server_rec<br/>PP-OCRv4_mobile_rec</td>
  </tr>

  <tr>
    <td rowspan="15">Document Scene Information Extraction v3 Pipeline</td>
    <td rowspan="2">Table Recognition</td>
    <td>SLANet</td>
  </tr>

  <tr>
    <td>SLANet_plus</td>
  </tr>

  <tr>
    <td>Layout Detection</td>
    <td>PicoDet_layout_1x</td>
  </tr>

  <tr>
    <td rowspan="2">Text Detection</td>
    <td>PP-OCRv4_server_det</td>
  </tr>

  <tr>
    <td>PP-OCRv4_mobile_det</td>
  </tr>

  <tr>
    <td rowspan="4">Text Recognition</td>
    <td>PP-OCRv4_server_rec</td>
  </tr>

  <tr>
    <td>PP-OCRv4_mobile_rec</td>
  </tr>

  <tr>
    <td>ch_RepSVTR_rec</td>
  </tr>

  <tr>
    <td>ch_SVTRv2_rec</td>
  </tr>

  <tr>
    <td rowspan="2">Seal Text Detection</td>
    <td>PP-OCRv4_server_seal_det</td>
  </tr>

  <tr>
    <td>PP-OCRv4_mobile_seal_det</td>
  </tr>

  <tr>
    <td>Text Image Rectification</td>
    <td>UVDoc</td>
  </tr>

  <tr>
    <td>Document Image Orientation Classification</td>
    <td>PP-LCNet_x1_0_doc_ori</td>
  </tr>

</table>