Category "gpu"

RTX 3070 compatibility with Pytorch

NVIDIA GeForce RTX 3070 with CUDA capability sm_86 is not compatible with the current PyTorch installation. The current PyTorch install supports CUDA capabilit

Validation warning about SPIR-V Capability

I'm using Vulkan for heavy GPU computations and in some kernels I'm applying subgroup arithmetic operations. In order to use this, I've included necessary exten

OpenCL/GPU/CUDA support under WSL2

I read that this almost impossible right now to use the GPU under WSL2 (Ubuntu-20.04 distro), but NVidia has some tutorials using docker (my GPU is nVidia 960m)

How can I fix this "dpkg" error while installing CUDA on google colab

I want to run CUDA code on google colab. For that I am following the below steps but I am not able to install CUDA packages. Step 1: Removing previous CUDA vers

Cannot install NVIDIA GPU driver 470.82.01 on the on Google Kubernetes Engine 1.21

I would like to run GPU nodes in a GKE cluster, that requires an installation DaemonSet. According to https://cloud.google.com/kubernetes-engine/docs/how-to/gpu

Why is Tensorflow not recognizing my GPU after conda install?

I am new to deep learning and I have been trying to install tensorflow-gpu version in my pc in vain for the last 2 days. I avoided installing CUDA and cuDNN dri

How to make Intel GPU available for processing through pytorch?

I'm using a laptop which has Intel Corporation HD Graphics 520. Does anyone know how to it set up for Deep Learning, specifically Pytorch? I have seen if you ha

tensorflow: Fail to find dnn implementation

I'm trying to run my code Keras CuDNNGRU on tensorflow using gpu but it always get error "Fail to find dnn implementation" even though I already installed CUDA

pytools.prefork.ExecError: error invoking 'nvcc --version': [Errno 2] No such file or directory

I have install pycuda and I am trying to test it with code below. import pycuda.driver as cuda import pycuda.autoinit from pycuda.compiler import SourceModule

Powershell. Why where {} directive can not find two matching DeviceID? One taken from Win32_VideoController, another from Regedit value

Here is code # | Get DeviceID and Name of GPUs presented in system $GPU_Inf = Get-CIMInstance -Query "SELECT Caption, PNPDeviceID from Win32_VideoController" #

GPU memory is empty, but CUDA out of memory error occurs

During training this code with ray tune(1 gpu for 1 trial), after few hours of training (about 20 trials) CUDA out of memory error occurred from GPU:0,1. And ev

FFMPEG with moviepy

I'm working on something that concatenate videos and adds some titles on through moviepy. As I saw on the web and on my on pc moviepy works on the CPU and takes

Training Yolov5 on RTX 3060 Ti GPU I'm getting error "RuntimeError: Unable to find a valid cuDNN algorithm to run convolution"

Training Yolov5 with --img 8088 and batch size 16 on RTX 3060 Ti GPU using the following command python train.py --img 1088 --batch 16 --epochs 3 --data coco12

numba cuda does not produce correct result with += (gpu reduction needed?)

I am using numba cuda to calculate a function. The code is simply to add up all the values into one result, but numba cuda gives me a different result from nu

UEFI secure boot, how (or will) the PCIE device (firmware) are checked?

Recently I'm searching for info about if PCIe devices are involved in the uefi secure boot, and if so, how it is done. From the uefi specification, the main boo

UnimplementedError: Graph execution error: running nn on tensorflow

I have been having this error, and I don't know why, especially since I am following someone's code exactly and the person had no error when running this img_sh

Google Colab GPU RAM depletes quickly on test data but not so on training data

I am training my neural network built with PyTorch under Google Colab Pro+ (Tesla P100-PCIE GPU) but encounters the following strange phenomenon: The amount of

WebGL2: what is up with max elements indices/vertices?

I'm developing a web component using WebGL2 (and three.js) with OES_element_index_uint enabled. I'm drawing a geometry using indexed vertices and I'm seeing th

Is there any way to break the limit of openCL memory in android?

I want to allocate 4.5GB to my openCL program in android phone wiht 8GB memory. But I found the memory size from CL_DEVICE_GLOBAL_MEM_SIZE is much lower than th

Is there any way to break the limit of openCL memory in android?

I want to allocate 4.5GB to my openCL program in android phone wiht 8GB memory. But I found the memory size from CL_DEVICE_GLOBAL_MEM_SIZE is much lower than th