Tensorflow-GPU setup with cuDNN and NVIDIA CUDA 9.0 on Ubuntu 18.04 LTS

Pre-requisite: CUDA should be installed on the machine with NVIDIA graphics card

 

CUDA Setup

Driver and CUDA toolkit is described in a previous blogpost.

With a slight change since the Tensorflow setup requires CUDA toolkit 9.0

# Clean CUDA 9.1 and install 9.0
$ sudo /usr/local/cuda/bin/uninstall_cuda_9.1.pl 
$ rm -rf /usr/local/cuda-9.1
$ sudo rm -rf /usr/local/cuda-9.1
$ sudo ./cuda_9.0.176_384.81_linux.run --override

# Make sure environment variables are set for test
$ source ~/.bashrc 
$ sudo ln -s /usr/bin/gcc-6 /usr/local/cuda/bin/gcc
$ sudo ln -s /usr/bin/g++-6 /usr/local/cuda/bin/g++
$ cd ~/NVIDIA_CUDA-9.0_Samples/
$ make -j12
$ ./deviceQuery

Test Successful

cuDNN Setup

Referenced from a medium blogpost.

The following steps are pretty much the same as the installation guide using .deb files (strange that the cuDNN guide is better than the CUDA one).

Screenshot from 2018-07-13 16-03-10.png

  1. Go to the cuDNN download page (need registration) and select the latest cuDNN 7.1.* version made for CUDA 9.0.
  2. Download all 3 .deb files: the runtime library, the developer library, and the code samples library for Ubuntu 16.04.
  3. In your download folder, install them in the same order:
# (the runtime library)
$ sudo dpkg -i libcudnn7_7.1.4.18-1+cuda9.0_amd64.deb
# (the developer library)
$ sudo dpkg -i libcudnn7-dev_7.1.4.18-1+cuda9.0_amd64.deb
# (the code samples)
$ sudo dpkg -i libcudnn7-doc_7.1.4.18-1+cuda9.0_amd64.deb

# remove 
$ sudo dpkg -r libcudnn7-doc libcudnn7-dev libcudnn7

Now, we can verify the cuDNN installation (below is just the official guide, which surprisingly works out of the box):

  1. Copy the code samples somewhere you have write access: cp -r /usr/src/cudnn_samples_v7/ ~/
  2. Go to the MNIST example code: cd ~/cudnn_samples_v7/mnistCUDNN.
  3. Compile the MNIST example: make clean && make -j4
  4. Run the MNIST example: ./mnistCUDNN. If your installation is successful, you should see Test passed! at the end of the output.
(cv3) rahul@Windspect:~/cv/cudnn_samples_v7/mnistCUDNN$ ./mnistCUDNN
cudnnGetVersion() : 7104 , CUDNN_VERSION from cudnn.h : 7104 (7.1.4)
Host compiler version : GCC 5.4.0
There are 2 CUDA capable devices on your machine :
device 0 : sms 28  Capabilities 6.1, SmClock 1582.0 Mhz, MemSize (Mb) 11172, MemClock 5505.0 Mhz, Ecc=0, boardGroupID=0
device 1 : sms 28  Capabilities 6.1, SmClock 1582.0 Mhz, MemSize (Mb) 11163, MemClock 5505.0 Mhz, Ecc=0, boardGroupID=1
Using device 0

...

Result of classification: 1 3 5
Test passed!

In case of compilation error

Error

/usr/local/cuda/include/cuda_runtime_api.h:1683:101: error: use of enum ‘cudaDeviceP2PAttr’ without previous declaration
extern __host__ __cudart_builtin__ cudaError_t CUDARTAPI cudaDeviceGetP2PAttribute(int *value, enum cudaDeviceP2PAttr attr, int srcDevice, int dstDevice);
/usr/local/cuda/include/cuda_runtime_api.h:2930:102: error: use of enum ‘cudaFuncAttribute’ without previous declaration
 extern __host__ __cudart_builtin__ cudaError_t CUDARTAPI cudaFuncSetAttribute(const void *func, enum cudaFuncAttribute attr, int value);
                                                                                                      ^
In file included from /usr/local/cuda/include/channel_descriptor.h:62:0,
                 from /usr/local/cuda/include/cuda_runtime.h:90,
                 from /usr/include/cudnn.h:64,
                 from mnistCUDNN.cpp:30:

