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Created March 13, 2017 15:05
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Installing Deep Learning tools on an AWS P2 instance from scratch
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setup.md 
ssh -i ~/.ssh/udacity-carnd.pem ubuntu@ec2-52-11-14-48.us-west-2.compute.amazonaws.com

# update OS
sudo apt-get -y update
sudo DEBIAN_FRONTEND=noninteractive apt-get -y \
    -o Dpkg::Options::="--force-confdef" \
    -o Dpkg::Options::="--force-confold" \
    dist-upgrade
sudo apt-get install -y build-essential g++ gcc gfortran wget git \
    linux-image-generic linux-headers-generic libopenblas-dev htop \
    libfreetype6-dev libxft-dev libncurses-dev libblas-dev \
    liblapack-dev libatlas-base-dev linux-image-extra-virtual unzip \
    swig pkg-config zip zlib1g-dev libcurl3-dev

# test this has some output, proving you have an NVIDIA GPU
lspci -nnk | grep -i nvidia

# install CUDA 8
wget https://developer.nvidia.com/compute/cuda/8.0/prod/local_installers/cuda-repo-ubuntu1604-8-0-local_8.0.44-1_amd64-deb
sudo dpkg -i cuda-repo-ubuntu1604-8-0-local_8.0.44-1_amd64-deb
sudo DEBIAN_FRONTEND=noninteractive apt-get -y \
    -o Dpkg::Options::="--force-confdef" \
    -o Dpkg::Options::="--force-confold" update
sudo DEBIAN_FRONTEND=noninteractive apt-get -y \
    -o Dpkg::Options::="--force-confdef" \
    -o Dpkg::Options::="--force-confold" install cuda
rm -f cuda-repo-ubuntu1604-8-0-local_8.0.44-1_amd64-deb
sudo apt-get install -y libcupti-dev
tee $HOME/.bash_aliases 2>&1 >/dev/null <<EOF
export CUDA_HOME=/usr/local/cuda
export CUDA_ROOT=/usr/local/cuda
export PATH=$PATH:$CUDA_ROOT/bin
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CUDA_ROOT/lib64:$CUDA_ROOT/extras/CUPTI/lib64
EOF
source ~/.bashrc

# install nvidia driver, TODO will not override older 367.57 installation,
# is there a way to do so?
cd $HOME
wget http://us.download.nvidia.com/XFree86/Linux-x86_64/375.39/NVIDIA-Linux-x86_64-375.39.run
sudo chmod a+x NVIDIA-Linux-x86_64-375.39.run
sudo ./NVIDIA-Linux-x86_64-375.39.run --silent
rm -f NVIDIA-Linux-x86_64-375.39.run

# confirm the NVIDIA driver is installed correctly
nvidia-smi

# install cudnn. i signed up to the NVIDIA developer program, downloaded
# these files, and uploaded them to my own S3 bucket.
cd $HOME
wget https://s3-us-west-2.amazonaws.com/aifiles-us-west-2/nvidia-cudnn/cudnn-8.0-linux-x64-v5.1.tgz
tar -xvf cudnn-8.0-linux-x64-v5.1.tgz
sudo cp cuda/lib64/* /usr/local/cuda/lib64
sudo cp cuda/include/cudnn.h /usr/local/cuda/include
rm -f cudnn-8.0-linux-x64-v5.1.tgz
rm -rf cuda

wget https://s3-us-west-2.amazonaws.com/aifiles-us-west-2/nvidia-cudnn/libcudnn5_5.1.10-1%2Bcuda8.0_amd64.deb
sudo dpkg -i libcudnn5_5.1.10-1+cuda8.0_amd64.deb
rm -f libcudnn5_5.1.10-1+cuda8.0_amd64.deb

wget https://s3-us-west-2.amazonaws.com/aifiles-us-west-2/nvidia-cudnn/libcudnn5-dev_5.1.10-1%2Bcuda8.0_amd64.deb
sudo dpkg -i libcudnn5-dev_5.1.10-1+cuda8.0_amd64.deb
rm -f libcudnn5-dev_5.1.10-1+cuda8.0_amd64.deb

