TensorFlow学习笔记01-在EC2上安装

• Essentials
• CUDA 8.0
• cuDNN v5.1, for CUDA 8.0
• TensorFlow 1.0.0

选择EC2 p2.xlarge: 1 GPU (Nvidia K80), 61G RAM, $0.900 hourly
AMI: Ubuntu Server 16.04 LTS (HVM), SSD Volume Type - ami-a58d0dc5

此处略过如何启动ec2 instance，下面的操作直接在instance上进行。

安装dependencies & build tools

sudo apt-get update && sudo apt-get -y upgrade
sudo apt-get install -y build-essential git swig default-jdk zip zlib1g-dev
 
# 确定gcc已经安装
gcc --version
 
# 判断是否有NVIDIA GPU
lspci | grep -i nvidia

p2.xlarge GPU 如下：

We need to blacklist Nouveau which has a conflict with the nvidia driver.

echo -e "blacklist nouveau\nblacklist lbm-nouveau\noptions nouveau modeset=0\nalias nouveau off\nalias lbm-nouveau off\n" | sudo tee /etc/modprobe.d/blacklist-nouveau.conf echo options nouveau modeset=0 | sudo tee -a /etc/modprobe.d/nouveau-kms.conf
sudo update-initramfs -u
sudo reboot

安装Kenel headers

sudo apt-get install -y linux-image-extra-virtual
sudo reboot

sudo apt-get install -y linux-source linux-headers-`uname -r`

安装Cuda 8.0

wget https://developer.nvidia.com/compute/cuda/8.0/Prod2/local_installers/cuda-repo-ubuntu1604-8-0-local-ga2_8.0.61-1_amd64-deb

sudo dpkg -i cuda-repo-ubuntu1604-8-0-local-ga2_8.0.61-1_amd64-deb
rm cuda-repo-ubuntu1604-8-0-local-ga2_8.0.61-1_amd64-deb

sudo apt-get update
sudo apt-get install -y cuda

配置环境变量

vim ~/.profile

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

sudo reboot

验证Cuda安装成功

nvcc --version

# verify the driver is installed
nvidia-smi

cd /usr/local/cuda/
cd samples
sudo make

cd ./1_Utilities/deviceQuery
./deviceQuery

安装cuDNN v5.1

https://developer.nvidia.com/rdp/cudnn-download
cuDNN v5.1 Runtime Library for Ubuntu14.04 (Deb)
cuDNN v5.1 Developer Library for Ubuntu14.04 (Deb)

需要先加入Accelerated Computing Developer Program，然后下载到本地，再上传到ec2，然后安装

sudo dpkg -i libcudnn5_5.1.5-1+cuda8.0_amd64.deb
sudo dpkg -i libcudnn5-dev_5.1.5-1+cuda8.0_amd64.deb

The libcupti-dev library, which is the NVIDIA CUDA Profile Tools Interface. This library provides advanced profiling support.

sudo apt-get install libcupti-dev

安装Tensorflow

wget https://repo.continuum.io/archive/Anaconda2-4.3.0-Linux-x86_64.sh
bash Anaconda2-4.3.0-Linux-x86_64.sh

conda create -n tensorflow
source activate tensorflow
pip install --ignore-installed --upgrade https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.0.0-cp27-none-linux_x86_64.whl

运行成功，Tensorflow + GPU

Ref:
https://www.tensorflow.org/install/install_linux
https://gist.github.com/erikbern/78ba519b97b440e10640
http://expressionflow.com/2016/10/09/installing-tensorflow-on-an-aws-ec2-p2-gpu-instance/
https://medium.com/@giltamari/tensorflow-getting-started-gpu-installation-on-ec2-9b9915d95d6f#.ef96jc7a4
https://eatcodeplay.com/installing-tensorflow-with-python-3-on-ec2-gpu-instances-f9fa199eb3cc#.142acv4zq