first init

docs/hardware/install_other_devices.md

+# 多硬件安装飞桨
+本文档主要针对昇腾 NPU 硬件平台，介绍如何安装飞桨。
+## 1. 昇腾 NPU 飞桨安装
+### 1.1 环境准备
+当前 PaddleYOLO 支持昇腾 910B 芯片，昇腾驱动版本为 23.0.3。考虑到环境差异性，我们推荐使用飞桨官方提供的标准镜像完成环境准备。
+- 1. 拉取镜像，此镜像仅为开发环境，镜像中不包含预编译的飞桨安装包，镜像中已经默认安装了昇腾算子库 CANN-8.0.RC1。
+
+```
+# 适用于 X86 架构，暂时不提供 Arch64 架构镜像
+docker pull registry.baidubce.com/device/paddle-npu:cann80RC1-ubuntu20-x86_64-gcc84-py39
+```
+
+- 2. 参考如下命令启动容器，ASCEND_RT_VISIBLE_DEVICES 指定可见的 NPU 卡号
+```
+docker run -it --name paddle-npu-dev -v $(pwd):/work \
+    --privileged --network=host --shm-size=128G -w=/work \
+    -v /usr/local/Ascend/driver:/usr/local/Ascend/driver \
+    -v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \
+    -v /usr/local/dcmi:/usr/local/dcmi \
+    -e ASCEND_RT_VISIBLE_DEVICES="0,1,2,3,4,5,6,7" \
+    registry.baidubce.com/device/paddle-npu:cann80RC1-ubuntu20-x86_64-gcc84-py39 /bin/bash
+```
+### 1.2 安装 paddle 包
+当前提供 Python3.9 的 wheel 安装包。如有其他 Python 版本需求，可以参考[飞桨官方文档](https://www.paddlepaddle.org.cn/install/quick)自行编译安装。
+
+- 1. 下载安装 Python3.9 的 wheel 安装包
+
+```
+# 注意需要先安装飞桨 cpu 版本
+pip install https://paddle-model-ecology.bj.bcebos.com/paddlex/whl/paddle-device/npu/paddlepaddle-0.0.0-cp39-cp39-linux_x86_64.whl
+pip install https://paddle-model-ecology.bj.bcebos.com/paddlex/whl/paddle-device/npu/paddle_custom_npu-0.0.0-cp39-cp39-linux_x86_64.whl
+```
+- 2. 验证安装包
+安装完成之后，运行如下命令。
+```
+python -c "import paddle; paddle.utils.run_check()"
+```
+预期得到如下输出结果
+```
+Running verify PaddlePaddle program ...
+PaddlePaddle works well on 1 npu.
+PaddlePaddle works well on 8 npus.
+PaddlePaddle is installed successfully! Let's start deep learning with PaddlePaddle now.
+```
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docs/hardware/supported_models.md

+# PaddleYOLO模型列表
+
+*多硬件安装方式请参考[多硬件安装文档](install_other_devices.md)*
+
+| 模型名称 | 昇腾NPU |
+| ---------------- | -------- |
+| YOLOv5_small | √ |
+| YOLOv5_medium | √ |
+| YOLOv5_large | √ |
+| YOLOv5_extra_large | √ |
+| YOLOv5u_small | √ |
+| YOLOv5u_medium | √ |
+| YOLOv5u_large | √ |
+| YOLOv5u_extra_large | √ |
+| YOLOv8_small | √ |
+| YOLOv8_medium | √ |
+| YOLOv8_large | √ |
+| YOLOv8_extra_large | √ |
+| PP-YOLOE seg | √ |
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