#!/bin/bash # Qwen3-Omni Thinker 训练快速启动脚本 set -e # 遇到错误立即退出 echo "==========================================" echo "Qwen3-Omni Thinker 训练环境检查" echo "==========================================" # 1. 检查Python环境 echo "检查Python版本..." python --version # 2. 检查必要的包 echo "" echo "检查必要的Python包..." REQUIRED_PACKAGES=("torch" "transformers" "datasets" "deepspeed" "accelerate") for package in "${REQUIRED_PACKAGES[@]}"; do if python -c "import $package" 2>/dev/null; then echo "✓ $package 已安装" else echo "✗ $package 未安装" echo " 安装命令: pip install $package" fi done # 3. 检查CUDA echo "" echo "检查CUDA环境..." if command -v nvidia-smi &> /dev/null; then nvidia-smi --query-gpu=name,memory.total --format=csv else echo "警告: nvidia-smi 未找到" fi # 4. 检查必要的文件 echo "" echo "检查配置文件..." FILES=("train.py" "configs/ds_config_zero2.json" "train_qwen3_omni.sbatch") for file in "${FILES[@]}"; do if [ -f "$file" ]; then echo "✓ $file 存在" else echo "✗ $file 不存在" fi done # 5. 创建必要的目录 echo "" echo "创建必要的目录..." mkdir -p logs mkdir -p output mkdir -p data echo "✓ 目录创建完成" # 6. 检查模型和数据路径配置 echo "" echo "==========================================" echo "下一步操作:" echo "==========================================" echo "1. 下载或准备Qwen3-Omni模型" echo " 例如: git lfs clone https://huggingface.co/Qwen/Qwen3-Omni-base" echo "" echo "2. 准备训练数据" echo " 方式1: 创建示例数据测试" echo " $ python prepare_data.py --create-sample --output data/train.jsonl" echo "" echo " 方式2: 转换您的数据" echo " $ python prepare_data.py --input your_data.jsonl --output data/train.jsonl" echo "" echo "3. 修改SLURM脚本中的路径" echo " 编辑 train_qwen3_omni.sbatch 或 train_qwen3_omni_single.sbatch" echo " - MODEL_PATH: 模型路径" echo " - DATA_PATH: 数据路径" echo " - SBATCH参数: 根据集群配置修改" echo "" echo "4. 提交训练任务" echo " 多节点: sbatch train_qwen3_omni.sbatch" echo " 单节点: sbatch train_qwen3_omni_single.sbatch" echo "" echo "5. 监控训练" echo " 查看队列: squeue -u \$USER" echo " 查看日志: tail -f logs/train_.log" echo "=========================================="