528 lines
16 KiB
Markdown
528 lines
16 KiB
Markdown
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- [Docker 一键安装脚本](#docker-一键安装脚本)
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- [支持平台](#支持平台)
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- [安装](#安装)
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- [测试](#测试)
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- [BPU 模型性能测试工具](#bpu-模型性能测试工具)
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- [工具说明](#工具说明)
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- [目录结构](#目录结构)
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- [RDK S100 系列使用方法（Nash-e / Nash-m）](#rdk-s100-系列使用方法nash-e--nash-m)
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- [RDK X5 系列使用方法（Bayes-e）](#rdk-x5-系列使用方法bayes-e)
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- [交互式进入容器](#交互式进入容器)
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- [task.json 协议说明](#taskjson-协议说明)
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- [输出结果说明](#输出结果说明)
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- [CoreMark-PRO CPU 性能测试工具](#coremark-pro-cpu-性能测试工具)
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- [工具说明](#工具说明-1)
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- [目录结构](#目录结构-1)
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- [使用方法](#使用方法)
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- [输出结果说明](#输出结果说明-1)
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---
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## Docker 一键安装脚本
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### 支持平台
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| 硬件平台 | BPU 架构 | RDK OS 版本要求 | MiniBoot 版本要求 |
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|----------|----------|-----------------|-------------------|
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| RDK S100 / RDK S100P | Nash-e / Nash-m | >= 4.0.4-Beta | >= 4.0.4-20251015222314 |
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| RDK X5 / RDK X5 Module | Bayes-e | >= 3.3.3 | >= Sep-03-2025 |
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### 安装
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脚本路径 `RDK_Docker_Tools/install_docker/install_docker.sh`，该脚本会在 RDK 板端一键安装 Docker 软件，配置好网络相关配置。
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1. 安装到使用过程无需重启。
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2. 安装过程中 eth0 网络配置不会做任何修改，ssh 不会挂掉。
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3. 请使用 root 用户安装。
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4. 请确保板卡能正常访问互联网，`/` 目录有 2GB 可用空间。
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```bash
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bash install_docker/install_docker.sh
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```
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预期输出
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```text
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============================================================
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Docker 一键安装脚本 (Ubuntu 22.04 ARM64/x86_64)
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============================================================
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[INFO] 检查系统架构...
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[OK] 架构: aarch64 (ARM64) ✓
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[INFO] 检查运行权限...
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[OK] 当前为 root 用户 ✓
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[INFO] 检查 eth0 网络接口...
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[OK] eth0 状态: UP, IP: 10.112.10.98/24
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[INFO] 注意: 本脚本不会修改 eth0 的任何配置
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[INFO] 检查 Docker 是否已安装...
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[INFO] 检查依赖命令...
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...
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[OK] Docker 安装完毕 ✓
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[INFO] 配置 Docker 镜像加速源...
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[OK] 镜像加速配置完毕 ✓
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[INFO] 启动 Docker 服务...
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[OK] Docker 服务已通过 systemd 启动 ✓
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...
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============================================================
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[OK] Docker 安装完成!
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============================================================
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```
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### 测试
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```bash
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bash install_docker/test_docker.sh
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```
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预期输出
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```text
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============================================================
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Docker 功能测试脚本
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============================================================
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[INFO] 检查 Docker 服务状态...
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[OK] Docker 服务正常 ✓
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------------------------------------------------------------
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[INFO] [1/5] 测试 docker pull...
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------------------------------------------------------------
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[OK] docker pull 成功 ✓
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------------------------------------------------------------
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[INFO] [2/5] 测试 docker run...
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------------------------------------------------------------
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[OK] docker run 成功 ✓
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------------------------------------------------------------
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[INFO] [3/5] 测试 docker commit...
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------------------------------------------------------------
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[OK] docker commit 成功 -> hello-world-committed:test ✓
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------------------------------------------------------------
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[INFO] [4/5] 测试 docker export...
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------------------------------------------------------------
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[OK] docker export 成功 ✓
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------------------------------------------------------------
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[INFO] [5/5] 测试 docker save & load...
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------------------------------------------------------------
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[OK] docker save 成功 ✓
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[OK] docker load 成功, 镜像已恢复 ✓
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============================================================
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[OK] 全部测试通过!
