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MZ YANG
193
example/debug_cuda_issue.py
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193
example/debug_cuda_issue.py
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#!/usr/bin/env python3
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"""详细诊断CUDA问题"""
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import sys
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import os
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import subprocess
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def check_system_info():
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"""检查系统信息"""
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print("=== 系统信息 ===")
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import platform
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print(f"操作系统: {platform.system()} {platform.release()}")
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print(f"Python版本: {sys.version}")
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print(f"Python路径: {sys.executable}")
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print(f"当前目录: {os.getcwd()}")
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def check_nvidia_driver():
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"""检查NVIDIA驱动"""
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print("\n=== NVIDIA驱动检查 ===")
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try:
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result = subprocess.run(['nvidia-smi'], capture_output=True, text=True, encoding='utf-8', errors='ignore')
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if result.returncode == 0:
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print("✓ nvidia-smi 命令成功")
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# 提取驱动版本
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for line in result.stdout.split('\n'):
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if 'Driver Version' in line:
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print(f"驱动版本: {line.strip()}")
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if 'CUDA Version' in line:
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print(f"CUDA版本: {line.strip()}")
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return True
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else:
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print("✗ nvidia-smi 命令失败")
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print(f"错误: {result.stderr}")
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return False
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except FileNotFoundError:
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print("✗ nvidia-smi 未找到")
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return False
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except Exception as e:
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print(f"✗ 检查NVIDIA驱动时出错: {e}")
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return False
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def check_cuda_toolkit():
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"""检查CUDA工具包"""
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print("\n=== CUDA工具包检查 ===")
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try:
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result = subprocess.run(['nvcc', '--version'], capture_output=True, text=True, encoding='utf-8', errors='ignore')
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if result.returncode == 0:
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print("✓ nvcc 命令成功")
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for line in result.stdout.split('\n'):
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if 'release' in line.lower():
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print(f"CUDA编译器: {line.strip()}")
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return True
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else:
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print("✗ nvcc 命令失败")
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return False
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except FileNotFoundError:
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print("✗ nvcc 未找到")
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return False
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except Exception as e:
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print(f"✗ 检查CUDA工具包时出错: {e}")
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return False
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def check_pytorch_detailed():
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"""详细检查PyTorch安装"""
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print("\n=== PyTorch详细检查 ===")
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try:
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import torch
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print(f"PyTorch版本: {torch.__version__}")
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print(f"PyTorch路径: {torch.__file__}")
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# 检查是否包含+cpu
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if '+cpu' in torch.__version__:
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print("⚠ PyTorch版本包含 '+cpu'，这是CPU版本")
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elif '+cu' in torch.__version__.lower():
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cuda_version = torch.__version__.split('+cu')[-1].split('+')[0]
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print(f"✓ PyTorch是GPU版本，CUDA: {cuda_version}")
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else:
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print("ℹ PyTorch版本信息不明确")
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# 检查CUDA可用性
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print(f"\nCUDA可用性检查:")
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print(f" torch.cuda.is_available(): {torch.cuda.is_available()}")
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if torch.cuda.is_available():
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print(f" CUDA版本: {torch.version.cuda}")
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print(f" GPU数量: {torch.cuda.device_count()}")
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for i in range(torch.cuda.device_count()):
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print(f" GPU {i}: {torch.cuda.get_device_name(i)}")
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print(f" 内存: {torch.cuda.get_device_properties(i).total_memory / 1024**3:.2f} GB")
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# 测试GPU计算
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try:
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x = torch.randn(3, 3).cuda()
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y = torch.randn(3, 3).cuda()
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z = torch.matmul(x, y)
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print(f" GPU计算测试: ✓ 成功")
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except Exception as e:
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print(f" GPU计算测试: ✗ 失败 - {e}")
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else:
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print(" CUDA不可用，原因可能是:")
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print(" 1. 安装了CPU版本的PyTorch")
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print(" 2. CUDA驱动不匹配")
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print(" 3. 系统环境问题")
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return torch.cuda.is_available()
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except ImportError:
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print("✗ PyTorch未安装")
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return False
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except Exception as e:
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print(f"✗ 检查PyTorch时出错: {e}")
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return False
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def check_conda_environment():
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"""检查conda环境"""
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print("\n=== Conda环境检查 ===")
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try:
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result = subprocess.run(['conda', 'info', '--envs'], capture_output=True, text=True, encoding='utf-8', errors='ignore')
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if result.returncode == 0:
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print("当前conda环境:")
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for line in result.stdout.split('\n'):
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if '*' in line:
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print(f" {line.strip()}")
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else:
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print("无法获取conda环境信息")
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except Exception as e:
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print(f"检查conda环境时出错: {e}")
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def check_torch_installation():
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"""检查torch安装详情"""
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print("\n=== Torch安装详情 ===")
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try:
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import pkg_resources
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packages = ['torch', 'torchvision', 'torchaudio']
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for pkg in packages:
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try:
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dist = pkg_resources.get_distribution(pkg)
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print(f"{pkg}: {dist.version} ({dist.location})")
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except pkg_resources.DistributionNotFound:
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print(f"{pkg}: 未安装")
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except Exception as e:
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print(f"检查包详情时出错: {e}")
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def check_environment_variables():
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"""检查环境变量"""
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print("\n=== 环境变量检查 ===")
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cuda_vars = ['CUDA_HOME', 'CUDA_PATH', 'PATH']
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for var in cuda_vars:
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value = os.environ.get(var, '未设置')
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if var == 'PATH' and 'cuda' in value.lower():
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print(f"{var}: 包含CUDA路径")
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elif var != 'PATH':
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print(f"{var}: {value}")
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def main():
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print("CUDA问题详细诊断工具")
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print("=" * 60)
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check_system_info()
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check_nvidia_driver()
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check_cuda_toolkit()
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check_conda_environment()
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check_environment_variables()
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check_torch_installation()
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cuda_available = check_pytorch_detailed()
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print("\n" + "=" * 60)
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print("=== 问题诊断与解决方案 ===")
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if not cuda_available:
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print("\n问题: PyTorch无法检测到CUDA")
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print("\n可能的原因和解决方案:")
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print("1. 安装了CPU版本的PyTorch")
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print(" 解决方案: 重新安装GPU版本的PyTorch")
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print(" 命令: pip uninstall torch torchvision torchaudio")
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print(" pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121")
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print()
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print("2. CUDA版本不匹配")
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print(" 你的CUDA版本: 12.3")
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print(" 需要安装对应的PyTorch版本")
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print(" 命令: pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu123")
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print()
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print("3. 环境变量问题")
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print(" 确保CUDA_HOME或CUDA_PATH正确设置")
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print()
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print("4. 驱动程序问题")
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print(" 更新NVIDIA驱动程序到最新版本")
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else:
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print("\n✓ CUDA可用，可以正常使用GPU")
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print("运行命令: python run_pipeline.py --device cuda")
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if __name__ == "__main__":
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main()
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