1. 理解cuda_launch_blocking参数的作用 CUDA_LAUNCH_BLOCKING是一个环境变量,用于控制CUDA操作的执行模式。 当CUDA_LAUNCH_BLOCKING=1时,CUDA操作将同步执行,即CPU会等待GPU上的操作完成后再继续执行,这有助于捕获更准确的错误堆栈。 默认情况下,CUDA操作是异步的,以提高CPU和GPU的并行效率。 2. 在代码中设置cuda...
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...
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. ...
6 For debugging consider passing CUDA_LAUNCH_BLOCKING=1. 7 AttributeError: 'LayerNorm' object has no attribute 'affine' 1 cuda 路径错误提示: (base) wenjtop@Server:/opt$ sudo ln -sf /usr/local/cuda-11.3/targets/x86_64-linux/lib/libcudnn_adv_train.so.8.2.1 /usr/local/cuda-11.3/targe...
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 withTORCH_USE_CUDA_DSAto enable device-side assertions. ...