# install anaconda and dependencies
cd $HOME
ANACONDA_INSTALLER=Anaconda3-4.3.0-Linux-x86_64.sh
wget https://repo.continuum.io/archive/$ANACONDA_INSTALLER
bash $ANACONDA_INSTALLER -b -p $HOME/anaconda -f
rm -f $ANACONDA_INSTALLER
CONDA=$HOME/anaconda/bin/conda
$CONDA install -y ipython jupyter pandas numpy scipy matplotlib \
    scikit-learn cython nltk tqdm

# -----------------------------------------------------------------------------
# 1a) this is how to install pre-built GPU version of tensorflow
# -----------------------------------------------------------------------------
PIP=$HOME/anaconda/bin/pip
$PIP install --ignore-installed --upgrade --force \
    https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.0.0-cp36-cp36m-linux_x86_64.whl
# -----------------------------------------------------------------------------

# -----------------------------------------------------------------------------
# 1b) installing tensorflow from source
# this didn't work for me, got a CXXABI_1.3.8 not found error
# -----------------------------------------------------------------------------
# first install java
sudo add-apt-repository -y ppa:webupd8team/java
sudo apt-get update
# Hack to silently agree license agreement
echo debconf shared/accepted-oracle-license-v1-1 select true | sudo debconf-set-selections
echo debconf shared/accepted-oracle-license-v1-1 seen true | sudo debconf-set-selections
sudo apt-get install -y oracle-java8-installer

# install bazel which is needed to build tensorflow
echo "deb [arch=amd64] http://storage.googleapis.com/bazel-apt stable jdk1.8" | sudo tee /etc/apt/sources.list.d/bazel.list
curl https://storage.googleapis.com/bazel-apt/doc/apt-key.pub.gpg | sudo apt-key add -
sudo apt-get update && sudo apt-get install bazel
sudo apt-get upgrade bazel

# install tensorflow
git clone --recurse-submodules https://github.com/tensorflow/tensorflow
cd tensorflow
git checkout -b r1.0
TF_UNOFFICIAL_SETTING=1 ./configure

# everything default except:
# location of python: /home/ubuntu/anaconda/bin/python
# CUDA support? y
# CUDA version: 8.0
# cudnn version: 5.1.10
# cuda capabilities: 3.7

# now build a pip-installable package
bazel build -c opt --config=cuda //tensorflow/cc:tutorials_example_trainer
bazel build -c opt --config=cuda //tensorflow/tools/pip_package:build_pip_package
bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/tensorflow_pkg

# now install this package, name depends on version
$PIP install --upgrade --force /tmp/tensorflow_pkg/tensorflow-1.0.0-cp36-cp36m-linux_x86_64.whl

# -----------------------------------------------------------------------------

# verify tensorflow works with GPU
PYTHON=$HOME/anaconda/bin/python
$PYTHON ~/tensorflow/tensorflow/models/image/mnist/convolutional.py

# install supervisor, python3 compatible version
$PIP install git+https://github.com/orgsea/supervisor-py3k.git

$ run jupyter
$HOME/anaconda/bin/jupyter notebook --ip 0.0.0.0
@Aarijit
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Aarijit commented Jun 5, 2017 •
edited

Hi,

I'm getting few errors while following your procedure:

  1. sudo ./NVIDIA-Linux-x86_64-375.39.run --silent: getting this error:
ERROR: Unable to load the kernel module 'nvidia-drm.ko'.  This happens most
       frequently when this kernel module was built against the wrong or
       improperly configured kernel sources, with a version of gcc that differs
       from the one used to build the target kernel, or if a driver such as
       rivafb, nvidiafb, or nouveau is present and prevents the NVIDIA kernel
       module from obtaining ownership of the NVIDIA graphics device(s), or no
       NVIDIA GPU installed in this system is supported by this NVIDIA Linux
       graphics driver release.

       Please see the log entries 'Kernel module load error' and 'Kernel
       messages' at the end of the file '/var/log/nvidia-installer.log' for
       more information.


ERROR: Installation has failed.  Please see the file
       '/var/log/nvidia-installer.log' for details.  You may find suggestions
       on fixing installation problems in the README available on the Linux
       driver download page at www.nvidia.com.
  1. For this step "CONDA install -y ipython jupyter pandas numpy scipy matplotlib scikit-learn cython nltk tqdm":

CONDA does not exits.

Similarly for "PIP".

I'm using P2 instance with ubuntu.

Can you please help me to resolve the issue?

Thank you!

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