AWS를 이용하기 위해서 AWS Instance를 생성하시고 SSH로 접속하여 아래 스크립트를 따라서 설치하면 됩니다.
[AWS 인스턴스 만들기]
[윈도우에서 Putty를 이용한 SSH 접속하기]
[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 :( |
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