当在CUDA编程或使用依赖CUDA的库(如PyTorch)进行深度学习时,遇到错误信息提示For debugging consider passing CUDA_LAUNCH_BLOCKING=1.时,这通常意味着CUDA运行时在异步执行中遇到了错误,但当前的错误堆栈可能并不准确,因为它捕获的是CPU(host)在GPU(device)报错时正在执行的其他操作。为了更准确地定位问题,可以通过设置...
首先,这个错误提示是告诉你,PyTorch在CUDA运行时遇到了一个错误,但是这个错误信息并没有直接显示出来。为了查看具体的错误信息,你可以设置环境变量CUDA_LAUNCH_BLOCKING=1,这样可以让CUDA运行在同步模式下,从而在发生错误时能够停止程序并显示详细的错误信息。你可以在运行PyTorch程序之前设置这个环境变量,如下所示:在Linux...
确认您正在使用的CUDA版本与安装在系统上的驱动程序和CUDA工具包兼容。您可以参考官方文档进行检查:https://docs.nvidia.com/cuda/cuda-toolkit-release-notes/index.html 尝试将环境变量CUDA_LAUNCH_BLOCKING设置为1,以使CUDA运行时等待所有内核执行完成后再返回结果。这样做可能会降低性能,但有助于确定代码中出现问题...
6 For debugging consider passing CUDA_LAUNCH_BLOCKING=1. 标签和预测不匹配 7 AttributeError: 'LayerNorm' object has no attribute 'affine' pip install thop-0.0.31.post2005241907-py3-none-any.whl
RuntimeError: CUDA error: device-side assert triggered CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect. For debugging consider passing CUDA_LAUNCH_BLOCKING=1. Compile withTORCH_USE_CUDA_DSAto enable device-side assertions. ...
RuntimeError: CUDA error: device-side assert triggered CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect. For debugging consider passing CUDA_LAUNCH_BLOCKING=1. Compile withTORCH_USE_CUDA_DSAto enable device-side assertions. ...
在没有用torch.nn.DataParallel()的情况下 遇到了报错RuntimeError: CUDA error: CUDA OUT OF MEMORYCUDA kernel errors might be asynchronously reported at some other API call,so the stacktrace below might be incorrect.For debugging consider passing CUDA_LAUNCH_BLOCKING=1.但是我的显存是可用且够用的 ...
RuntimeError: CUDA error: device-side assert triggered CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect. For debugging consider passing CUDA_LAUNCH_BLOCKING=1. Compile with TORCH_USE_CUDA_DSA to enable device-side assertions. ...