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============================================================
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```
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---
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## BPU 模型性能测试工具
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### 工具说明
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基于 `hrt_model_exec perf` 命令，对指定的 `.hbm` 或 `.bin` 模型文件，分别在线程数 1、2、3、4 下进行性能测试，输出每个线程数对应的平均延迟和 FPS，并将结果保存为 JSON 文件。
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| 硬件平台 | BPU 架构 | 镜像名称 |
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|----------|----------|----------|
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| RDK S100 / RDK S100P | Nash-e / Nash-m | `hrt_perf_nashem:v3.7.3` |
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| RDK X5 / RDK X5 Module | Bayes-e | `hrt_perf_bayese:v1.24.5` |
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### 目录结构
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```
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bpu_model_perf_images/
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├── Dockerfile.nashem_perf # RDK S100 (Nash-e/Nash-m) Dockerfile
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├── Dockerfile.bayese_perf # RDK X5 (Bayes-e) Dockerfile
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├── docker_build_nashem.sh # RDK S100 镜像构建脚本
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├── docker_build_bayese.sh # RDK X5 镜像构建脚本
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├── docker_run_nashem_perf.sh # RDK S100 自动化 perf 运行脚本
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├── docker_run_bayese_perf.sh # RDK X5 自动化 perf 运行脚本
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├── docker_test_nashem.sh # RDK S100 交互式进入容器脚本
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├── docker_test_bayese.sh # RDK X5 交互式进入容器脚本
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├── workspace/
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│ ├── perf.py # 性能测试主脚本（两平台共用）
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│ └── entrypoint.sh # 容器入口脚本（两平台共用）
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├── example_fs/ # RDK S100 示例输入输出
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│ ├── input/
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│ │ ├── task.json # 输入协议文件
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│ │ └── *.hbm # 模型文件（放在此处）
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│ └── output/
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│ └── result.json # 运行后自动生成
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└── example_fs_bayese/ # RDK X5 示例输入输出
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├── input/
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│ ├── task.json # 输入协议文件
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│ └── *.hbm / *.bin # 模型文件（放在此处）
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└── output/
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└── result.json # 运行后自动生成
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```
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### 预打包版本
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```bash
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# RDK X5 / RDK X5 module
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docker pull ccr-29eug8s3-pub.cnc.bj.baidubce.com/public/hrt_perf_bayese:v1.24.5
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# RDK S100 / RDK S100P
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docker pull ccr-29eug8s3-pub.cnc.bj.baidubce.com/public/hrt_perf_nashem:v3.7.3
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```
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### RDK S100 系列使用方法（Nash-e / Nash-m）
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**第一步：构建镜像**（只需构建一次，之后可直接交付镜像）
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```bash
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cd bpu_model_perf_images
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bash docker_build_nashem.sh
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```
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构建脚本会自动从板子上找到 `hrt_model_exec`（`which hrt_model_exec`）并打包进镜像，无需手动操作。
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**第二步：准备输入**
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将模型文件（`.hbm`）放入 `example_fs/input/`，并编辑 `example_fs/input/task.json`：
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```json
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{
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"model_relative_path": "your_model.hbm",
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"frame_count": 200
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}
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```
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**第三步：运行**
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```bash
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bash docker_run_nashem_perf.sh
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```
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运行脚本将 `example_fs/input` 挂载为容器内 `/workspace/input`，`example_fs/output` 挂载为 `/workspace/output`。
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**预期输出**
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```text
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============================================================
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task.json content:
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{
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"model_relative_path": "yolo11n_detect_nashe_640x640_nv12.hbm",
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"frame_count": 200
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}
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============================================================
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[OK] 1 model(s) validated, frame_count=200
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[Benchmarking] yolo11n_detect_nashe_640x640_nv12.hbm (/workspace/input/yolo11n_detect_nashe_640x640_nv12.hbm)
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thread_num=1 ... FPS=625.20 avg_latency=1.538ms
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thread_num=2 ... FPS=1133.43 avg_latency=1.703ms
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thread_num=3 ... FPS=1171.23 avg_latency=2.482ms
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thread_num=4 ... FPS=1167.98 avg_latency=3.322ms
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================================================================================
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+---------------------------------------+-----------+------------------+------------+
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| Model | Threads | Avg Latency(ms) | FPS |
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+---------------------------------------+-----------+------------------+------------+
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| yolo11n_detect_nashe_640x640_nv12.hbm | 1 | 1.538 | 625.20 |
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| | 2 | 1.703 | 1133.43 |
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| | 3 | 2.482 | 1171.23 |
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| | 4 | 3.322 | 1167.98 |
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+---------------------------------------+-----------+------------------+------------+
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Results saved to: /workspace/output/result.json