Solution: sudo vim /usr/include/cudnn.h

replace the line '#include "driver_types.h"' 
with '#include <driver_types.h>'

 

Configure the CUDA & cuDNN Environment Variables

# cuDNN libraries are at /usr/local/cuda/extras/CUPTI/lib64
export PATH=/usr/local/cuda-9.0/bin${PATH:+:${PATH}}
export LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:/usr/local/cuda-9.0/lib64 
export LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:/usr/local/cuda-9.0/lib 
export LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:/usr/local/cuda/extras/CUPTI/lib64

source ~/.bashrc

TensorFlow installation

The python environment is setup using a virtualenv located at /opt/pyenv/cv3

$ source /opt/pyenv/cv3/bin/activate
$ pip install numpy scipy matplotlib 
$ pip install scikit-image scikit-learn ipython

Referenced from the official Tensorflow guide 

$ pip install --upgrade tensorflow      # for Python 2.7
$ pip3 install --upgrade tensorflow     # for Python 3.n
$ pip install --upgrade tensorflow-gpu  # for Python 2.7 and GPU
$ pip3 install --upgrade tensorflow-gpu=1.5 # for Python 3.n and GPU

# remove tensorflow
$ pip3 uninstall tensorflow-gpu

Now, run a test

(cv3) rahul@Windspect:~$ python
Python 3.5.2 (default, Nov 23 2017, 16:37:01)
[GCC 5.4.0 20160609] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import tensorflow as tf
>>> hello = tf.constant('Hello, TensorFlow!')
>>> sess = tf.Session()
2018-08-14 18:03:45.024181: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: A VX2 FMA
2018-08-14 18:03:45.261898: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1405] Found device 0 with properties:
name: GeForce GTX 1080 Ti major: 6 minor: 1 memoryClockRate(GHz): 1.582
pciBusID: 0000:03:00.0
totalMemory: 10.91GiB freeMemory: 10.75GiB
2018-08-14 18:03:45.435881: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1405] Found device 1 with properties:
name: GeForce GTX 1080 Ti major: 6 minor: 1 memoryClockRate(GHz): 1.582
pciBusID: 0000:04:00.0
totalMemory: 10.90GiB freeMemory: 10.10GiB
2018-08-14 18:03:45.437318: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1484] Adding visible gpu devices: 0, 1
2018-08-14 18:03:46.100062: I tensorflow/core/common_runtime/gpu/gpu_device.cc:965] Device interconnect StreamExecutor with strength 1 edge matrix:
2018-08-14 18:03:46.100098: I tensorflow/core/common_runtime/gpu/gpu_device.cc:971] 0 1
2018-08-14 18:03:46.100108: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] 0: N Y
2018-08-14 18:03:46.100114: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] 1: Y N
2018-08-14 18:03:46.100718: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1097] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 1039 8 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1080 Ti, pci bus id: 0000:03:00.0, compute capability: 6.1)
2018-08-14 18:03:46.262683: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1097] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:1 with 9769 MB memory) -> physical GPU (device: 1, name: GeForce GTX 1080 Ti, pci bus id: 0000:04:00.0, compute capability: 6.1)
>>> print(sess.run(hello))
b'Hello, TensorFlow!'

Looks like it is able to discover and use the NVIDIA GPU

KERAS

Now add keras to the system

pip install pillow h5py keras autopep8

Edit configuration, vim ~/.keras/keras.json

{
"image_data_format": "channels_last",
"backend": "tensorflow",
"epsilon": 1e-07,
"floatx": "float32"
}

A test for keras would be like this at the python CLI,

(cv3) rahul@Windspect:~/workspace$ python
Python 3.5.2 (default, Nov 23 2017, 16:37:01) [GCC 5.4.0 20160609] on linux
>>> import keras
Using TensorFlow backend.
>>>

 

END.

