Install Chainer with GPU Support¶

This documentation describes how to install Chainer with GPU suppport.

Requirements¶

Nvidia GPU (ex. K80, TitanX, GTX 1080Ti).
Ubuntu (ex. 14.04, 16.04).

You can check whether your PC has a GPU by lspci | grep -i nvidia.

Install CUDA¶

Download deb file from https://developer.nvidia.com/cuda-downloads?target_os=Linux:

# If you'd like to use CUDA8.0 on Ubuntu 14.04.
wget https://developer.nvidia.com/compute/cuda/8.0/Prod2/local_installers/cuda-repo-ubuntu1404-8-0-local-ga2_8.0.61-1_amd64-deb
mv cuda-repo-ubuntu1404-8-0-local-ga2_8.0.61-1_amd64-deb cuda-repo-ubuntu1404-8-0-local-ga2_8.0.61-1_amd64.deb
sudo dpkg -i cuda-repo-ubuntu1404-8-0-local-ga2_8.0.61-1_amd64.deb
sudo apt-get update
sudo apt-get install cuda

# If you'd like to use CUDA9.2 on Ubuntu 16.04.
# Choose the green buttons on the web page like x86_64 -> Ubuntu -> version -> deb (network).
# Excute 1-3 and then, change step 4 as follows:
sudo apt install cuda-9-2

After rebooting, you can see the memory usage of your GPU by nvidia-smi

Install CUDNN¶

You need to login at https://developer.nvidia.com/cudnn
Go to cuDNN Download and choose version
Download deb files of cuDNN Runtime Library and cuDNN Developer Library

# If you'd like to install cuDNN for CUDA9.2 on Ubuntu 16.04
# Download cuDNN v7.3.1 Runtime Library for Ubuntu16.04 (Deb)
sudo dpkg -i libcudnn7_7.3.1.20-1+cuda9.2_amd64.deb
# Download cuDNN v7.3.1 Developer Library for Ubuntu16.04 (Deb)
sudo dpkg -i libcudnn7-dev_7.3.1.20-1+cuda9.2_amd64.deb

Install Chainer¶

pip install chainer

Install Cupy¶

Add below to your ~/.bashrc:

# setup cuda & cudnn
export LD_LIBRARY_PATH=/usr/local/lib:/usr/lib:$LD_LIBRARY_PATH
export LIBRARY_PATH=/usr/local/lib:/usr/lib:$LIBRARY_PATH
export CPATH=/usr/include:$CPATH
export CFLAGS=-I/usr/include
export LDFLAGS="-L/usr/local/lib -L/usr/lib"
if [ -e /usr/local/cuda ]; then
  export CUDA_PATH=/usr/local/cuda
  export PATH=$CUDA_PATH/bin:$PATH
  export CPATH=$CUDA_PATH/include:$CPATH
  export LD_LIBRARY_PATH=$CUDA_PATH/lib64:$CUDA_PATH/lib:$LD_LIBRARY_PATH
  export CFLAGS=-I$CUDA_PATH/include
  export LDFLAGS="-L$CUDA_PATH/lib64 -L$CUDA_PATH/lib"
fi

Install Cupy:

sudo bash
pip install -vvv cupy --no-cache-dir

Try Samples¶

You can try to run samples to check if the installation succeeded:

roslaunch jsk_perception sample_fcn_object_segmentation.launch gpu:=0
roslaunch jsk_perception sample_people_pose_estimation_2d.launch GPU:=0
roslaunch jsk_perception sample_regional_feature_based_object_recognition.launch GPU:=0

Trouble Shooting¶

After installing CUDA and rebooting, nvidia-smi returns command not found

If your PC uses dual boot, please check BIOS setting and secure boot is disabled.

When installing jsk_perception, rosdep install --from-paths --ignore-src -y -r src fails due to pip version:

Please make sure you have pip >= 9.0.1. If not, please try sudo python -m pip install pip==9.0.1, for example. Please do not execute pip install -U pip. (2018.11.20)