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```
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### RDK X5 系列使用方法（Bayes-e）
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**第一步：构建镜像**
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```bash
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cd bpu_model_perf_images
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bash docker_build_bayese.sh
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```
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**第二步：准备输入**
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将模型文件（`.hbm` 或 `.bin`）放入 `example_fs_bayese/input/`，并编辑 `example_fs_bayese/input/task.json`：
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```json
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{
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"model_relative_path": "your_model.hbm",
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"frame_count": 200
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}
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```
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**第三步：运行**
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```bash
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bash docker_run_bayese_perf.sh
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```
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### 交互式进入容器
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镜像内置了 `hrt_model_exec` 等 BPU 工具，可以交互式进入容器手动调试模型。
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**RDK S100（Nash-e / Nash-m）**
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```bash
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cd bpu_model_perf_images
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bash docker_test_nashem.sh
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```
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**RDK X5（Bayes-e）**
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```bash
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cd bpu_model_perf_images
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bash docker_test_bayese.sh
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```
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进入容器后可直接使用 `hrt_model_exec`，例如：
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```bash
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# 查看帮助
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hrt_model_exec --help
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# 手动跑 perf
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hrt_model_exec perf --model_file /workspace/input/xxx.hbm --thread_num 2 --frame_count 200
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# 手动跑推理
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hrt_model_exec infer --model_file /workspace/input/xxx.hbm --input_file input.jpg
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```
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> 容器以 `--privileged` 模式启动，宿主机的 `/usr/hobot`、`/opt/hobot` 等目录均已挂载，库文件与板子上完全一致。退出容器后自动删除（`--rm`）。
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### task.json 协议说明
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支持两种格式：
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**单模型**
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| 字段 | 类型 | 必填 | 说明 |
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|------|------|------|------|
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| `model_relative_path` | string | 是 | 模型文件名，相对于 input 目录，后缀必须为 `.hbm` 或 `.bin` |
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| `frame_count` | int | 否 | 测试帧数，默认 200 |
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```json
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{
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"model_relative_path": "model.hbm",
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"frame_count": 200
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}
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```
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**多模型**
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| 字段 | 类型 | 必填 | 说明 |
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|------|------|------|------|
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| `models` | list | 是 | 模型列表 |
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| `models[].path` | string | 是 | 模型文件名，相对于 input 目录 |
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| `models[].name` | string | 否 | 显示名称，默认取文件名 |
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| `frame_count` | int | 否 | 测试帧数，默认 200 |
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```json
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{
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"models": [
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{"name": "model_a", "path": "model_a.hbm"},
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{"name": "model_b", "path": "model_b.bin"}
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],
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"frame_count": 200
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}
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```
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> 注意：`model_relative_path` 和 `models` 不能同时出现。
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### 输出结果说明
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结果保存在 `output/result.json`，结构如下：
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```json
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[
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{
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"model_name": "model.hbm",
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"model_path": "/workspace/input/model.hbm",
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"perf_results": [
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{
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"thread_num": 1,
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"frame_count": 200,
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"run_time_ms": 320.18,
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"total_latency_ms": 307.53,
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"avg_latency_ms": 1.538,
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"fps": 625.197,
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"raw_output": "...",
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"returncode": 0
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}
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]
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}
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]
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```
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---
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## CoreMark-PRO CPU 性能测试工具
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### 工具说明
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基于 CoreMark-PRO 标准 CPU 性能测试套件，对 ARM 处理器进行全面性能测试。测试包含 9 个典型工作负载，自动检测 CPU 核心数并在 1 到最大核心数之间进行多线程性能测试，最终输出 CoreMark-PRO 综合得分。
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| 硬件平台 | 架构 | 镜像名称 |
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|----------|------|----------|
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| 所有 ARM 平台 | ARMv8 / aarch64 | `coremark_pro_rdk_arm:latest` |
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CoreMark-PRO 包含以下 9 个工作负载：
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1. **cjpeg-rose7-preset** - JPEG 压缩
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2. **core** - 增强版 CoreMark