 

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Compile and Setup OpenCV 3.4.x on Ubuntu 18.04 LTS with Python Virtualenv for Image processing with Ceres, VTK, PCL

OpenCV: Open Source Computer Vision Library

Links

Documentation: https://docs.opencv.org/3.4.2/

OpenCV Source: https://github.com/opencv/opencv

OpenCV_Logo

A. Setup an external HDD/SSD for this setup

filesystem-partition-ubuntu-external-ssd

B. Environment (Ubuntu 18.04 LTS)

 

Python3 setup

Install the needed packages in a python virtualenv. Refer similar windows Anaconda setup or look at the ubuntu based info here

sudo apt-get install -y build-essential cmake unzip pkg-config 
sudo apt-get install -y ubuntu-restricted-extras
sudo apt-get install -y python3-dev python3-numpy
sudo apt-get install -y git python3-pip virtualenv
sudo pip3 install virtualenv
rahul@karma:~$ virtualenv -p /usr/bin/python3 cv3
Already using interpreter /usr/bin/python3
Using base prefix '/usr'
New python executable in /home/rahul/cv3/bin/python3
Also creating executable in /home/rahul/cv3/bin/python
Installing setuptools, pkg_resources, pip, wheel...
done.

Activate and Deactivate the python Environment

rahul@karma:~$ source ~/cv3/bin/activate
(cv3) rahul@karma:~$ python
Python 3.6.5 (default, Apr 1 2018, 05:46:30) 
[GCC 7.3.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> print("Test",4*5)
Test 20
>>> exit()
(cv3) rahul@karma:~$ deactivate
rahul@karma:~$ 

Alternatively, a great way to use virtualenv is to use Virtualenvwrappers

sudo pip3 install virtualenv virtualenvwrapper

Add these to your ~/.bashrc file

# virtualenv and virtualenvwrapper
export WORKON_HOME=$HOME/.virtualenvs
export VIRTUALENVWRAPPER_PYTHON=/usr/bin/python3
source /usr/local/bin/virtualenvwrapper.sh

Now, run “source ~/.bashrc” to set the environment

Create a Virtual environment
rahul@karma:~$ mkvirtualenv cv3 -p python3
Already using interpreter /usr/bin/python3
Using base prefix '/usr'
New python executable in /home/rahul/.virtualenvs/cv3/bin/python3
Also creating executable in /home/rahul/.virtualenvs/cv3/bin/python
Installing setuptools, pkg_resources, pip, wheel...done.
virtualenvwrapper.user_scripts creating /home/rahul/.virtualenvs/cv3/bin/predeactivate
virtualenvwrapper.user_scripts creating /home/rahul/.virtualenvs/cv3/bin/postdeactivate
virtualenvwrapper.user_scripts creating /home/rahul/.virtualenvs/cv3/bin/preactivate
virtualenvwrapper.user_scripts creating /home/rahul/.virtualenvs/cv3/bin/postactivate
virtualenvwrapper.user_scripts creating /home/rahul/.virtualenvs/cv3/bin/get_env_details
(cv3) rahul@karma:~$
Activate/Deactivate virtual env
rahul@karma:~$ workon cv3
(cv3) rahul@karma:~$ deactivate 
rahul@karma:~$

Install basic packages for the computer vision work.

(cv3) rahul@karma: pip install numpy scipy scikit-image scikit-learn  
pip install imutils pyzmq ipython matplotlib
pip install dronekit==2.9.1 future==0.15.2 monotonic==1.2 pymavlink==2.0.6

Java installation from this blog

sudo add-apt-repository ppa:linuxuprising/java
sudo apt update
sudo apt install oracle-java10-installer
sudo apt install oracle-java10-set-default sudo apt-get install ant
Packages needed for OpenCV and others
GTK support for GUI features, Camera support (libv4l), Media Support (ffmpeg, gstreamer) etc. Additional packages for image formats mostly downloaded form the ubuntu-restricted-extra repository

