NVIDIA Fabric Manager 是用于管理和监控 GPU 集群网络（尤其是 NVSwitch / NVLink / InfiniBand Fabric）的服务。它负责 拓扑管理、链路状态监控、带宽优化和错误检测，确保多 GPU 系统和节点间通信性能稳定、高效。
NVIDIA【Fabric Manager】#
基础环境：
- Ubuntu 22.04；内核5.15.0-119-generic
- NVIDIA GPU
一、基础环境#
1.1 配置源#
- 备份原有 sources.list（若已存在则不重复覆盖）
[ -f /etc/apt/sources.list ] && cp -n /etc/apt/sources.list /etc/apt/sources.list.bak
- 写入阿里云 Ubuntu 22.04 (jammy) 镜像源
cat <<'EOF' > /etc/apt/sources.list
deb https://mirrors.aliyun.com/ubuntu/ jammy main restricted universe multiverse
deb-src https://mirrors.aliyun.com/ubuntu/ jammy main restricted universe multiverse
deb https://mirrors.aliyun.com/ubuntu/ jammy-security main restricted universe multiverse
deb-src https://mirrors.aliyun.com/ubuntu/ jammy-security main restricted universe multiverse
deb https://mirrors.aliyun.com/ubuntu/ jammy-updates main restricted universe multiverse
deb-src https://mirrors.aliyun.com/ubuntu/ jammy-updates main restricted universe multiverse
# deb https://mirrors.aliyun.com/ubuntu/ jammy-proposed main restricted universe multiverse
# deb-src https://mirrors.aliyun.com/ubuntu/ jammy-proposed main restricted universe multiverse
deb https://mirrors.aliyun.com/ubuntu/ jammy-backports main restricted universe multiverse
deb-src https://mirrors.aliyun.com/ubuntu/ jammy-backports main restricted universe multiverse
EOF
- 更新源
sudo apt update
1.2 内核包#
注意: 内核相关的包一定要保持与当前系统内核版本号一致。
apt install linux-image-5.15.0-119-generic linux-headers-5.15.0-119-generic linux-tools-5.15.0-119-generic linux-cloud-tools-5.15.0-119-generic
二、NVIDIA Fabric Manager#
- 安装
# 1. 下载repo
wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/cuda-keyring_1.1-1_all.deb
# 2. 安装
dpkg -i cuda-keyring_1.1-1_all.deb
# 3. 创建源列表
apt update
# 4. 安装；请注意后续其他包之间版本依赖问题
# apt-cache madison 命令查看所有版本
apt install nvidia-fabricmanager-570=570.133.20-1 nvidia-fabricmanager-dev-570=570.133.20-1
- 启动服务
systemctl enable nvidia-fabricmanager.service
systemctl start nvidia-fabricmanager.service
结语#
Fabric Manager 提供 GPU 集群网络管理和优化功能，通过监控链路状态和带宽使用，确保多 GPU 系统在高性能计算和 AI 训练中保持稳定、高效的通信。
参考：
