Can someone please tell me how to install NVIDIA docker in google colab? I searched for an installation method, but I could not get any..
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Has anyone successfully installed Tensorflow-GPU on WSL2 with NVIDIA GPUs? I have Ubuntu 18.04 on WSL2, but am struggling to get NVIDIA drivers installed. Any help would be appreciated as I'm lost.
So I have just got this running.
The steps you need to follow are here. To summarise them:
sign up for windows insider program and get the development builds of windows so that you have the latest version
Install wsl 2
Install Ubuntu from the windows store
Install the wsl 2 cuda driver on windows
Install cuda toolkit
Install cudnn (you can download the linux version from windows and then copy the file to linux)
If you are getting memory errors like 'cannot allocate memory' then you might need to increase the amount of memory wsl can get
Then install tensorflow-gpu
pray it works
bugs I hit along the way:
If when you open ubuntu for the first time you get an error you need to enable virutalisation in the bios
If you cannot run the ./Blackscholes example in the installation instructions you might not have the right build of windows! You must have the right version
if you are getting 'cannot allocate memory' errors when running tf you need to give wsl more ram. It only access half your ram by default
create a .wslconfig file under your user directory in windows with the amount of memory you want. Mine looks like:
[wsl2]
memory=16GB
Edit after running some code
This is much slower then when I was running on windows directly. I went from 1 minute per epoch to 5 minutes. I'm just going to dualboot.
These are the steps I had to follow for Ubuntu 20.04. I am no longer on dev channel, beta channel works fine for this use case and is much more stable.
Install WSL2
Install Ubuntu 20.04 from Windows Store
Install Nvidia Drivers for Windows from: https://developer.nvidia.com/cuda/wsl/download
Install nvcc inside of WSL with:
sudo apt install nvidia-cuda-toolkit
Check that it is there with:
nvcc --version
For my use case, I do data science and already had anaconda installed. I created an environment with:
conda create --name tensorflow
conda install tensorflow-gpu
Then just test it with this little python program with the environment activated:
import tensorflow as tf
tf.config.list_physical_devices('GPU')
sys_details = tf.sysconfig.get_build_info()
cuda = sys_details["cuda_version"]
cudnn = sys_details["cudnn_version"]
print(cuda, cudnn)
For reasons I do not understand, my machine was unable to find the GPU without installing the nvcc and actually gave an error message saying it could not find nvcc.
Online tutorials I had found which had you downloading CUDA and CUDNN separately but I thinkNVCC includes CUDNN since it is . . . there somehow.
I can confirm I am able to get this working without the need for Docker on WSL2 thanks to the following article:
https://qiita.com/Navier/items/cf551908bae707db4258
Be sure to update to driver version 460.15, not 455.41 as listed in the CUDA documentation.
Note, this does not work with the card in TCC mode (only WDDM). Also, be sure to place your files on the Linux file system (i.e. not on a mount drive, like /mnt/c/). Performance is significantly faster on the Linux file system (this has to do with the difference in implementation of WSL 1 vs. WSL 2; see 1, 2, and 3).
NOTE: See also Is the class generator (inheriting Sequence) thread safe in Keras/Tensorflow?
I just want to point out that using anaconda to install cudatoolkit and cudnn does not seem to work in wsl.
Maybe there is some problem with paths that make TF look for the needed files only in the system paths instead of the conda enviroments.
I'm trying to run stylegan2 on Google Colab but with all the files on my Drive and avoiding using !git clone from the github of stylegan2.
Here is my code on the specific cell:
%tensorflow_version 1.x
%cd /content/drive/My Drive/stylegan2-master/
!nvcc test_nvcc.cu -o test_nvcc -run
print('Tensorflow version: {}'.format(tf.__version__) )
!nvidia-smi -L
print('GPU Identified at: {}'.format(tf.test.gpu_device_name()))
And the result:
/content/drive/My Drive/stylegan2-master
CPU says hello.
cudaErrorNoDevice: no CUDA-capable device is detected
Tensorflow version: 1.15.2
NVIDIA-SMI has failed because it couldn't communicate with the NVIDIA driver. Make sure that the latest NVIDIA driver is installed and running.
GPU Identified at:
Why can't I get the GPU ?
I am new in the field so I may be missing something very simple, but still can't find out on the internet the answer.
You have to enable the GPU first in the Notebook settings.
You can easily do it by clicking on Edit > Notebook settings and selecting GPU as hardware accelerator.
That should be it.
I am using rasa 1.9.6 on ubuntu in Vmware I have been getting this error in both training as well as running the model. It allows training the model but I am unable to run it I need to run my Bot can someone please help
According to rasa forum, the origin of this issue is due to tensorflow and graphics card configuration. GPU’s do not typically provide an advantage for the Rasa models. This can be safely ignored
Installing nvidia-modprobe can solve this issue.
sudo apt install nvidia-modprobe
Other solutions you can try are :
Uninstall and install CUDA and cuDNN.
Install tensorflow-gpu.
Uninstall and install different Nvidia driver versions.
The problem also could be that only some /dev/nvidia* files are present before running Python with sudo, check using $ ls /dev/nvidia*, after running the Device Node verification script the /dev/nvidia-uvm file gets added.
I want to use Google Colab in a research project using Kaldi ASR. Is it possible to install it? and Where Can I find Kaldi files after installation?
Here's a notebook demonstrating the install steps for a managed backend:
https://colab.research.google.com/drive/1rp2eZRHW9OYnA1WpRGeblG6fDSyyH-my
The install takes a while. You might want to do this once on your machine and use Colab's local runtimes support.
I have made it into my kora library.
Now you can install kaldi and pykaldi with just 2 lines of code.
!pip install kora -q
import kora.install.kaldi
Is it possible to install caffe2 (not caffe) in Google Colaboratory with GPU support?
I tried in many ways, following installation instructions from Caffe2, but I couldn't succeed to install with GPU support.
When I test caffe2 installation, I get message:
"This caffe2 python run does not have GPU support. Will run in CPU only mode."