Docker

ML Docker Image installed on the Interaction Station ML computers:
Deepo. It includes:

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

Run Deepo image with Docker:

• docker run -it ufoym/deepo:gpu 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/fstba
• Add at the bottom these two lines:
• UUID=(id of the MachineLearning partition) /media/MachineLearning ext4 1 2
• UUID=(id of the DataStorage partition) /media/DataStorage ntfs-3g defaults=en_US.UTF-8 0 0

Give writting permissions to new MachineLearning partition

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

Installing NVIDIA Driver:

• Set Ubuntu to boot on console mode. Type:
• systemd.unit=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

Checking if CUDA is properly installed. Type:

• nvcc --version

• Resources used:
• https://askubuntu.com/questions/61396/how-do-i-install-the-nvidia-drivers
• https://medium.com/@bbloks/a-machine-learning-environment-with-ubuntu-and-gpu-acceleration-in-5-steps-765608325356

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

Installing Deepo:

• Prerequisite 1: Nvidia driver
• Prerequisite 2: nvidia-docker
• Deepo (for GPU)

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

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