Is there a simple way to check if an NVIDIA GPU is available on my system using only standard libraries? I've already seen other answers where they recommend using PyTorch or TensorFlow but that's not what I'm looking for. I'd like to know how to do this on both Windows and Linux. Thanks!
When you have Nvidia drivers installed, the command nvidia-smi outputs a neat table giving you information about your GPU, CUDA, and driver setup.
By checking whether or not this command is present, one can know whether or not an Nvidia GPU is present.
Do note that this code will only work if both an Nvidia GPU and appropriate drivers are installed.
This code should work on both Linux and Windows, and the only library it uses is subprocess, which is a standard library.
import subprocess
try:
subprocess.check_output('nvidia-smi')
print('Nvidia GPU detected!')
except Exception: # this command not being found can raise quite a few different errors depending on the configuration
print('No Nvidia GPU in system!')
Following code shows if cuda available. cuda is in contact with gpu
print(torch.cuda.is_available())
print(torch.backends.cudnn.enabled)
Related
I try to train a machine learning model with my rtx380 10g and I use ubuntu 20.4 on my computer.
When i lunch my application, it says me :
2021-07-02 10:29:53.581609: I tensorflow/core/platform/profile_utils/cpu_utils.cc:114] CPU Frequency: 2496000000 Hz
after that, i tried to print len(my phisical_devices('Gpu')) but it gives me "0".
I installed Conda, but i'm not sure it's working. (Same for Cudnn)
To be honest, i'm quite lost now.
I had the same issue, did you install CUDA Toolkit, is it the right version based on your GPU?
Also did you do
pip install tensorflow-gpu
Try this thread: Tensorflow not running on GPU
it's probably a compatibility issue. Make sure your TF version is compatible with the Python, Cuda & Cudnn versions that are installed on ur PC and make sure that Microsoft Visual Studio is installed.
I'm currently using TF2.4, Python 3.8, Cuda 11.0 and Cudnn 8.0. Give this combination a try and see if it works.
I am trying to execute code with pytorch in visual studio code, the problem is that I must be able to do it from the CPU. But my idea is that for certain deep learning projects to use the gpu and others not. How can I switch from CPU to GPU
when i run
import torch
torch.cuda.is_available()
the output is
"False".
i have cuda already installed. I'm using Ubuntu 20.04.2 . It is important for me to do it in Visual Studio Code
Several issues could prevent you from using a GPU.
The GPU is not supported by CUDA or does not have the minimum CUDA version
You installed PyTorch CPU instead of the GPU variant. You will need to reinstall from the PyTorch website
I am having some difficulty running code with the cudf and dask_cudf modules in python.
I am working on Jupyter Labs through Anaconda. I have been able to correctly install my nvidia-gpu driver, cudf (through rapidsai), and cuda. Only, when I go to import cudf in python using import cudf, I get an error reading: "home/lib/python3.7/site-packages/cudf/utils/gpu_utils.py:120: UserWarning: No NVIDIA GPU detected. Warnings.warn("No NVIDIA GPU detected")
My environment:
Linux: RHEL8
Python: 3.7.7
Cuda: 10.2
Nvidia Driver: 390.138
CUDF/Dask_CUDF: 0.13 through rapidsai
I am trying to load and manipulate datasets with data in the hundreds of thousands to millions of items, so I really need the cudf/dask_cudf utility to maximize my time.
When I run nvidia-smi in the terminal, everything looks fine and the persistence mode is on. I have searched all over the internet for a solution with no great ideas. Any help would be appreciated.
Based on the conversations you're having with Robert, it seems that your GPU's architecture being a few generations outside of what RAPIDS will work with is the issue. Thanks Robert for working with Maggie to figure that out!
I wouldn't try to force RAPIDS to work on Kepler when there are so many alternative ways to provision a GPU - even free options for trial purposes!
If you are still interested in trying out RAPIDS and only need a single GPU, please look at our Google Colab notebooks and set up script OR app.blazingsql.com. They are shared or extra instances, with Colab allowing you more customization of your workspace if you need to install more packages and blazing having the fastest "get up and running" time.
If you feel that you need more than one GPU, you move to the paid realm and can provision it with any major cloud provider, install RHEL version of your choice (we only officially support RHEL 7, though).
Does that help you?
I want to know whether opencv3 and python3 have GPU mode,I looked at this link and knew that there was no GPU mode when opencv2, but does opencv3 have GPU mode now?
You can manually compile the OpenCV 3 source with GPU support for Python 3. All steps are outlined in this blog post. To answer your question, follow all parts of Step 0 up to and including step 5 to install OpenCV 3 with GPU support for Python 3.
The major requirement is to have an NVIDIA graphics card with CUDA support and all required graphics drivers installed. These steps should work for any debian-like linux distro, I have tested on Ubuntu 16.04, 17.04 and Linux Mint 18.3 without problem.
As per the latest release 4.0.0-pre, GPU modules are not yet supported by OpenCV-python.
Remaining fields specify what modules are to be built. Since GPU modules are not yet supported by OpenCV-Python, you can completely
avoid it to save time (But if you work with them, keep it there).
Source: OpenCV Docs
Related Question
First, I am not sure my computer(macbook pro) is capable of running tensor flow on GPU. I have checked system/hardware/Graphics-Display settings. You can find below related infos.
Graphics: Intel Iris Pro 1536 MB
So, Is it possible to run tensor flow on GPU with this computer graphics capabilities?
Second question, I am using conda environment to install tensor flow. On tensorflow installation page I could only find pip package for cpu. How could I install tensor gpu package? tensorflow installarion page
https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.3.0-py3-none-any.whl
The newest version of tensorflow does not have gpu support for mac os, as seen here.
I havent found any way to run my application (which uses tensorflow) with gpu support in my Mac. It has an "Intel Iris Ghaphics 6100" graphic card.
In this report (Can I run Cuda or opencl on intel iris?) says that only NVDIA ghaphic cards have CUDA supports, then likely I wont be able to do ..
But I have installed tensorflow-gpu without problems following this ways:
https://wangpei.ink/2019/03/29/Install-TensorFlow-GPU-by-Anaconda(conda-install-tensorflow-gpu)/