Is there an automatic way to check which GPU is currently available on Google Colab (Pro).
Say I would like to use a Tesla P100 instead of the Tesla T4 to train my model, is there a way to periodically check with a python script in Colab whether the P100 is available?
I have tried eliminate the kernel periodically but it won't restart again automatically after shutting down:
import os
def restart_runtime():
os.kill(os.getpid(), 9)
Thank you
There is no way to check what GPU is available. Add the line:
!nvidia-smi
to the beginning of your code and then keep on disconnecting and reconnecting the runtime until you get the GPU that you want.
Related
I met this issue when I running my python file in linux.
I searched some answers in google like use the code below:
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
The system can be running but without ant out information. Actually I found the 2 measn ignore all information while 1 means give all information like error or normal output.
because I need to use GPU so my original code is:
os.environ["CUDA_VISIBLE_DEVICES"]="0"
But if I keep the code my output would be an error information like the title.
How can I do? and of course I need use the GPU and the codes can be running in colab which indicated that my code has no problem.
some guys even said uninstall tensorboard...that must a wrong way.
Or should I download tensorflow gpu not tensorflow in m,y virtual enviroment? when I USE THE tensorflow gpu version, the error is core dumped.
If when forcing the os.environ["CUDA_VISIBLE_DEVICES"]="0" doesn't work, then this means that your tensorflow gpu installation did not succeed. You must ensure you have the right combination of TensorFlow + CUDA + CUDNN. That is why you get the error, because due to improper versions/installation TF falls back on CPU.
I am using A100-SXM4-40GB Gpu but training is terribly slow. I tried two models, a simple classification on cifar and a Unet on Cityscapes. I tried my code on other GPUs and it worked totally fine, but I do not know why training on this high capacity GPU is super slow.
I would appreciate any help.
Here are some other properties of GPUs.
GPU 0: A100-SXM4-40GB
GPU 1: A100-SXM4-40GB
GPU 2: A100-SXM4-40GB
GPU 3: A100-SXM4-40GB
Nvidia driver version: 460.32.03
cuDNN version: Could not collect
Thank you for your answer. Before trying your answer, I decided to uninstall anaconda and reinstall it and this solved the problem.
Call .cuda() on the model during initialization.
As per your above comments, you have GPUs, as well as CUDA installed, so there's no point of checking the device availability with torch.cuda.is_available().
Additionally, you should wrap your model in nn.DataParallel to allow PyTorch use every GPU you expose it to. You also could do DistributedDataParallel, but DataParallel is easier to grasp initially.
Example initialization:
model = UNet().cuda()
model = torch.nn.DataParallel(model)
Also, you can be sure you're exposing the code to all GPUs by executing the python script with the following flag:
CUDA_VISIBLE_DEVICES=0,1,2,3 python3 train_unet.py
Last thing to note - nn.DataParallel encapsulates the model itself, so for saving the state_dict, you'll need to reach module inside DataParallel:
torch.save(model.module.state_dict(), 'unet.pth')
I am trying to use the cv2.FastFeatureDetector() method and everytime i run this code to extract features my kernel in both Google Collab and Anaconda crashes for some reason. Initially I thought it was memory management issue with my system, but the same thing is happening in Colab.
import cv2
import numpy as np
image=cv2.imread('tree.jpg',0)
fast=cv2.FastFeatureDetector()
keypoints=fast.detect(image,None)
#After running this code my kernel crashes
There is no error message due to the kernel crash.
The image is fairly small in size and not that computationally expensive.
Here is the image:
https://www.setaswall.com/wp-content/uploads/2017/06/Sun-Tree-Branches-1920-x-1080.jpg
I had the same problem. With newer OpenCV versions you have to create your detector via fast = cv2.FastFeatureDetector_create(). Note that you might have to adjust the rest of your code due to other API changes.
Im trying to train a Neural Network that I wrote, but it seems that colab is not recognizing the gtx 1050 on my laptop. I can't use their cloud GPU's for this task, because I run into memory constraints
print(cuda.is_available())
is returning False
Indeed you gotta select the local runtime accelerator to use GPUs or TPUs, go to Runtime then Change runtime type like in the picture:
And then change it to GPU (takes some secs):
Im new in Python and Theano library. I want to install Theano on windows 7-64. I have a display adapters :
Intel(R) HD Graphics 3000 which is not compatible with NVIDA.
My QUESTIONS:
1-Is obligatory to install CUDA to i can use Theano?
2- Even if i have an Ubuntu opearting system, with the same display adapters, CUDA still mandatory?
Any help!
Thanks
You do not need CUDA to run Theano.
Theano can run on either CPU or GPU. If you want to run on GPU you must (currently) use CUDA which means you must be using a NVIDIA display adapter. Without CUDA/NVIDIA you must run on CPU.
There is no disadvantage to running on CPU other than speed -- Theano can be much faster on GPU but everything that runs on a GPU will also run on a CPU as long as it has been coded generically (the default and standard method for Theano code).