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或Mac上:export C
for debugging consider passing cuda_launch_blocking=1. 文心快码 这个错误通常是因为CUDA内核执行时间过长导致的超时错误。 CUDA内核执行时间过长,超过了GPU的默认超时限制,导致内核被终止。这个错误可能异步地报告在其他API调用中,因此堆栈跟踪可能不正确。为了调试这个问题,可以考虑设置环境变量CUDA_LAUNCH_BLOCKING=1...
1 cuda 路径错误提示: 2 卸载docker 3 AttributeError: 'Upsample' object has no attribute 'recompute_scale_factor' 4 status-code=409 kind=snap-change-conflict message=snap 5 RuntimeError: “unfolded2d_copy“ not implemented for ‘Half‘ 6 For debugging consider passing CUDA_LAUNCH_BLOCKING=1. ...
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. ...
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. ...
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. 我的解决方案: 重启容器就解决了。 sudo docker restart 容器ID/名称
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. The requirements is here: aiofiles==0.4.0 aniso8601==3.0.2 apispec==1.0.0b6 ...
I got the following error: RuntimeError: CUDA error: no kernel image is available for execution on the device 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_BLOCK...
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. To troubleshoot: Try running the code on CPU to see if the error is reproducible. ...
For debugging consider passing CUDA_LAUNCH_BLOCKING=1. Compile withTORCH_USE_CUDA_DSAto enable device-side assertions. 2024-03-29 18:28:51,875 xinference.api.restful_api 8 ERROR [address=0.0.0.0:43266, pid=897] CUDA error: invalid argument ...