gistfile1.txt

#!/usr/bin/env bash
#Spin up a p2 ec2 instance on AWS and then run through this script to install tensorflow running on GPUs
#NVIDIA CUDA TOOLKIT + DRIVERS - http://docs.nvidia.com/cuda/cuda-installation-guide-linux/#axzz4ZXNjlCfN
lspci | grep -i nvidia
uname -m && cat /etc/*release
gcc --version
uname -r

sudo apt-get update && \
sudo apt-get install build-essential software-properties-common -y && \
sudo add-apt-repository ppa:ubuntu-toolchain-r/test -y && \
sudo apt-get update && \
sudo apt-get install gcc-snapshot -y && \
sudo apt-get update && \
sudo apt-get install gcc-6 g++-6 -y && \
sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-6 60 --slave /usr/bin/g++ g++ /usr/bin/g++-6 && \
sudo apt-get install gcc-4.8 g++-4.8 -y && \
sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-4.8 60 --slave /usr/bin/g++ g++ /usr/bin/g++-4.8;
sudo update-alternatives --config gcc #When completed, you must change to the gcc you want to work with by default
gcc -v #To verify if it worked. Just type in your terminal

#https://developer.nvidia.com/cuda-downloads
scp <DOWNLOAD_PATH>/cuda-repo-ubuntu1604_8.0.61-1_amd64.deb ubuntu@<REMOTE_HOST>:~/
chmod +x cuda-repo-ubuntu1604_8.0.61-1_amd64.deb
sudo dpkg -i cuda-repo-ubuntu1604_8.0.61-1_amd64.deb
sudo apt-get update
sudo apt-get install cuda
sudo apt-get install cuda-drivers
export CUDA_HOME=/usr/local/cuda-8.0
export PATH=${CUDA_HOME}/bin:${PATH}
export LD_LIBRARY_PATH=${CUDA_HOME}/lib64

nvcc -V
cuda-install-samples-8.0.sh ~/
cd ~/NVIDIA_CUDA-8.0_Samples
make

~/NVIDIA_CUDA-8.0_Samples/bin/x86_64/linux/release/deviceQuery
~/NVIDIA_CUDA-8.0_Samples/bin/x86_64/linux/release/bandwidthTest

#TENSOR FLOW - https://www.tensorflow.org/install/install_linux#InstallingDocker
sudo apt-get install libcupti-dev

#DOCKER
sudo apt-get install \
    apt-transport-https \
    ca-certificates \
    curl \
    software-properties-common
curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add -
sudo apt-key fingerprint 0EBFCD88
sudo add-apt-repository \
   "deb [arch=amd64] https://download.docker.com/linux/ubuntu \
   $(lsb_release -cs) \
   stable"
sudo apt-get update
sudo apt-get install docker-ce=7.03.0
sudo docker run hello-world
sudo groupadd docker
sudo usermod -aG docker $USER
sudo systemctl enable docker

#NVIDIA-DOCKER
#https://github.com/NVIDIA/nvidia-docker
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
nvidia-docker run --rm nvidia/cuda nvidia-smi

nvidia-docker run -it gcr.io/tensorflow/tensorflow:latest-gpu bash
python
#>>> import tensorflow as tf
#>>> hello = tf.constant('Hello, TensorFlow!')
#>>> sess = tf.Session()
    #W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE3 instructions, but these are available on your machine and could speed up CPU computations.
    #W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations.
    #W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.
    #W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
    #W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.
    #W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.
    #I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:910] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
    #I tensorflow/core/common_runtime/gpu/gpu_device.cc:885] Found device 0 with properties:
    #name: Tesla K80
    #major: 3 minor: 7 memoryClockRate (GHz) 0.8235
    #pciBusID 0000:00:1e.0
    #Total memory: 11.17GiB
    #Free memory: 11.11GiB
    #I tensorflow/core/common_runtime/gpu/gpu_device.cc:906] DMA: 0
    #I tensorflow/core/common_runtime/gpu/gpu_device.cc:916] 0:   Y
    #I tensorflow/core/common_runtime/gpu/gpu_device.cc:975] Creating TensorFlow device (/gpu:0) -> (device: 0, name: Tesla K80, pci bus id: 0000:00:1e.0)
#>>> print(sess.run(hello))
nvidia-docker run -it -p 8888:8888 gcr.io/tensorflow/tensorflow:latest-gpu