Runtimeerror: Cuda Error: All Cuda-Capable Devices Are Busy Or Unavailable

Runtimeerror: Cuda Error: All Cuda-Capable Devices Are Busy Or Unavailable. • cuda 11.2, uses linux driver >=460.27.03, you have 460.32.03 • major + minor values = 3.5, need at least 3 computational power to use. Web in pytorch, torch.cuda.is_available() returns true but sending anything to cuda returns runtimeerror:

RuntimeError CUDA error all CUDAcapable devices are busy or from blog.csdn.net

~ provide your environment information using the following. The full error (the process freezes after this. Web what seems to be fine:

Source: github.com

Device to device bandwidth, 1. Print (training on gpu) device =.

Source: blog.csdn.net

Web of course, training on gpu is printed, meaning cuda is available: The full error (the process freezes after this.

Source: blog.csdn.net

Print (training on gpu) device =. • cuda 11.2, uses linux driver >=460.27.03, you have 460.32.03 • major + minor values = 3.5, need at least 3 computational power to use.

Source: stackoverflow.com

• cuda 11.2, uses linux driver >=460.27.03, you have 460.32.03 • major + minor values = 3.5, need at least 3 computational power to use. Device to device bandwidth, 1.

The Full Error (The Process Freezes After This.

Print (training on gpu) device =. Web what exact command you run: Web in pytorch, torch.cuda.is_available() returns true but sending anything to cuda returns runtimeerror:

• Cuda 11.2, Uses Linux Driver >=460.27.03, You Have 460.32.03 • Major + Minor Values = 3.5, Need At Least 3 Computational Power To Use.

Device to device bandwidth, 1. When i'm trying to run the highgui_gpu_gpu example (with all except the gpumat openglgpumatwnd window. Web device to host bandwidth, 1 device(s) pinned memory transfers transfer size (bytes) bandwidth(mb/s) 33554432 9469.3.

Run Module In Terminal Full Logs Or Other Relevant Observations:

If args.n_gpu > 0 and torch.cuda.is_available (): Web on the server a where the code fails the cuda version is 10.1 while the server b where the code runs has cuda version 11. Web of course, training on gpu is printed, meaning cuda is available:

Web What Seems To Be Fine:

~ provide your environment information using the following.