Hi Im using Colab for my project
And I have a problem about using GPU in Colab.
Although I Change My Runtime Type to 'GPU'
However, I keep getting a pop-up saying'I am connected to the GPU runtime but I am not using the GPU'.
For reference, I got a message like this because it was less than 10 minutes after I started learning. Is there a need for extra code to use the GPU?
Related
I'm trying to do object detection with yolo. I want to use gpu instead of cpu, but as you can see, I get the following error;
DNN MODULE WAS NOT BUILT WITH CUDA BACKEND; SWITCHING TO CPU
I am using windows 10. can you help?
I have bought the Colab Pro, whereas I can only apply for the P100 for most of the time. V100 can run almost 2 times faster than P100. How can I get a V100 manually?
I have also been using Colab Pro for a long time, and as far as I know these resources are allocated according to Google's availablity. I have been using Tesla P100-PCIE-16GB most of the time, but at random times I get assigned a Tesla V100-SXM2-16GB.
BTW, to print your device name, you can use this command in Pytorch:
import torch
torch.cuda.get_device_name(device=None)
It's about three months since I started using Colab pro, and ever since, I haven't even a single time gotten the V100, and most of the time, I got the P100 and some times T4.
And to get the GPU that you are using in Colab, the best way is to use the command below:
!nvidia-smi
I am trying to train a model on around 4500 text sentences. The embedding is however heavy. The session crashes for number of training clauses > 350. It works fine and displays results till 350 sentences though.
Error message:
Your session crashed after using all available RAM.
Runtime type - GPU
I have hereby attached the screenshot of logs.
I am considering training model by batches, but I am a newbie and finding it difficult to have my way around it. Any help will be appreciated. session logs attached
This is basically because of out of memory on Google colab.
Google colab provides ~12GB Free RAM, to extend it 25 GB follow the instruction mentioned here.
Your session crashed for an unknown reason
when I run the following cell in Google Colab:
from keras import backend as K
if 'tensorflow' == K.backend():
import tensorflow as tf
from keras.backend.tensorflow_backend import set_session
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
config.gpu_options.visible_device_list = "0"
set_session(tf.Session(config=config))
I receive this message since I have uploaded two data sets to google drive.
Does anyone know this message and can give me some advice?
Many thanks for every hint.
Update:
I always receive the message
Update
I have removed the data sets from Google Drive, but the session is still crashing.
Google Colab is crashing because you are trying to Run Code related to GPU with Runtime as CPU.
The execution is successful if you change the Runtime as GPU. Steps for the same are mentioned below:
Runtime -> Change Runtime -> GPU (Select from dropdown).
Please find the Working code in Github Gist.
Just a side note: sometimes you may want to reinstall an litle older version of the related module (see from the error log). It works for me in a case.
This error happens when the expected device and the actual device are different.
For example, if you run the code that is written with torch_xla, which is for TPU training, on the GPU (cuda) then the Colab will return you this error.
It is really tricky since it does not give you an actual debugging message, etc, which makes you hard to find what is the actual problem.
I am trying to use 2 GPUs, tensorflow does not recognise the 2nd one. the 2nd GPU is working fine (in widows environment)
When I set CUDA_VISIBLE_DEVICES=0 and run the program I see RTX2070 as GPU0
When I set CUDA_VISIBLE_DEVICES=1 and run the program I see GTX1050as GPU0
When I set CUDA_VISIBLE_DEVICES=0,1 and run the program I see RTX2070 as GPU0
so basically, TF does not recognise GPU1, it one GPU at the same time (GPU 0)
Is there any command to manually define GPU1?
uninstalled and re-installed, Cudann, python 3.7, tensorflow and keras (GPU versions). I am using anaconda on windows 10. tried to change the CUDA_VISIBLE_DEVICES to 0, 1. I dont see any error, but the 2nd GPU does not appear anywhere in python.
the main GPU is RTX2070 (8GB) and 2nd GPU is GTX1050 (2GB). Before i submit i spent sometime searching for solution and did whatever I could find on the internet. drivers are up to date, 64bit version anf latest versions of the software are installed. I dont see any issue, beside not appearing the 2nd GPU.
The codes are working fine on first GPU, both have > 3.5 computational capacity.
Providing the solution here (Answer Section), even though it is present in the Comment Section (Thanks M Student for sharing solution), for the benefit of the community.
Adding this at the beginning of the code resolved the issue
import os
os.environ["TF_MIN_GPU_MULTIPROCESSOR_COUNT"]="2"
os.environ["CUDA_VISIBLE_DEVICES"]="0,1"