Hi, I am having the same issue described here: https://github.com/microsoft/WSL/issues/5598
In summary: Although torch is able to find CUDA, and nothing else is using the GPU, I get the error “all CUDA-capable devices are busy or unavailable”
Windows 10, Insider Build 20226
NVIDIA driver 460.20
WSL 2 kernel version 4.19.128
Python:
import torch
torch.cuda.is_available()
> True
torch.randn(5)
> tensor([-2.6408, -1.0831, -1.6984, 0.4742, -0.5909])
torch.randn(5).to(2)
> Traceback (most recent call last):
> File “”, line 1, in
> RuntimeError: CUDA error: all CUDA-capable devices are busy or unavailable
deviceQuery:
./deviceQuery
./deviceQuery Starting…
CUDA Device Query (Runtime API) version (CUDART static linking)
Detected 1 CUDA Capable device(s)
Device 0: “GeForce RTX 2080 with Max-Q Design”
CUDA Driver Version / Runtime Version 11.2 / 11.0
CUDA Capability Major/Minor version number: 7.5
Total amount of global memory: 8192 MBytes (8589934592 bytes)
(46) Multiprocessors, ( 64) CUDA Cores/MP: 2944 CUDA Cores
GPU Max Clock rate: 1095 MHz (1.10 GHz)
Memory Clock rate: 6001 Mhz
Memory Bus Width: 256-bit
L2 Cache Size: 4194304 bytes
Maximum Texture Dimension Size (x,y,z) 1D=(131072), 2D=(131072, 65536), 3D=(16384, 16384, 16384)
Maximum Layered 1D Texture Size, (num) layers 1D=(32768), 2048 layers
Maximum Layered 2D Texture Size, (num) layers 2D=(32768, 32768), 2048 layers
Total amount of constant memory: 65536 bytes
Total amount of shared memory per block: 49152 bytes
Total number of registers available per block: 65536
Warp size: 32
Maximum number of threads per multiprocessor: 1024
Maximum number of threads per block: 1024
Max dimension size of a thread block (x,y,z): (1024, 1024, 64)
Max dimension size of a grid size (x,y,z): (2147483647, 65535, 65535)
Maximum memory pitch: 2147483647 bytes
Texture alignment: 512 bytes
Concurrent copy and kernel execution: Yes with 2 copy engine(s)
Run time limit on kernels: Yes
Integrated GPU sharing Host Memory: No
Support host page-locked memory mapping: Yes
Alignment requirement for Surfaces: Yes
Device has ECC support: Disabled
Device supports Unified Addressing (UVA): Yes
Device supports Managed Memory: Yes
Device supports Compute Preemption: Yes
Supports Cooperative Kernel Launch: Yes
Supports MultiDevice Co-op Kernel Launch: Yes
Device PCI Domain ID / Bus ID / location ID: 0 / 1 / 0
Compute Mode:
< Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) >
deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 11.2, CUDA Runtime Version = 11.0, NumDevs = 1
Result = PASS