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@@ -38,17 +38,17 @@ Image Building Steps:
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## 1.1 Build the Requirement Collection Image (Optional)
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-Run follow script in the `server_env` directory to build the requirement collection image.
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+Navigate to the `server_env` directory and run follow script for building the requirement collection image in this directory.
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```bash
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./scripts/prepare_rc_image.sh
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```
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-This script builds a requirement collection image for each device type. The image includes Python 3.10 and [pip-tools](https://github.com/jazzband/pip-tools). [1.2 Freeze Requirement (Optional)](./README_en.md#12-freeze-requirement-optional) will be based on this image. After the build is complete, two images: `paddlex-hps-rc:gpu` and `paddlex-hps-rc:cpu` will be generated. If you encounter network issues, you can specify other pip sources through the `-p` parameter. If not specified, the default source https://pypi.org/simple will be used.
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+This script builds a requirement collection image for each device type. The image includes Python 3.10 and [pip-tools](https://github.com/jazzband/pip-tools). [1.2 Freeze Requirement (Optional)](./README_en.md#12-freeze-requirement-optional) will be based on this image. After the build is complete, two images: `paddlex-hps-rc:gpu` and `paddlex-hps-rc:cpu` will be generated. If you encounter network issues, you can specify other pip sources through the `-p` parameter. If not specified, the default source https://pypi.org/simple will be used.If you encounter issues pulling the base image during the build process, please refer to the relevant solutions in the [FAQ](./README_en.md#3-faq).
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## 1.2 Freeze Requirement (Optional)
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-To enhance the reproducibility of the build, this step freeze requirement to exact versions, run the following script:
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+To enhance the reproducibility of the build, this step freeze requirement to exact versions. Please switch to the `server_env` directory and run the following script:
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```bash
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./scripts/freeze_requirements.sh
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@@ -58,7 +58,34 @@ This script uses `pip-tools compile` to parse the source requirement files and g
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## 1.3 Building Image
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-If you need to build the GPU image, make sure to place the following installation packages in the `server_env` directory in advance:[cuDNN 8.9.7-CUDA 11.x Tar](https://developer.nvidia.cn/rdp/cudnn-archive) and [TensorRT 8.6-Linux x86_64 GA Tar Package](https://developer.nvidia.com/nvidia-tensorrt-8x-download). For Triton Inference Server, a precompiled version will be automatically downloaded during the build process, so manual download is not required. To build a GPU image, run the following command:
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+If you need to build the GPU image, make sure to place the following two installation packages in the `server_env` directory in advance:
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+
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+<table>
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+ <thead>
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+ <tr>
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+ <th>Package Name</th>
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+ <th>Version</th>
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+ <th>Download URL</th>
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+ <th>File Name</th>
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+ </tr>
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+ </thead>
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+ <tbody>
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+ <tr>
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+ <td><strong>cuDNN</strong></td>
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+ <td>v8.9.7-CUDA 11.x</td>
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+ <td><a href="https://developer.nvidia.cn/rdp/cudnn-archive">NVIDIA cuDNN Archive</a></td>
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+ <td>Local Installer for Linux x86_64 (Tar)</td>
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+ </tr>
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+ <tr>
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+ <td><strong>TensorRT</strong></td>
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+ <td>8.6-GA</td>
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+ <td><a href="https://developer.nvidia.com/nvidia-tensorrt-8x-download">TensorRT 8.x Download Page</a></td>
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+ <td>TensorRT 8.6 GA for Linux x86_64 and CUDA 11.x TAR Package</td>
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+ </tr>
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+ </tbody>
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+</table>
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+
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+For Triton Inference Server, a precompiled version will be automatically downloaded during the build process, so manual download is not required. To build a GPU image, run the following command:
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```bash
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./scripts/build_deployment_image.sh -k gpu -t latest-gpu
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@@ -89,6 +116,8 @@ Build image script supports the following configuration options:
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</tbody>
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</table>
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+If the basic image cannot be pulled, please refer to the solutions in the [FAQ](./README_en.md#3-faq).
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+
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After run successfully, the command line will display the following message:
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```text
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@@ -106,12 +135,12 @@ To build both GPU and CPU images run the following command:
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## 2. Pipeline Material Packaging
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-This stage mainly introduces the unified packaging function provided by the `sdk` directory for multiple pipelines. Meanwhile, this directory provides corresponding client and server code implementations for each pipeline:
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+This stage mainly introduces how to package pipeline materials. This function is provided in the `sdk` directory, which offers corresponding client and server code implementations for each pipeline:
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-`client`: Responsible for invoking the model services.
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-`server`: Deployed using the images built in [1. Image Building](./README_en.md#1-image-building), serving as the runtime environment for model services.
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+- `client`: Responsible for invoking the model services.
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+- `server`: Deployed using the images built in [1. Image Building](./README_en.md#1-image-building), serving as the runtime environment for model services.
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-Packaging can be performed using the `scripts/assemble.sh` script. For example, to package the general OCR pipeline, run:
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+Before packaging the pipeline materials, you need to switch to the `sdk` directory and run the `scripts/assemble.sh` script in this directory for packaging. For example, to package the general OCR pipeline, run:
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```bash
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./scripts/assemble.sh OCR
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Poki Bai