原文:NVIDIA Enroot v4.2.1 文档 Usage。本文完整翻译该页的用法、命令索引与 CUDA 示例,并补充明确标注的版本和静态审阅说明。原页未署个人作者;维护方为 NVIDIA。译编:未完纪。
Enroot 提供了一组用于操作容器沙箱的命令。下面从 NVIDIA GPU Cloud 导入 CUDA 镜像,在沙箱里编译并运行向量加法示例,再将修改后的容器导出为镜像,最后移除工作沙箱。

开始前:版本与运行条件
译注:本文沿用发布标签 v4.2.1 的示例:容器镜像是 nvidia/cuda:12.9.1-devel-ubuntu24.04,CUDA Samples 是 v12.9。这两处版本应一起保留。主机需要可用的 NVIDIA GPU、与该 CUDA 版本兼容的主机驱动,以及已配置好的 Enroot GPU 支持;容器并不会替主机安装内核驱动。
CUDA 12.9 Update 1 的官方发布说明列出 Linux x86_64 对应的 Toolkit 驱动版本为 >=575.57.08。同页另列 CUDA 12.x 小版本兼容性的最低 Linux 驱动 >=525.60.13,但那是带适用条件的兼容模式,不能当作本示例在所有 GPU、特性和 PTX 路径上都可运行的保证。实际部署应按 CUDA 12.9.1 发布说明及兼容指南核对。
命令行用法
用法如下;COMMAND 是子命令,方括号内为可选参数。
Usage: enroot COMMAND [ARG...]
用于操作容器沙箱的命令行工具。
Commands:
batch [options] [--] CONFIG [COMMAND] [ARG...]
bundle [options] [--] IMAGE
create [options] [--] IMAGE
exec [options] [--] PID COMMAND [ARG...]
export [options] [--] NAME
import [options] [--] URI
list [options]
remove [options] [--] NAME...
start [options] [--] NAME|IMAGE [COMMAND] [ARG...]
version
各命令的详细用法见同一版本文档:batch、bundle、create、exec、export、import、list、remove、start、version。
导入镜像并创建沙箱
从 NVIDIA GPU Cloud(NGC)导入 CUDA 12.9.1 开发镜像:
enroot import docker://nvcr.io#nvidia/cuda:12.9.1-devel-ubuntu24.04
使用导出的 SquashFS 文件创建名为 cuda 的容器,并列出当前容器:
enroot create --name cuda nvidia+cuda+12.9.1-devel-ubuntu24.04.sqsh
enroot list
原文示例的列表输出:
cuda
译注:镜像标签并非内容摘要锁定。需要可重复构建时,应另行记录实际镜像摘要、下载来源和构建环境。这里的示例名称 cuda 应留给本次工作使用;已有同名沙箱时先确认其内容,不要覆盖或删除他人的工作。
在容器内编译 vectorAdd
先在可写沙箱中安装 Git 和 CMake,再取得 v12.9 标签的 CUDA Samples:
enroot start --root --rw cuda sh -c 'apt update && apt install --no-install-recommends -y git cmake'
enroot start --root --rw cuda sh -c 'git clone --branch=v12.9 https://github.com/NVIDIA/cuda-samples.git /usr/local/cuda/samples'
进入向量加法示例目录,运行 CMake 并构建:
enroot start --rw cuda sh -c 'cd /usr/local/cuda/samples/Samples/0_Introduction/vectorAdd && cmake . && make -j'
静态审阅:v12.9 的 CMakeLists.txt明确要求 CMake 3.20 及以上、CUDA Toolkit,以及 C++17 / CUDA C++17 特性。--root 用于安装软件和写入系统路径;--rw 允许修改沙箱。不要由此推断沙箱是可安全执行任意不可信代码的安全边界。示例中的 sh -c 参数是固定字符串,没有拼接外部输入;改写脚本时也不要把不可信参数直接插进 shell 命令。
原文的 make -j 没有限定并行作业数,资源紧张时可自行改为例如 make -j2,但这属于本地资源设置调整。Git 克隆要求目标目录不存在;再次执行时应先核对已有源码及其版本。APT 软件包未固定版本,构建结果也会受软件源变化影响。
运行并核对计算结果
enroot start --rw cuda /usr/local/cuda/samples/Samples/0_Introduction/vectorAdd/vectorAdd
静态审阅:vectorAdd.cu对 50,000 个元素计算 C = A + B,检查 CUDA 分配、复制与内核启动的返回值,并逐个比较 GPU 结果与主机计算值;误差大于 1e-5 时退出失败。全部结果通过后才会打印 Test PASSED,随后释放资源,正常结束时打印 Done。
这些输出含义来自源码审阅,并非本次执行记录。这里只能说明样例自身的验证逻辑,不能替代实际驱动、GPU、内存与运行环境测试,也不是性能基准。
导出修改后的镜像,再清理工作沙箱
将安装的软件和编译结果打包到新镜像:
enroot export --output cuda-vecadd.sqsh cuda
确认导出命令成功、输出文件确实存在,并完成需要的备份或验证后,移除本次工作沙箱:
enroot remove cuda
译注:remove 删除的是所指定的沙箱及其修改,不能把它当成无损退出操作。上面的导出和删除分开列出,便于在清理前检查结果;不要在未核对名称、未成功导出的情况下盲目批量删除。
来源、许可与审核范围
原文与命令来自 NVIDIA Enroot v4.2.1 的 usage.md。Copyright (c) 2018–2023, NVIDIA CORPORATION. All rights reserved. Enroot 按 Apache License 2.0 发布;完整许可副本见文末。CUDA Samples 源码另带 NVIDIA 2022 版权声明与 BSD 三条款式再分发条件,不应把它与 Enroot 的许可混为一谈。
本译文于 2026-10-05 添加中文说明、版本条件与静态代码审阅。没有下载容器、安装软件、克隆或执行样例,也没有实际导出或删除沙箱。
示例代码许可证
随示例代码保留项目版权与许可通知。来源:官方 LICENSE。代码许可与本文其他素材的权利分别适用。
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