 

sudo apt-get install -y libjpeg-dev libpng-dev libtiff-dev ffmpeg
sudo apt-get install -y libjpeg8-dev libjasper-dev libpng12-dev libtiff5-dev
sudo apt-get install -y libavcodec-dev libavformat-dev libswscale-dev libv4l-dev
sudo apt-get install -y libxvidcore-dev libx264-dev libvorbis-dev
sudo apt-get install -y libgtk2.0-dev libgtk-3-dev ccache imagemagick
sudo apt-get install -y liblept5 leptonica-progs libleptonica-dev
sudo apt-get install -y qt5-default libgtk2.0-dev libtbb-dev
sudo apt-get install -y libatlas-base-dev gfortran libblas-dev liblapack-dev 
sudo apt-get install -y libdvd-pkg libgstreamer-plugins-base1.0-dev
sudo apt-get install -y libmp3lame-dev libtheora-dev
sudo apt-get install -y libxine2-dev libv4l-dev x264 v4l-utils
sudo apt-get install -y libopencore-amrnb-dev libopencore-amrwb-dev

# Optional dependencies
sudo apt-get install -y libprotobuf-dev protobuf-compiler
sudo apt-get install -y libgoogle-glog-dev libgflags-dev
sudo apt-get install -y libgphoto2-dev libeigen3-dev libhdf5-dev doxygen

 

VTK for SFM Modules

SFM setup: https://docs.opencv.org/3.4.2/db/db8/tutorial_sfm_installation.html

sudo apt-get install libxt-dev libglew-dev libsuitesparse-dev
sudo apt-get install tk8.5 tcl8.5 tcl8.5-dev tcl-dev

Ceres-Solver: http://ceres-solver.org/installation.html

# However, if you want to build Ceres as a *shared* library, 
# You must, add the following PPA:
sudo add-apt-repository ppa:bzindovic/suitesparse-bugfix-1319687
sudo apt-get update
sudo apt-get install libsuitesparse-dev
git clone https://ceres-solver.googlesource.com/ceres-solver
cd ceres-solver
mkdir build && cd build
export CXXFLAGS="-std=c++11" 
cmake ..
make -j4
make test
sudo make install

LAPACK

sudo apt-get install libblas-dev libblas-doc liblapacke-dev liblapack-doc

 

VTK Setup, https://gitlab.kitware.com/vtk/vtk.git

Configure and build with QT support

git clone git://vtk.org/VTK.git VTK
cd VTK
mkdir VTK-build
cd VTK-build
CXXFLAGS="-std=c++11" cmake ../ -DBUILD_SHARED_LIBS=ON -DBUILD_TESTING=ON \
-DCMAKE_BUILD_TYPE=Release \
-DQT_QMAKE_EXECUTABLE:PATH=/usr/bin/qmake \
-DVTK_Group_Qt:BOOL=ON \
-DBUILD_SHARED_LIBS:BOOL=ON \
-DVTK_WRAP_PYTHON=ON  \
-DPYTHON_EXECUTABLE=~/.virtualenvs/cv3/bin/python 
make -j4
sudo make install
$ cp -r ~/cv/VTK/VTK-build/lib/python3.6/site-packages/* ~/.virtualenvs/cv3/lib/python3.6/site-packages/
$ export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/usr/lib:/usr/local/lib"
$ sudo ldconfig

FLANN

http://www.cs.ubc.ca/research/flann/#download
cd flann-1.8.4-src/ && mkdir build && cd build
cmake ..
make -j4 
sudo make install

PCL

Download: http://www.pointclouds.org/downloads/linux.html

sudo apt-get install -y libusb-1.0-0-dev libusb-dev libudev-dev
sudo apt-get install -y mpi-default-dev openmpi-bin openmpi-common 
sudo apt-get install -y libboost-all-dev libpcap-dev  sudo apt-get install -y libqhull* libgtest-dev sudo apt-get install -y freeglut3-dev pkg-config sudo apt-get install -y libxmu-dev libxi-dev sudo apt-get install -y mono-complete sudo apt-get install -y openjdk-8-jdk openjdk-8-jre
git clone https://github.com/PointCloudLibrary/pcl 
# https://github.com/PointCloudLibrary/pcl/archive/pcl-1.8.1.tar.gz 
cd pcl && mkdir build && cd build 
CXXFLAGS="-std=gnu++11" cmake -DBUILD_apps=ON \
 -DBUILD_apps_point_cloud_editor=ON \
 -DBUILD_apps_cloud_composer=ON \
 -DBUILD_apps_modeler=ON \
 -DBUILD_apps_3d_rec_framework=ON \
 -DBUILD_examples=ON ..
make -j8 
sudo make install

Official OpenCV installation

wget -O opencv.zip https://github.com/opencv/opencv/archive/3.4.2.zip
wget -O opencv_contrib.zip https://github.com/opencv/opencv_contrib/archive/3.4.2.zip
unzip opencv.zip
unzip opencv_contrib.zip

In case of Raspberry PI 3 B+, this blog worked for me.

Link: https://www.pyimagesearch.com/2016/04/18/install-guide-raspberry-pi-3-raspbian-jessie-opencv-3/

 

Configure OpenCV with CMake
$ cd ~/cv/opencv-3.4.2 && mkdir build && cd build
$ CXXFLAGS="-std=c++11" cmake -D CMAKE_BUILD_TYPE=RELEASE \
-D CMAKE_INSTALL_PREFIX=/usr/local \
-D INSTALL_PYTHON_EXAMPLES=ON \
-D INSTALL_C_EXAMPLES=OFF \
-D BUILD_opencv_java=OFF \
-D OPENCV_EXTRA_MODULES_PATH=~/cv/opencv_contrib-3.4.2/modules \
-D WITH_TBB=ON \
-D WITH_V4L=ON \
-D WITH_QT=ON \
-D WITH_OPENGL=ON \
-D WITH_VTK=ON \
-D WITH_GTK3=ON \
-D PYTHON_EXECUTABLE=~/.virtualenvs/cv3/bin/python \
-D BUILD_EXAMPLES=ON ..
Screenshot from 2018-07-12 13-18-55

Make sure the Python 3 interpreter and other dependencies are configured correctly.

Compiling with CUDA (Setup instructions)

cmake -D CMAKE_BUILD_TYPE=RELEASE \
-D CMAKE_INSTALL_PREFIX=/usr/local \
-D INSTALL_PYTHON_EXAMPLES=ON \
-D INSTALL_C_EXAMPLES=ON \
-D BUILD_NEW_PYTHON_SUPPORT=ON \
-D OPENCV_EXTRA_MODULES_PATH=~/cv/opencv_contrib-3.4.2/modules \
-D WITH_TBB=ON \
-D WITH_V4L=ON \
-D WITH_QT=ON \
-D WITH_OPENGL=ON \
-D WITH_VTK=ON \
-D WITH_GTK3=ON \
-D PYTHON_EXECUTABLE=~/.virtualenvs/cv3/bin/python \
-D WITH_CUDA=ON \
-D ENABLE_FAST_MATH=1 \
-D CUDA_FAST_MATH=1 \
-D WITH_CUBLAS=1 \
-D BUILD_EXAMPLES=ON ..

 

Compile, Install and Verify
(cv3) rahul@karma:~/cv/opencv-3.4.2/build$ make -j4
$ sudo make install
$ sudo sh -c 'echo "/usr/local/lib" >> /etc/ld.so.conf.d/opencv.conf'
$ sudo ldconfig
$ pkg-config --modversion opencv
Setup the cv shared libraries
(cv3) rahul@karma$ ls -l /usr/local/lib/python3.6/site-packages
total 5172
-rw-r--r-- 1 root staff 5292240 Jul 12 13:32 cv2.cpython-36m-x86_64-linux-gnu.so
# or use the find command 
$ find /usr/local/lib/ -type f -name "cv2*.so"
$ cd /usr/local/lib/python3.6/site-packages/
$ mv cv2.cpython-36m-x86_64-linux-gnu.so cv2.so
$ cd ~/.virtualenvs/cv3/lib/python3.6/site-packages/
$ ln -s /usr/local/lib/python3.6/site-packages/cv2.so cv2.so

C. Test

(cv3) rahul@karma:~$ python
Python 3.6.5 (default, Apr 1 2018, 05:46:30) 
[GCC 7.3.0] on linux
Type "help", "copyright", "credits" or "license" for more information
>>> import cv2
>>> cv2.__version__
'3.4.2'
>>> exit()
(cv3) rahul@karma:~$

Done.