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3. **linear_alg-mid-100x100-sp** - 线性代数（单精度浮点）
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4. **loops-all-mid-10k-sp** - Livermore Loops（单精度浮点）
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5. **nnet_test** - 神经网络
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6. **parser-125k** - XML 解析
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7. **radix2-big-64k** - FFT（双精度浮点）
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8. **sha-test** - SHA-256 哈希
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9. **zip-test** - ZIP 压缩
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### 目录结构
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```
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coremark_images/
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├── Dockerfile # 镜像构建文件
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├── docker_build.sh # 构建脚本
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├── docker_run.sh # 运行脚本
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├── docker_test.sh # 测试脚本
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├── workspace/
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│ ├── coremark_perf.py # 性能测试主脚本
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│ └── entrypoint.sh # 容器入口脚本
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├── coremark-pro/ # CoreMark-PRO 源代码
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│ └── builds/linux64/gcc64/bin/ # 预编译的二进制文件
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└── example_fs/
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└── output/ # 默认输出目录
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└── result.json # 运行后自动生成
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```
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### 使用方法
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**第一步：构建镜像**（只需构建一次）
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```bash
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cd coremark_images
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bash docker_build.sh
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```
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**第二步：运行**
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```bash
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bash docker_run.sh
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```
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运行脚本将 `example_fs/output` 挂载为容器内 `/workspace/output`。
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**预期输出**
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```text
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==========================================
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CoreMark-PRO CPU Performance Benchmark
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==========================================
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==========================================================================================
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System Information
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==========================================================================================
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Hostname: xxxxxxxx
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Platform: Linux-6.1.83-aarch64-with-glibc2.35
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Architecture: aarch64
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CPU Model: aarch64
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CPU Count: 8 logical, 8 physical
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CPU Frequency: 1800 MHz
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Memory Total: 5768 MB
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Memory Used: 48.2%
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Timestamp: 2026-03-20 11:37:11
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==========================================================================================
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[Benchmark] Running CoreMark-PRO with thread counts: [1, 2, 3, 4, 5, 6, 7, 8]
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[Benchmark] Total workloads: 9
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--- Running with 1 thread(s) ---
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[1 threads] cjpeg-rose7-preset ... 59.88 iter/s
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[1 threads] core ... 0.46 iter/s
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[1 threads] linear_alg-mid-100x100-sp ... 24.74 iter/s
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...
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==========================================================================================
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CoreMark-PRO Workload Results
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==========================================================================================
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Workload T=1 T=2 T=3 T=4 ... Scaling
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------------------------------------------------------------------------------------------
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cjpeg-rose7-preset 59.88 117.65 169.49 232.56 ... 7.26
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core 0.46 0.91 1.37 1.82 ... 6.07
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...
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==========================================================================================
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==========================================================================================
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||
CoreMark-PRO Score Summary
|
||
==========================================================================================
|
||
Threads CoreMark-PRO Score
|
||
------------------------------------------------------------------------------------------
|
||
1 1390.51
|
||
2 2684.16
|
||
...
|
||
8 7027.42
|
||
------------------------------------------------------------------------------------------
|
||
Final CoreMark-PRO Score 7027.42
|
||
==========================================================================================
|
||
|
||
[OK] Results saved to: /workspace/output/result.json
|
||
[OK] CoreMark-PRO Score: 7027.42
|
||
```
|
||
|
||
### 输出结果说明
|
||
|
||
结果保存在 `example_fs/output/result.json`，结构如下：
|
||
|
||
```json
|
||
{
|
||
"system_info": {
|
||
"cpu": {
|
||
"cpu_count": 8,
|
||
"cpu_count_physical": 8,
|
||
"cpu_freq_mhz": 1800.0,
|
||
"cpu_model": "aarch64",
|
||
"architecture": "aarch64"
|
||
},
|
||
"memory": {
|
||
"total_mb": 5768,
|
||
"available_mb": 2985,
|
||
"percent_used": 48.2
|
||
},
|
||
"system": {
|
||
"hostname": "rdk-board",
|
||
"platform": "Linux-6.1.83-aarch64",
|
||
"timestamp": "2026-03-20 11:37:11"
|
||
}
|
||
},
|
||
"benchmark_config": {
|
||
"workloads": ["cjpeg-rose7-preset", "core", ...],
|
||
"thread_counts": [1, 2, 3, 4, 5, 6, 7, 8],
|
||
"max_threads": 8
|
||
},
|
||
"results": [
|
||
{
|
||
"workload": "cjpeg-rose7-preset",
|
||
"thread_num": 1,
|
||
"iter_per_sec": 59.88,
|
||
"returncode": 0,
|
||
"elapsed_time_sec": 0.17
|
||
}
|
||
],
|
||
"coremark_pro_score": 7027.42
|
||
}
|
||
```
|
||
|
||
| 字段 | 说明 |
|
||
|------|------|
|
||
| `system_info` | 系统信息（CPU、内存、平台等） |
|
||
| `benchmark_config` | 测试配置（workload 列表、线程数等） |
|
||
| `results` | 每个 workload 在不同线程数下的性能数据 |
|
||
| `coremark_pro_score` | 最终 CoreMark-PRO 综合得分 |
|