If you are confirm that you're using GPU(s) then try updating the nvidia drivers to an appropriate version(in Ubuntu distro anything >= 450 is good enough) Try running torch.cuda.devices to get the number of devices, It should show correct number of devices. Once it starts showing the ...
changing env variable CUDA_VISIBLE_DEVICES after program start. Setting the available devices to be zero.: str Traceback (most recent call last): File "/home/a/ais/stable-diffusion-webui/modules/errors.py", line 98, in run code() File "/home/a/ais/stable-diffusion-webui/modules/devices...
Even then, you have to stop WSL from doing a driver version check by using a NVIDIA_DISABLE_REQUIRE=1 environment variable, as per ch14ota’s link below. E.g.: docker run --gpus all --env NVIDIA_DISABLE_REQUIRE=1 nvcr.io/nvidia/k8s/cuda-sample:nbody nbody -gpu -benchmark Go...
1. NVIDIA GPU Accelerated Computing on WSL 2 — CUDA on WSL 12.3 documentation The guide for using NVIDIA CUDA on Windows Subsystem for Linux. However now when I try to run any docker like sudo docker run --gpus all nvcr.io/nvidia/k8s/cuda-sample:nbo...
# check if torch supports GPU; this must output "True". You need CUDA 11. installed for this. You might be able to use # a different version, but this is what I tested. python -c "import torch; print(torch.cuda.is_available())" ...
TensorFlow is able to allocate 7 out of 8 GB dedicated GPU memory. So there is no issue accessing the VRAM via WSL and TensorFlow. I have installed AlphaFold without Docker followingthis Non Docker Setupof@sanjaysrikakulam. I still got theCUDA_ERROR_OUT_OF_MEMORYerror messages, but after som...
---+ Your runtime has 27.4 gigabytes of available RAM You are using a high-RAM runtime! And still the same issue (even without option --with_scratch): Running Stage 1: Overall restoration Now you are processing 1.jpeg Skip 1.jpeg due to an error: CUDA out of memory. Tried to alloc...
(64-bit runtime) Python platform: Linux-5.4.72-microsoft-standard-WSL2-x86_64-with-debian-buster-sid Is CUDA available: False CUDA runtime version: No CUDA GPU models and configuration: No CUDA Nvidia driver version: No CUDA cuDNN version: No CUDA HIP runtime version: N/A MIOpen run...
maybe--gpus allinstead of--runtime=nvidia. I also ran with this since I wasn't able to produce the same steps in my WSL. Can someone expert on this subject explain the necessity of--runtime=nvidia? dev10110 commentedon Jul 2, 2023 ...
docker run --gpus all nvidia/cuda:10.0-base nvidia-smi i get this error docker: Error response from daemon: OCI runtime create failed: container_linux.go:349: starting container process caused "process_linux.go:449: container init caused \"process_linux.go:432: running prestart hook 0 caus...