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# Note – this is not a bash script (some of the steps require reboot) |
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# I named it .sh just so Github does correct syntax highlighting. |
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# |
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# This is also available as an AMI in us-east-1 (virginia): ami-cf5028a5 |
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# |
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# The CUDA part is mostly based on this excellent blog post: |
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# http://tleyden.github.io/blog/2014/10/25/cuda-6-dot-5-on-aws-gpu-instance-running-ubuntu-14-dot-04/ |
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|
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# Install various packages |
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sudo apt-get update |
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sudo apt-get upgrade -y # choose “install package maintainers version” |
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sudo apt-get install -y build-essential python-pip python-dev git python-numpy swig python-dev default-jdk zip zlib1g-dev |
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|
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# Blacklist Noveau which has some kind of conflict with the nvidia driver |
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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 |
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echo options nouveau modeset=0 | sudo tee -a /etc/modprobe.d/nouveau-kms.conf |
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sudo update-initramfs -u |
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sudo reboot # Reboot (annoying you have to do this in 2015!) |
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|
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# Some other annoying thing we have to do |
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sudo apt-get install -y linux-image-extra-virtual |
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sudo reboot # Not sure why this is needed |
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|
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# Install latest Linux headers |
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sudo apt-get install -y linux-source linux-headers-`uname -r` |
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|
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# Install CUDA 7.0 (note – don't use any other version) |
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wget http://developer.download.nvidia.com/compute/cuda/7_0/Prod/local_installers/cuda_7.0.28_linux.run |
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chmod +x cuda_7.0.28_linux.run |
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./cuda_7.0.28_linux.run -extract=`pwd`/nvidia_installers |
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cd nvidia_installers |
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sudo ./NVIDIA-Linux-x86_64-346.46.run |
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sudo modprobe nvidia |
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sudo ./cuda-linux64-rel-7.0.28-19326674.run |
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cd |
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|
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# Install CUDNN 6.5 (note – don't use any other version) |
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# YOU NEED TO SCP THIS ONE FROM SOMEWHERE ELSE – it's not available online. |
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# You need to register and get approved to get a download link. Very annoying. |
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tar -xzf cudnn-6.5-linux-x64-v2.tgz |
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sudo cp cudnn-6.5-linux-x64-v2/libcudnn* /usr/local/cuda/lib64 |
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sudo cp cudnn-6.5-linux-x64-v2/cudnn.h /usr/local/cuda/include/ |
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|
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# At this point the root mount is getting a bit full |
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# 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 |
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# Make sure you don't run out of disk space when building Tensorflow! |
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sudo mkdir /mnt/tmp |
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sudo chmod 777 /mnt/tmp |
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sudo rm -rf /tmp |
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sudo ln -s /mnt/tmp /tmp |
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# Note that /mnt is not saved when building an AMI, so don't put anything crucial on it |
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|
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# Install Bazel |
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cd /mnt/tmp |
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git clone https://github.com/bazelbuild/bazel.git |
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cd bazel |
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git checkout tags/0.1.0 |
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./compile.sh |
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sudo cp output/bazel /usr/bin |
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|
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# Install TensorFlow |
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cd /mnt/tmp |
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export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/usr/local/cuda/lib64" |
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export CUDA_HOME=/usr/local/cuda |
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git clone --recurse-submodules https://github.com/tensorflow/tensorflow |
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cd tensorflow |
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# Patch to support older K520 devices on AWS |
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# wget "https://gist.githubusercontent.com/infojunkie/cb6d1a4e8bf674c6e38e/raw/5e01e5b2b1f7afd3def83810f8373fbcf6e47e02/cuda_30.patch" |
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# git apply cuda_30.patch |
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# According to https://github.com/tensorflow/tensorflow/issues/25#issuecomment-156234658 this patch is no longer needed |
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# Instead, you need to run ./configure like below (not tested yet) |
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TF_UNOFFICIAL_SETTING=1 ./configure |
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bazel build -c opt --config=cuda //tensorflow/cc:tutorials_example_trainer |
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|
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# Build Python package |
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# Note: you have to specify --config=cuda here - this is not mentioned in the official docs |
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# https://github.com/tensorflow/tensorflow/issues/25#issuecomment-156173717 |
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bazel build -c opt --config=cuda //tensorflow/tools/pip_package:build_pip_package |
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bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/tensorflow_pkg |
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sudo pip install /tmp/tensorflow_pkg/tensorflow-0.5.0-cp27-none-linux_x86_64.whl |
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|
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# Test it! |
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cd tensorflow/models/image/cifar10/ |
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python cifar10_multi_gpu_train.py |
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|
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# On a g2.2xlarge: step 100, loss = 4.50 (325.2 examples/sec; 0.394 sec/batch) |
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# On a g2.8xlarge: step 100, loss = 4.49 (337.9 examples/sec; 0.379 sec/batch) |
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# doesn't seem like it is able to use the 4 GPU cards unfortunately :( |
