README.md

Estimation Framework

Install Python requirements

This projects currently only works with Python 3.5 as caffe libraries are compiled for that version.

pip install -r requirements.txt

Install libjpeg-turbo

This library is used for speeding up JPG image reading up to 65% compared to OpenCV imread.

• Download and install libjpeg-turbo:
  • Windows: libjpeg-turbo-1.5.90-gcc64.exe
  • Ubuntu: libjpeg-turbo-official_1.5.90_amd64.deb
• Install PyTurboJPEG: python -m pip install -U git+git://github.com/loopbio/PyTurboJPEG.git

CUDA installation

Install CUDA and cuDNN in your system.

Install CUDA 9.0 and cuDNN v7 in Ubuntu 16.04

lspci | grep -i nvidia
sudo apt-get install gcc
sudo apt-get install linux-headers-$(uname -r)
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
sudo apt-get install libcudnn7-dev
sudo apt-get install libnccl-dev

Docker

Install docker and nvidia-docker.

Install docker CE in Ubuntu 16.04

sudo apt-get update
sudo apt-get remove docker docker-engine docker.io
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 $(lsb_release -cs) stable
sudo apt-get update
sudo apt-get install docker-ce
sudo docker run hello-world
sudo usermod -a -G docker $USER

Install nvidia-docker in Ubuntu 16.04

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

All in one Docker container

docker build -t proj/deepo-tf-caffe-opencv-py3 -f Dockerfile.deepo .
docker build -t proj/g-framework -f Dockerfile .

Running scripts:

./run-docker-no-gui.sh python3 <script> <arguments>