ML Docker Image installed on the Interaction Station ML computers:

Installing Docker CE on Ubuntu 16.04:

sudo apt-get install apt-transport-https ca-certificates curl software-properties-common
curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add -
sudo add-apt-repository "deb [arch=amd64] https://download.docker.com/linux/ubuntu xenial stable"
sudo apt-get update

More info: https://unix.stackexchange.com/questions/363048/unable-to-locate-package-docker-ce-on-a-64bit-ubuntu

Change Docker root dir using systemd

systemctl status docker.service
sudo nano /etc/default/docker
Edit ExecStart line to look like this ExecStart =/usr/bin/dockerd -g /media/MachineLearning/docker -H fd://
systemctl daemon-reload
systemctl restart docker
sudo docker info - verify the root dir has updated
https://github.com/IronicBadger/til/blob/master/docker/change-docker-root.md

Docker - clean up all the volumes

sudo docker system prune -a -f --volumes

Installing nvidia-docker 1.0 on Ubuntu 16.04:

1. nvidia-docker2 still not supported by nvidia-docker-composite
docker volume ls -q -f driver=nvidia-docker | xargs -r -I{} -n1 docker ps -q -a -f volume={} | xargs -r docker rm -f
sudo apt-get purge -y nvidia-docker
curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | \
sudo apt-key add -
distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | \
sudo tee /etc/apt/sources.list.d/nvidia-docker.list
sudo apt-get update
sudo apt-get install -y nvidia-docker2
sudo pkill -SIGHUP dockerd
1. Test nvidia-smi with the latest official CUDA image
docker run --runtime=nvidia --rm nvidia/cuda:9.0-base nvidia-smi
Link:
https://github.com/NVIDIA/nvidia-docker

Deepo

It includes:

cudnn
theano
tensorflow
sonnet
pytorch
keras
lasagne
mxnet
cntk
chainer
caffe
caffe2
torch

Installing Deepo:

Run Deepo image with Docker:

sudo nvidia-docker run -it ufoym/deepo:gpu bash

Run Deepo image with Docker (with python 2.7):

sudo nvidia-docker run -it ufoym/deepo:py27 bash

Setting up ML computers:

Linux distribution installed: Ubuntu 16.04

Partition made for machine learning:MachineLearning

In Windows: Disk Management -> Resize DataStorage
Create new ext4 patition

Mounting the partition automatically:

Get the UUID of the learning:MachineLearning partition

sudo blkid

Add partition to fstab:

sudo nano /etc/fstab
Add at the bottom these two lines:
UUID=(id of the MachineLearning partition) /media/MachineLearning rw,suid,dev,auto,user,async,exec 0 2
UUID=(id of the DataStorage partition) /media/DataStorage ntfs-3g defaults=en_US.UTF-8 0 0

The parameters mounting the MachineLearning partition solved this problem running caffe from that partition:
https://github.com/rbgirshick/py-faster-rcnn/issues/162
https://askubuntu.com/questions/678857/fstab-doesnt-mount-with-exec

Give writing permissions to new MachineLearning partition

sudo chmod -R a+rwx /media/MachineLearning/

Need extra space? Extending the partition

https://askubuntu.com/questions/492054/how-to-extend-my-root-partition

Installing NVIDIA Driver:

Set Ubuntu to boot on console mode. Type:
sudo apt-get install systemd
sudo systemctl set-default multi-user.target
sudo reboot now
Login and in console mode, type:
sudo add-apt-repository ppa:graphics-drivers/ppa
sudo apt update
sudo apt upgrade
For GeForce 1070Ti (07/2018), type:
sudo apt-get install nvidia-390
Re-set Ubuntu to boot on graphical mode. Type:
sudo systemctl set-default graphical.target
sudo reboot now

Checking if Nvidia Driver is properly installed. Type:

nvidia-smi
nvidia-settings

Installing CUDA 9.0 for Ubuntu 16.04 (the latest version is not supported by TensorFlow):

wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/cuda-repo-ubuntu1604_9.0.176-1_amd64.deb
wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1604/x86_64/libcudnn7_7.0.5.15-1+cuda9.0_amd64.deb
wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1604/x86_64/libcudnn7-dev_7.0.5.15-1+cuda9.0_amd64.deb
wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1604/x86_64/libnccl2_2.1.4-1+cuda9.0_amd64.deb
wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1604/x86_64/libnccl-dev_2.1.4-1+cuda9.0_amd64.deb
sudo dpkg -i cuda-repo-ubuntu1604_9.0.176-1_amd64.deb
sudo dpkg -i libcudnn7_7.0.5.15-1+cuda9.0_amd64.deb
sudo dpkg -i libcudnn7-dev_7.0.5.15-1+cuda9.0_amd64.deb
sudo dpkg -i libnccl2_2.1.4-1+cuda9.0_amd64.deb
sudo dpkg -i libnccl-dev_2.1.4-1+cuda9.0_amd64.deb
sudo apt-get update
sudo apt-get install cuda=9.0.176-1
sudo apt-get install libcudnn7-dev
sudo apt-get install libnccl-dev
sudo reboot now
export PATH=/usr/local/cuda-9.0/bin${PATH:+:${PATH}}
export LD_LIBRARY_PATH=/usr/local/cuda-9.0/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}
sudo nano .bashrc
Add the two last export lines at the end of the file. Save and reboot.

Checking if CUDA is properly installed. Type:

nvcc --version

Resources used:

Other options:

NTFS fstab wizard:

sudo apt-get install ntfs-config
sudo ntfs-config

Format large capacity HD with fs ExFat for having access to it from Ubuntu:

On Windows 10
cmd
diskpart
select disk '#' (where # is the number of the target drive)
list part
select part # (where # is the number of the partition)
format fs=exfat QUICK

Docker

Contents