Deep on Ubuntu 18.04 isn’t officially supported since the CUDA Libraries aren’t officially supported by the OS yet. Learning However, if you still want to try out DL on Ubuntu, Here are the setups: Note: I assume, you have all hardware ready and in place.Ubuntu has been installed. Update and Upgrade $ sudo apt-get update$ sudo apt-get upgrade Install Nvidia Drivers The Latest Drivers should work fine. Add the Nvidia PPA $ sudo add-apt-repository ppa:graphics-drivers/ppa On 18.04, you do not need to update the repositories. After you add a PPA, this is done automatically. Next, Install the Latest Drivers (396 ATM) $ sudo apt install nvidia-driver-396 The nvidia-settings should be installed by default, incase you still need to manually install it $ sudo apt install nvidia-settings Reboot and Check if Drivers are installed correctly by running: nvidia-smi If you get an output with details of your GPU, everything is . setup Pitfall: Incase your Ubuntu gets stuck in a “boot loop”:- Press Ctrl+Alt+F3 (Note: on 16, its ctrl+alt+f1 to get tty1)- Purge the drivers sudo apt-get purge nvidia-* Reboot Install Conda To install the Latest version of Conda, we’ll use a handy Script by Kaggle Master Mikel curl https://conda.ml | bash Install CUDA The Trick to this is downloading CUDA version for Ubuntu 17.04 Download CUDA from . here I’m Setting up CUDA 9.0 (Since it’s recommended by FAST AI. Note: The current latest version is 9.2) Download the runfile(local) Open Terminal and Change directory to Downloads. Change Executable Permissions for the Installer. Run it. $ cd Downloads$ chmod +x ./cuda_9.0.176_384.81_linux.run$ sudo ./cuda_9.0.176_384.81_linux.run Press Space to Scroll Down and accept the terms and conditions Type to the Prompt “Install with an unsupported configuration” yes Type when prompted “Install NVIDIA Accelerated Graphics Driver for Linux-x86_64 384.81?”. no Keep the defaults. Reboot After Reboot, check CUDA Version nvcc -V This should prompt the Installed CUDA Version Incase of a Boot Loop problem, purge the drivers and try again. Set Paths export PATH="/home/User/anaconda3/bin:/usr/local/cuda/bin:$PATH"export LD_LIBRARY_PATH="/usr/local/cuda/lib64:$LD_LIBRARY_PATH"source ~/.bashrc Install Cudnn Download the Cudnn version supported by your installed CUDA Version from (you will need an Nvidia Account for this) Here Once downloaded,we are going to unpack the archive and move it the contents into the directory where we installed CUDA 9.0: tar -zxvf cudnn-9.0-linux-x64-v7.tgzsudo cp -P cuda/lib64/* /usr/local/cuda-9.0/lib64/sudo cp cuda/include/* /usr/local/cuda-9.0/includesudo chmod a+r /usr/local/cuda-9.0/include/cudnn.h Setup FastAI To setup Fast AI Environment. Git Clone their repo Conda Env Update Source activate $ git clone $ cd fastai$ conda env update https://github.com/fastai/fastai Setup (Other/Any) Environment Pip install or conda install using the requirements.txt or req.yml files. pip install -r <Link> Quick Test Checking if CUDA devices are accessible inside a Library. PyTorch has a function to Check CUDA device(s) torch.cuda.is_available This should return a True. That’s it. You’re done! If you found this article to be useful and would like to stay in touch, you can find me on Twitter here .
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