167 lines
5.9 KiB
Bash
Executable File
167 lines
5.9 KiB
Bash
Executable File
#!/bin/bash
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set -euo pipefail
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# =============================================================================
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# VLM (Qwen2.5-VL-3B) 依赖安装脚本
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# =============================================================================
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# 运行环境：ECS GPU 容器（/workspace 挂载在 EFS 上持久化）
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# 前置条件：NVIDIA GPU 可用，CUDA 驱动已安装
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# =============================================================================
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ROOT="/workspace"
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SITE_PACKAGES="${ROOT}/.site-packages"
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VLM_MODEL_CACHE="${ROOT}/.cache/huggingface"
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RED='\033[0;31m'
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GREEN='\033[0;32m'
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YELLOW='\033[1;33m'
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NC='\033[0m'
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log_info() { echo -e "${GREEN}[INFO]${NC} $*"; }
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log_warn() { echo -e "${YELLOW}[WARN]${NC} $*"; }
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log_error() { echo -e "${RED}[ERROR]${NC} $*"; }
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# -----------------------------------------------------------------------------
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# 0. 环境检查
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# -----------------------------------------------------------------------------
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log_info "检查 GPU / CUDA 环境..."
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if ! command -v nvidia-smi &>/dev/null; then
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log_error "nvidia-smi 未找到。请确认当前为 GPU 实例且 NVIDIA 驱动已安装。"
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exit 1
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fi
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nvidia-smi --query-gpu=name,memory.total,driver_version --format=csv,noheader || true
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if ! python3 -c "import torch; assert torch.cuda.is_available()" 2>/dev/null; then
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log_warn "PyTorch CUDA 未安装或不可用，将重新安装 PyTorch CUDA 版..."
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NEED_PYTORCH_CUDA=1
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else
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python3 -c "import torch; print(f'PyTorch {torch.__version__}, CUDA {torch.version.cuda}, Device: {torch.cuda.get_device_name(0)}')"
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NEED_PYTORCH_CUDA=0
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fi
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# -----------------------------------------------------------------------------
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# 1. 安装 PyTorch CUDA (如需要)
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# -----------------------------------------------------------------------------
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if [[ "$NEED_PYTORCH_CUDA" == "1" ]]; then
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log_info "安装 PyTorch 2.1.2 + CUDA 12.1 ..."
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pip install --target="$SITE_PACKAGES" --upgrade \
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torch==2.1.2 torchvision==0.16.2 torchaudio==2.1.2 \
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--index-url https://download.pytorch.org/whl/cu121 \
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--no-cache-dir
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fi
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# -----------------------------------------------------------------------------
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# 2. 安装 transformers / accelerate / qwen_vl_utils
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# -----------------------------------------------------------------------------
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log_info "安装 transformers + accelerate + qwen_vl_utils ..."
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pip install --target="$SITE_PACKAGES" --upgrade \
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"transformers>=4.40.0" \
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"accelerate>=0.30.0" \
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"qwen-vl-utils>=0.0.8" \
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"pillow>=10.0.0" \
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"safetensors>=0.4.0" \
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--no-cache-dir
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# -----------------------------------------------------------------------------
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# 3. 可选：安装 Flash Attention 2 (Ampere+ GPU)
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# -----------------------------------------------------------------------------
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GPU_ARCH=$(python3 -c "
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import sys, os
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sys.path.insert(0, '$SITE_PACKAGES')
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import torch
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major, minor = torch.cuda.get_device_capability()
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print(f'{major}{minor}')
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" 2>/dev/null || echo "0")
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if [[ "$GPU_ARCH" == "8"* ]] || [[ "$GPU_ARCH" == "9"* ]]; then
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log_info "检测到 Ampere/Hopper 架构 (sm_$GPU_ARCH)，尝试安装 Flash Attention 2..."
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pip install --target="$SITE_PACKAGES" --no-build-isolation \
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"flash-attn>=2.5.0" --no-cache-dir || log_warn "Flash Attention 安装失败，将使用 eager attention"
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else
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log_warn "GPU 架构 sm_$GPU_ARCH 不支持 Flash Attention 2，跳过安装"
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fi
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# -----------------------------------------------------------------------------
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# 4. 验证安装
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# -----------------------------------------------------------------------------
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log_info "验证 Python 包..."
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python3 -c "
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import sys
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sys.path.insert(0, '$SITE_PACKAGES')
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import torch
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print(f'PyTorch: {torch.__version__}')
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print(f'CUDA available: {torch.cuda.is_available()}')
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if torch.cuda.is_available():
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print(f'Device: {torch.cuda.get_device_name(0)}')
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print(f'Memory: {torch.cuda.get_device_properties(0).total_memory / 1e9:.2f} GB')
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"
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python3 -c "
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import sys
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sys.path.insert(0, '$SITE_PACKAGES')
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from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor
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print('transformers + Qwen2.5-VL classes OK')
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"
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python3 -c "
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import sys
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sys.path.insert(0, '$SITE_PACKAGES')
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from qwen_vl_utils import process_vision_info
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print('qwen_vl_utils OK')
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"
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# -----------------------------------------------------------------------------
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# 5. 预下载模型（可选，推荐）
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# -----------------------------------------------------------------------------
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VLM_MODEL="${VLM_MODEL:-Qwen/Qwen2.5-VL-3B-Instruct}"
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log_info "预下载 VLM 模型: $VLM_MODEL ..."
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log_info "模型将缓存到: $VLM_MODEL_CACHE"
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mkdir -p "$VLM_MODEL_CACHE"
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python3 -c "
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import sys
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import os
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sys.path.insert(0, '$SITE_PACKAGES')
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os.environ['HF_HOME'] = '$VLM_MODEL_CACHE'
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from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor
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print(f'正在下载模型: $VLM_MODEL ...')
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model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
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'$VLM_MODEL',
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torch_dtype='auto',
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device_map='auto',
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trust_remote_code=True,
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)
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processor = AutoProcessor.from_pretrained(
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'$VLM_MODEL',
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trust_remote_code=True,
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)
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print('模型下载完成！')
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"
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# -----------------------------------------------------------------------------
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# 6. 完成
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# -----------------------------------------------------------------------------
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log_info "安装完成！"
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log_info ""
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log_info "============================================"
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log_info " VLM 安装摘要"
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log_info "============================================"
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log_info "模型: $VLM_MODEL"
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log_info "缓存路径: $VLM_MODEL_CACHE"
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log_info "Python 包路径: $SITE_PACKAGES"
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log_info ""
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log_info "使用方法:"
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log_info " export VISION_BACKEND=vlm"
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log_info " export VLM_INTERVAL=5.0"
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log_info " ros2 run vision_yolo detector"
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log_info ""
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log_info "如需使用 AWQ 量化版（显存 3-4GB）:"
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log_info " export VLM_MODEL=Qwen/Qwen2.5-VL-3B-Instruct-AWQ"
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log_info "============================================"
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