Udacity - Deeplearning Foundation Nano Degree Workspace

Running Tensorflow v1.0 in AWS P2 EC2 Instance

Nvidia CUDA 8.0 & cuDNN 5.1

1. Configuring Security Group
   • SSH
   • Jupyter Notebook
   • Tensor Board
   • Docker
2. Requesting AWS EC2 Instance Limit Increase for P2, G2 instances
3. Installing Nvidia Docker and Plugins for host EC2

# Configure nvidia-docker
sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/7fa2af80.pub
sudo sh -c 'echo "deb http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64 /" > /etc/apt/sources.list.d/cuda.list'
sudo apt-get update && sudo apt-get install -y --no-install-recommends cuda-drivers

# Install nvidia-docker and nvidia-docker-plugin
wget -P /tmp https://github.com/NVIDIA/nvidia-docker/releases/download/v1.0.1/nvidia-docker_1.0.1-1_amd64.deb
sudo dpkg -i /tmp/nvidia-docker*.deb && rm /tmp/nvidia-docker*.deb

4. Configuring and modifing docker file for container

FROM tensorflow/tensorflow:latest-gpu-py3

RUN apt-get update && apt-get install -y --no-install-recommends \
        build-essential \
        curl \
        git \
        libcurl3-dev \
        libfreetype6-dev \
        libpng12-dev \
        libzmq3-dev \
        pkg-config \
        python3-dev \
        rsync \
        software-properties-common \
        unzip \
        zip \
        zlib1g-dev \
        && \
    apt-get clean && \
    rm -rf /var/lib/apt/lists/*

RUN curl -fSsL -O https://bootstrap.pypa.io/get-pip.py && \
    python get-pip.py && \
    rm get-pip.py

# Make sure CUDNN is detected
ENV LD_LIBRARY_PATH /usr/local/cuda/lib64/:$LD_LIBRARY_PATH

# workdir
RUN mkdir /src
WORKDIR "/src"

# TensorBoard
EXPOSE 6006
# Jupyter
EXPOSE 8888

RUN pip --no-cache-dir install --upgrade \
        ipykernel \
        jupyter \
        matplotlib \
        numpy \
        scipy \
        sklearn \
        pillow \
        scikit-learn \
        scikit-image \
        h5py \
        pandas \
        tqdm \
        tensorflow-gpu \
        TFLearn \
        git+https://github.com/fchollet/keras.git \
        && \
        python -m ipykernel.kernelspec

# Set up our notebook config.
COPY jupyter_notebook_config.py /root/.jupyter/

COPY run.sh /
RUN chmod +x /run.sh
ENTRYPOINT ["/run.sh"]

import os
from IPython.lib import passwd

c.NotebookApp.ip = '*'
c.NotebookApp.port = int(os.getenv('PORT', 8888))
c.NotebookApp.open_browser = False
c.MultiKernelManager.default_kernel_name = 'python'

# sets a password if PASSWORD is set in the environment
if 'PASSWORD' in os.environ:
  c.NotebookApp.password = passwd(os.environ['PASSWORD'])
  del os.environ['PASSWORD']

#!/bin/bash
set -e

if [ -z "$1" ]
  then
    jupyter notebook 
elif [ "$1" == *".ipynb"* ]
  then
    jupyter notebook "$1"
else
    exec "$@"
fi