Merge pull request #120 from svc-design/grntcf-codex/修复离线安装支持问题

Fix GPU offline packaging

docs/gpu-offline-package.md

@@ -20,6 +20,7 @@ bash scripts/create-gpu-k8s-offline-package.sh
 - NVIDIA 驱动（nvidia-driver-535）及 nvidia-container-toolkit 离线包（deb/rpm）
 - nerdctl CLI（v${NERDCTL_VERSION:-2.1.2}）
 - 必要的容器镜像，包括 `registry.k8s.io/pause:3.8`
+- GPU 环境检测脚本 `check-gpu-status.sh`
 
 该离线包用于基于 `sealos` 部署 Kubernetes，最低推荐版本为 **1.29**，也可以使用更新的 `1.30` 等稳定版本。
 

scripts/check-gpu-status.sh

@@ -0,0 +1,78 @@
+#!/bin/bash
+set -euo pipefail
+
+AUTO_FIX=false
+
+# 检查是否带 --fix 参数
+if [[ "${1:-}" == "--fix" ]]; then
+  AUTO_FIX=true
+fi
+
+echo "🔍 Checking NVIDIA GPU status..."
+
+# 1. 检查是否识别 GPU
+echo -e "\n📦 [1] PCI 设备检测:"
+if lspci | grep -i nvidia; then
+  echo "✅ 已检测到 NVIDIA GPU"
+else
+  echo "❌ 未检测到 GPU，请检查硬件绑定或云平台配置"
+  exit 1
+fi
+
+# 2. 检查内核模块
+echo -e "\n📦 [2] 内核模块检测:"
+if lsmod | grep -q nvidia; then
+  echo "✅ nvidia 模块已加载"
+else
+  echo "❌ nvidia 模块未加载"
+  echo "👉 尝试执行：sudo modprobe nvidia"
+fi
+
+# 3. 检查设备节点
+echo -e "\n📦 [3] 设备节点检测:"
+if ls /dev/nvidia0 &>/dev/null; then
+  echo "✅ /dev/nvidia0 存在"
+else
+  echo "❌ 缺少 /dev/nvidia0，驱动可能未成功加载"
+fi
+
+# 4. 检查 nvidia-smi
+echo -e "\n📦 [4] 驱动状态检测 (nvidia-smi):"
+if command -v nvidia-smi &>/dev/null; then
+  if nvidia-smi; then
+    echo "✅ nvidia-smi 正常"
+  else
+    echo "❌ nvidia-smi 执行失败，驱动可能未正确绑定设备"
+  fi
+else
+  echo "❌ 未安装 nvidia-smi 工具"
+  echo "👉 需安装驱动包 nvidia-driver-535、nvidia-utils-535 等"
+  if $AUTO_FIX; then
+    echo -e "\n⚙️ 正在自动安装驱动..."
+    sudo apt-get update
+    sudo apt-get install -y nvidia-driver-535 nvidia-utils-535 dkms linux-headers-$(uname -r)
+    echo -e "\n✅ 驱动安装完成，请重启后再运行本脚本确认"
+    exit 0
+  else
+    echo -e "\n👉 可执行以下命令安装推荐驱动："
+    echo "sudo apt-get update && sudo apt-get install -y nvidia-driver-535 nvidia-utils-535 dkms linux-headers-\$(uname -r)"
+  fi
+fi
+
+# 5. dmesg 错误日志
+echo -e "\n📦 [5] dmesg 日志（最近 NVIDIA 行）:"
+dmesg | grep -i nvidia | tail -n 20 || echo "ℹ️ 无 NVIDIA 错误日志"
+
+# 6. nerdctl 测试（可选）
+if command -v nerdctl &>/dev/null; then
+  echo -e "\n📦 [6] nerdctl GPU 容器测试:"
+  if nerdctl run --rm --gpus all nvidia/cuda:12.2.0-base-ubuntu22.04 nvidia-smi; then
+    echo "✅ nerdctl 能访问 GPU"
+  else
+    echo "❌ nerdctl 无法访问 GPU"
+  fi
+else
+  echo -e "\n📦 [6] nerdctl 未安装，跳过容器测试"
+fi
+
+echo -e "\n🎉 GPU 检查完成"

scripts/create-gpu-k8s-offline-package.sh

@@ -57,6 +57,25 @@ sudo apt-get install --download-only -y "${APT_PACKAGES[@]}"
 cp /var/cache/apt/archives/*.deb "$WORKDIR/packages/"
 sudo apt-get clean
 
+if command -v dnf >/dev/null; then
+  distribution=$(. /etc/os-release; echo $ID$VERSION_ID)
+  curl -s -L "https://nvidia.github.io/nvidia-docker/${distribution}/nvidia-docker.repo" \
+    | sudo tee /etc/yum.repos.d/nvidia-docker.repo
+  sudo dnf -y install 'dnf-command(download)'
+  sudo dnf -y makecache
+  RPM_PACKAGES=(nvidia-container-toolkit nvidia-container-toolkit-base \
+    libnvidia-container-tools libnvidia-container1)
+  if [ -n "${NVIDIA_CONTAINER_TOOLKIT_VERSION:-}" ]; then
+    for pkg in "${RPM_PACKAGES[@]}"; do
+      sudo dnf download --resolve --destdir "$WORKDIR/packages" \
+        "${pkg}-${NVIDIA_CONTAINER_TOOLKIT_VERSION}"
+    done
+  else
+    sudo dnf download --resolve --destdir "$WORKDIR/packages" "${RPM_PACKAGES[@]}"
+  fi
+  sudo rm -f /etc/yum.repos.d/nvidia-docker.repo
+fi
+
 # Download sealos deb
 curl -L -o "$WORKDIR/sealos_5.0.1_linux_amd64.deb" \
   https://github.com/labring/sealos/releases/download/v5.0.1/sealos_5.0.1_linux_amd64.deb
@@ -82,6 +101,7 @@ curl -L -o "$WORKDIR/nvidia-gpgkey" https://nvidia.github.io/nvidia-docker/gpgke
 
 # Include deployment script
 cp "$(dirname "$0")/gpu-k8s.sh" "$WORKDIR/"
+cp "$(dirname "$0")/check-gpu-status.sh" "$WORKDIR/"
 
 # Create final archive
 TAR_NAME="gpu_k8s_offline_packages.tar.gz"