AWS에 Tensorflow 설치하기

 

AWS를 이용하기 위해서 AWS Instance를 생성하시고 SSH로 접속하여 아래 스크립트를 따라서 설치하면 됩니다.

[AWS 인스턴스 만들기]

http://yfkwon.tistory.com/5

---

[윈도우에서 Putty를 이용한 SSH 접속하기]

http://yfkwon.tistory.com/3

---

[TensorFlow 설치하기]

http://erikbern.com/2015/11/12/installing-tensorflow-on-aws/

install-tensorflow.sh                       

	# Note – this is not a bash script (some of the steps require reboot)
	# I named it .sh just so Github does correct syntax highlighting.
	#
	# This is also available as an AMI in us-east-1 (virginia): ami-cf5028a5
	#
	# The CUDA part is mostly based on this excellent blog post:
	# http://tleyden.github.io/blog/2014/10/25/cuda-6-dot-5-on-aws-gpu-instance-running-ubuntu-14-dot-04/
	# Install various packages
	sudo apt-get update
	sudo apt-get upgrade -y # choose “install package maintainers version”
	sudo apt-get install -y build-essential python-pip python-dev git python-numpy swig python-dev default-jdk zip zlib1g-dev
	# Blacklist Noveau which has some kind of 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 # Reboot (annoying you have to do this in 2015!)
	# Some other annoying thing we have to do
	sudo apt-get install -y linux-image-extra-virtual
	sudo reboot # Not sure why this is needed
	# Install latest Linux headers
	sudo apt-get install -y linux-source linux-headers-`uname -r`
	# Install CUDA 7.0 (note – don't use any other version)
	wget http://developer.download.nvidia.com/compute/cuda/7_0/Prod/local_installers/cuda_7.0.28_linux.run
	chmod +x cuda_7.0.28_linux.run
	./cuda_7.0.28_linux.run -extract=`pwd`/nvidia_installers
	cd nvidia_installers
	sudo ./NVIDIA-Linux-x86_64-346.46.run
	sudo modprobe nvidia
	sudo ./cuda-linux64-rel-7.0.28-19326674.run
	cd
	# Install CUDNN 6.5 (note – don't use any other version)
	# YOU NEED TO SCP THIS ONE FROM SOMEWHERE ELSE – it's not available online.
	# You need to register and get approved to get a download link. Very annoying.
	tar -xzf cudnn-6.5-linux-x64-v2.tgz
	sudo cp cudnn-6.5-linux-x64-v2/libcudnn* /usr/local/cuda/lib64
	sudo cp cudnn-6.5-linux-x64-v2/cudnn.h /usr/local/cuda/include/
	# At this point the root mount is getting a bit full
	# I had a lot of issues where the disk would fill up and then Bazel would end up in this weird state complaining about random things
	# Make sure you don't run out of disk space when building Tensorflow!
	sudo mkdir /mnt/tmp
	sudo chmod 777 /mnt/tmp
	sudo rm -rf /tmp
	sudo ln -s /mnt/tmp /tmp
	# Note that /mnt is not saved when building an AMI, so don't put anything crucial on it
	# Install Bazel
	cd /mnt/tmp
	git clone https://github.com/bazelbuild/bazel.git
	cd bazel
	git checkout tags/0.1.0
	./compile.sh
	sudo cp output/bazel /usr/bin
	# Install TensorFlow
	cd /mnt/tmp
	export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/usr/local/cuda/lib64"
	export CUDA_HOME=/usr/local/cuda
	git clone --recurse-submodules https://github.com/tensorflow/tensorflow
	cd tensorflow
	# Patch to support older K520 devices on AWS
	# wget "https://gist.githubusercontent.com/infojunkie/cb6d1a4e8bf674c6e38e/raw/5e01e5b2b1f7afd3def83810f8373fbcf6e47e02/cuda_30.patch"
	# git apply cuda_30.patch
	# According to https://github.com/tensorflow/tensorflow/issues/25#issuecomment-156234658 this patch is no longer needed
	# Instead, you need to run ./configure like below (not tested yet)
	TF_UNOFFICIAL_SETTING=1 ./configure
	bazel build -c opt --config=cuda //tensorflow/cc:tutorials_example_trainer
	# Build Python package
	# Note: you have to specify --config=cuda here - this is not mentioned in the official docs
	# https://github.com/tensorflow/tensorflow/issues/25#issuecomment-156173717
	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
	sudo pip install /tmp/tensorflow_pkg/tensorflow-0.5.0-cp27-none-linux_x86_64.whl
	# Test it!
	cd tensorflow/models/image/cifar10/
	python cifar10_multi_gpu_train.py
	# On a g2.2xlarge: step 100, loss = 4.50 (325.2 examples/sec; 0.394 sec/batch)
	# On a g2.8xlarge: step 100, loss = 4.49 (337.9 examples/sec; 0.379 sec/batch)
	# doesn't seem like it is able to use the 4 GPU cards unfortunately :(