I installed Anaconda, CUDA, and PyTorch today, and I can’t access my GPU (RTX 2070) in torch. I followed all of installation steps and PyTorch works fine otherwise, but when I try to access the GPU either in shell or in script I get >>> import torch >>> torch.cuda.is_available() False >>> torch.cuda.device_count() 0 >>> print(torch.version.cuda) ..
This is my setup, what I have done: Windows 10 x64 Pro Nvdia 1050Ti Installed Cuda 11.5 Python 3.8.12 conda install cudatoolkit pip install tensorflow-gpu Now in Jupyter: import tensorflow as tf print(tf.__version__) reports: 2.6.0 import tensorflow as tf print("GPUs: ", tf.config.list_physical_devices(‘GPU’)) reports: GPUs:  What am I missing? Source: Python-3x..
Hi I’m working on a project that requires tensorflow 2.3.0 with CUDA10.1 and cndnn7 however all installation i see online on checking ”’ nvidia-smi ”’ the cuda version displayed is 11.5 and when i checked with ”’nvcc –version”’ the CUDA version is CUDA9 please i need someone to tell me whats happening and how do ..
@guvectorize([‘void(float64[:, :], int64[:, :, :], float64[:, :])’], ‘(m, n), (g, h, m) ->(g, h)’, target=’cuda’, nopython=True) def das(data1, k_value, image1): for i in range(image1.shape) : for j in range(image1.shape) : sum = 0. for k in range(data1.shape): k_num = k_value[i, j, k] if k_num < data1.shape : sum += data1[k, k_num] image1[i, j] = sum ..
I’m trying to start using Cupy for some Cuda Programming. I need to write my own kernels. However, I’m struggling with 2D kernels. It seems that Cupy does not work the way I expected. Here is a very simple example of a 2D kernel in Numba Cuda: import cupy as cp from numba import cuda ..
I work on mobility simulation and location prediction of people using CUDA, but recently I got stuck at cuMemAlloc issue when working with large arrays. I have been testing on Windows laptop but after reading about windows watchdog timer, I started testing on Linux with GeForce RTX 2080 (still got the issue). This is a ..
I would like to free and Reuse the GPU while using Tensorflow. I imagen a workflow like this: Make a TF calculation. Free the GPU Wait a while Step 1. again. This is the code i use right no. Steps 1 to 3 are working step 4 is not: import time import tensorflow as tf ..
My Program will make some physical simulation in the 2 dimensional space on the GPU using CUDA. After a certain iteration of steps the data will get copied to the normal RAM and then each pixel gets copied into some PyQt Pixmap for it to display as a Bitmap Image. heatmap = cuda_map.copy_to_host() for x ..
So I’m currently working with GPT2 running on Tensorflow for text generation. I’m working with this repo specifically. I recently decided to install CUDA and cudnn to improve GPU capability and installed it via these instructions. I’m currently using Windows 10 x64 with NVIDIA Geforce GTX 1650 for my GPU and I’m using the command ..
@guvectorize([‘void(float64[:, :], int64[:, :, :], float64[:, :])’], ‘(m, n), (g, h, m) ->(g, h)’, target=’cuda’) def addition(data1, k_value, image1): for i in range(image1.shape) : for j in range(image1.shape) : sum = 0. for k in range(data1.shape): time1 = k_value[i, j, k] if time1 < data1.shape : sum += data1[k, time1] image1[i, j] = sum when ..