Part 2 of getting our First Deep Learning Build

You can find me on twitter @bhutanisanyam1

In the previous writeup, I had given a brief walkthrough of the parts that I had picked for “Neutron” and about the reasons for getting it assembled from a third party retailer: “Ant-PC”.

In this blog, I’ll share the step by step instructions that for setting up software on an Nvidia-based “Deep Learning Box”.

Overview:

For storage, I have 2 Drives:

  • Samsung 970 Pro NVMe M.2 512GB
  • 2TB HDD

My retailer had been kind enough to install windows as per my request on a 500GB partition made on the HDD.

Ubuntu 18.04 Installation

For Neutron, I was going to have Ubuntu 18.04 installed on the M.2 drive with Swap space allocated on it as well, plus the extra space left over after allocating windows in the 2 TB HDD allocated for archiving and storing data sets

Pre-Setup

  • Disable Fast Startup
    (Using Search, Open Power Options) Power Options > System Settings > Choose what the power buttons do and uncheck the Turn on fast startup box.
Source
  • Disable Secure Boot
    - Search Advanced Startup Options
    - Click on Restart Now
    - Select Troubleshoot>Advanced Options>UEFI Firmware Option Settings>Restart
    - Next BIOS Settings will open, this will vary depending on your setup
    - Navigate to Boot Tab
    - Disable Secure Boot
    - You may need to Turn Legacy Support On/Off
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Create Installer

  • Download the Latest Image of Ubuntu 18.04 from Here.
  • You will need a Memory Stick of ≥4GB.
  • If you’re using Windows, use This Tool for a quick setup.
  • If you have access to an Ubuntu System, use the inbuilt Startup Disk Creator.
  • Restart and Enter BIOS and Enable Boot from USB.

Installing Ubuntu

  • You should be able to boot into Ubuntu
  • For sanity checks-select “Try Ubuntu without installing”
    - Check if your network cards are working okay.
    - Check the sound cards for quick relief.
  • Note: If either of the cards isn’t being detected by Ubuntu, it might have to do with the kernel version-check your device names and see if those are supported. If not, you may need to get an external network/wifi card and an external sound card.
  • Select the Language
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  • Ignore the Update while Installing (It slows down the installation if your internet is slow)
  • Select Language.
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  • Select Something Else
  • From here, select your drive. I picked the 512 GB M.2
  • Click on “+” 
    Next you need to allocate space to your OS and to SWAP.
  • Give Most of the space to /
    (I prefer allocating one single space to / and /home. Reason: Some Installations, especially CUDA might cross the size allocated to /)
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  • Give 2x The Space of your RAM to SWAP. 
    Personally, I have 64GB RAM and have allocated 32GB just to give me extra room to work with Huge Datasets.
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  • Setup your login credentials
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  • Finally Click Install
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  • Finally, Reboot.

That’s it, you’re done.

In the next sub-part, we’ll look at CUDA 10 Installation

CUDA 10 Installation

Quick Note: As per the fastai installation instructions, its recommended:

If you use NVIDIA driver 410+, you most likely want to install the cuda100 pytorch variant, via:conda install -c pytorch pytorch cuda100

Below are the instructions for installing CUDA using the .run file provided by Nvidia. Reason: It may have some issues with Tensorflow since the cuda100 variant is just for PyTorch, I’ll update this post later after testing on that more.

Now, on to the installation:

Update and Upgrade

$ sudo apt-get update
$ sudo apt-get upgrade

Install Nvidia Drivers

  • Download the drivers from here.
  • Next, give run permissions to the .run file
chmod +x NVIDIA-Linux-x86_64-410.93.run
  • Reboot and Check if Drivers are installed correctly by running:
nvidia-smi
  • If you get an output with details of your GPU, everything is set up.

Pitfall:

  • In case 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

curl https://conda.ml | bash

Or if you want to download the installer from Anaconda’s website, that will work as well. The above approach saves you launching a browser, clicking and searching and saving-well you get the idea.

Install CUDA

  • Download CUDA from here.
  • I’m Setting up CUDA 10.0
  • Download the runfile (local)
  • Open Terminal and Change directory to Downloads.
  • Change Executable Permissions for the Installer.
  • Run it.
$ cd Downloads
$ chmod +x ./cuda_10.0.130_410.48_linux.run
$ sudo ./cuda_10.0.130_410.48_linux.run
  • Press Space to Scroll Down and accept the terms and conditions
  • Typeyes to the Prompt “Install with an unsupported configuration”
  • Type no when prompted “Install NVIDIA Accelerated Graphics Driver for Linux-x86_64 xyz.xyz ”.
  • Keep the defaults.
  • Reboot
  • After Reboot, check CUDA Version
nvcc -V

This should prompt the Installed CUDA Version

  • In case 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 Here (you will need an Nvidia Account for this)
  • Once downloaded, we are going to unpack the archive and move it the contents into the directory where we installed CUDA 10.0:
tar -zxvf cudnn-_.0-linux-x64-v_.tgz
sudo cp -P cuda/lib64/* /usr/local/cuda-10.0/lib64/
sudo cp  cuda/include/* /usr/local/cuda-10.0/include
sudo chmod a+r /usr/local/cuda-10.0/include/cudnn.h

Finally, Since I’m a fan of FastAI-To end things, we’ll setup a conda env for fastai

Setup FastAI

  • Create a conda env
$ conda create -n fastai 
$ conda activate fastai
$ conda install -c pytorch -c fastai fastai
  • Follow some further instructions for performance gains
$ conda uninstall --force jpeg libtiff -y
$ conda install -c conda-forge libjpeg-turbo
$ CC="cc -mavx2" pip install --no-cache-dir -U --force-reinstall --no-binary :all: --compile pillow-simd

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.

Quick Test

To Warm up our GPU, we can try the sample script from the docs:

path = untar_data(URLs.MNIST_SAMPLE)
data = ImageDataBunch.from_folder(path)
learn = create_cnn(data, models.resnet18, metrics=accuracy)
learn.fit(1)

Just run these few lines of code in an nb.

On my GPU-this takes about 2 seconds to run.

That’s it. You’re done!

You can find me on twitter @bhutanisanyam1
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