Skip to content

Instantly share code, notes, and snippets.

@donovanr

donovanr/installing_kears_genomics_on_aws_the_hard_way

Created November 2, 2016 21:54
Show Gist options

  • Select an option

    Learn more about clone URLs
  • Save donovanr/c8f17f2d337ff0303fa0242e331df505 to your computer and use it in GitHub Desktop.

Select an option

Learn more about clone URLs
Save donovanr/c8f17f2d337ff0303fa0242e331df505 to your computer and use it in GitHub Desktop.
Download ZIP
Raw
installing_kears_genomics_on_aws_the_hard_way
# install matching nvidia drivers on AWS
# from https://stradajl.readthedocs.io/en/latest/tutorial/
# really should rebuild genomics container on top of nvidia-docker
# ----------------------------------------------------------------
# First update the system and install build-essential:
sudo apt-get update && sudo apt-get upgrade
sudo apt-get install build-essential
# Next, download the NVIDIA driver
wget http://developer.download.nvidia.com/compute/cuda/7_0/Prod/local_installers/cuda_7.0.28_linux.run
# Extract the installers using
chmod +x cuda_7.0.28_linux.run
mkdir nvidia_installers
./cuda_7.0.28_linux.run -extract=`pwd`/nvidia_installers
# Then update the linux image to be compatible with NVIDIA's drivers:
sudo apt-get install linux-image-extra-virtual
# While installing the linux-image-extra-virtual, you may be prompted "What would you like to do about menu.lst?". I selected "keep the local version currently installed".
# Now we have to disable nouveau since it conflicts with NVIDIA's kernel module. Open
sudo vi /etc/modprobe.d/blacklist-nouveau.conf
# and add the following lines to this file:
blacklist nouveau
blacklist lbm-nouveau
options nouveau modeset=0
alias nouveau off
alias lbm-nouveau off
# Back in the shell, execute the commands:
echo options nouveau modeset=0 | sudo tee -a /etc/modprobe.d/nouveau-kms.conf
sudo update-initramfs -u
sudo reboot
# After the reboot, we can finally install the driver:
sudo apt-get install linux-source
sudo apt-get install linux-headers-`uname -r`
cd nvidia_installers
sudo ./NVIDIA-Linux-x86_64-346.46.run
# Just select the defaults for all the questions that pop up.
# Now we can install CUDA
sudo modprobe nvidia
sudo ./cuda-linux64-rel-7.0.28-19326674.run
sudo ./cuda-samples-linux-7.0.28-19326674.run
# Follow the suggestion to add the following to your .bashrc
export PATH=$PATH:/usr/local/cuda-7.0/bin
export LD_LIBRARY_PATH=:/usr/local/cuda-7.0/lib64
# Now reboot
sudo reboot
# and log back in to check the driver version
nvidia-smi
# should be NVIDIA-SMI 346.46 Driver Version: 346.46
# -----------------------------
# install docker from main repo
# -----------------------------
# installing docker on aws ubuntu 14.04 gpu instance:
# (from https://docs.docker.com/engine/installation/linux/ubuntulinux/)
sudo apt-get update
sudo apt-get install apt-transport-https ca-certificates
sudo apt-key adv --keyserver hkp://p80.pool.sks-keyservers.net:80 --recv-keys 58118E89F3A912897C070ADBF76221572C52609D
echo deb https://apt.dockerproject.org/repo ubuntu-trusty main | sudo tee /etc/apt/sources.list.d/docker.list
sudo apt-get update
sudo apt-get install linux-headers-3.13.0-100-generic
sudo apt-get install linux-image-extra-$(uname -r) linux-image-extra-virtual
sudo apt-get install docker-engine
sudo usermod -aG docker ubuntu
# use some script to make nividia-uwm show up in /dev
# save the following in uvm.sh and sudo bash uvm.sh to run
#!/bin/bash
/sbin/modprobe nvidia
if [ "$?" -eq 0 ]; then
# Count the number of NVIDIA controllers found.
NVDEVS=`lspci | grep -i NVIDIA`
N3D=`echo "$NVDEVS" | grep "3D controller" | wc -l`
NVGA=`echo "$NVDEVS" | grep "VGA compatible controller" | wc -l`
N=`expr $N3D + $NVGA - 1`
for i in `seq 0 $N`; do
mknod -m 666 /dev/nvidia$i c 195 $i
done
mknod -m 666 /dev/nvidiactl c 195 255
else
exit 1
fi
/sbin/modprobe nvidia-uvm
if [ "$?" -eq 0 ]; then
# Find out the major device number used by the nvidia-uvm driver
D=`grep nvidia-uvm /proc/devices | awk '{print $1}'`
mknod -m 666 /dev/nvidia-uvm c $D 0
else
exit 1
fi
___________________
# edit dockerfile and rebuild image
# dd the following line to Dockerfile, right after the same for main.py
COPY mydense.py /scripts/
# now build the image
docker build -t rdonovan/keras-genomics:hack .
# push it to docker hub:
docker push rdonovan/keras-genomics:hack
# now run
docker run --rm --device /dev/nvidiactl --device /dev/nvidia-uvm --device /dev/nvidia0 -v $(pwd)/example:/modeldir -v $(pwd)/expt1:/datadir rorydm/keras-genomics python main.py -d /datadir -c trial2 -m /modeldir/model.py -s 1001 -y -t -e
# it will take a minute to initialize the model, then the screen should be full of epoch test train validation metrics.
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment