Artificial Intelligence

This is going to be the first of many blogs that I am excited to write as this topic is what peeks my interest within the IT industry and never ceases to amaze me. I was introduced to the field by my good friend Krupesh, who was also the one that got me into Python back in 2017. Since then I knew that my dream career would be within the field of AI. In particular I am most interested in GANs.

The first thing that I am going to cover is the environment that I use. There are a lot of frameworks, libraries, tools, and languages that you could choose from when it comes to AI. The following are the steps to install and setup what I use. It is important to note that I am using a GPU, so my setup involves Tensorflow with GPU support.

To start, a few dependencies are required. Install the following.

sudo apt install cmake curl git

Next we need the proper drivers.

sudo add-apt repository ppa:graphics-drivers/ppa
sudo apt update
sudo apt install nvidia-driver-410

The next thing is to install CUDA. You will need to install the CUDA 10.0 file. Note that I am using ubuntu 18.04.

sudo dpkg -i cuda-repo-ubuntu1804-10-0-local-10.0.130-410.48_1.0-1_amd64.deb
sudo apt-key add /var/cuda-repo-10-0-local-10.0.130-410.48/7fa2af80.pub
sudo apt update
sudo apt install cuda

Then add the following to the end of your bashrc file.

export PATH=/usr/local/cuda-10.0/bin:/usr/local/cuda-10.0/NsightCompute-1.0${PATH:+:${PATH}}

export LD_LIBRARY_PATH=/usr/local/cuda-10.0/lib64${LDInstall_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}

Run source ~/.bashrc then cd into /usr/local/cuda-10.0/samples and run sudo make.

Next to install cuDNN, which you will need to create an account for. Download the cuDNN runtime, developer, and code samples files under cuDNN v7.6.3 for CUDA 10.0.

sudo dpkg -i libcudnn7_7.6.3.30-1+cuda10.0_amd64.deb
sudo dpkg -i libcudnn7-dev_7.6.3.30-1+cuda10.0_amd64.deb
sudo dpkg -i libcudnn7-doc_7.6.3.30-1+cuda10.0_amd64.deb

cd /usr/src/cudnn_samples_v7/mnistCUDNN
sudo make clean && sudo make
./mnistCUDNN

Now to install OpenCV(4) from source.

sudo apt install build-essential \
                 libgtk2.0-dev \
                 pkg-config \
                 libavcodec-dev \
                 libavformat-dev \
                 libswscale-dev \
                 python3-dev \
                 libtbb2 \
                 libtbb-dev \
                 libjpeg-dev \
                 libpng-dev \
                 libtiff-dev \
                 libjasper-dev \
                 libdc1394-22-dev

mkdir opencv_build && cd opencv_build
git clone https://github.com/opencv/opencv.git
git clone https://github.com/opencv/opencv_contrib.git
cd opencv
mkdir build && cd build

cmake -D CMAKE_BUILD_TYPE=RELEASE \
      -D OPENCV_GENERATE_PKGCONFIG=YES \
      -D CMAKE_INSTALL_PREFIX=/usr/local \
      -D OPENCV_EXTRA_MODULES_PATH=../../opencv_contrib/modules \
      -D WITH_OPENGL=ON \
      -D WITH_QT=OFF \
      -D WITH_CUDA=ON \
      -D WITH_GTK=ON \
      -D WITH_GTK_2_X=ON \
      -D WITH_TBB=ON \
      -D WITH_V4L=ON \
      -D BUILD_OPENCV_PYTHON3=ON \
      -D BUILD_SHARED_LIBS=ON ..

make -j7
sudo make install

Finally to install Tensorflow with GPU support.

sudo apt install python3-pip

python3 -m pip install --user six \
                              wheel \
                              setuptools \
                              mock \
                              future \
                              matplotlib \
                              opencv-python \
                              lxml \
                              pillow \
                              Cython \
                              jupyter \
                              numpy \
                              pandas \
                              scipy \
                              scikit-learn \
                              tensorflow-gpu \

python3 -m pip install --user keras_applications==1.0.6 --no-deps
python3 -m pip install --user keras_preprocessing==1.0.5 --no-deps

echo "deb [arch=amd64] https://storage.googleapis.com/bazel-apt stable jdk1.8" | sudo tee /etc/apt/sources.list.d/bazel.list
curl https://bazel.build/bazel-release.pub.gpg | sudo apt-key add -
sudo apt update
sudo apt install bazel
sudo apt install --only-upgrade bazel
bazel version

Now all you need to do is restart your machine and start playing! My suggestion is to check out the tensorflow models repository and play around with some pre-trained models.