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@erikbern
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Installing TensorFlow on EC2
# 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 :(
@raindeer
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To build for Python 3.4:

  • During configure specify /usr/bin/python3
  • Use bazel 0.1.1, git checkout tags/0.1.1
  • Use wheel 0.26
  • Don't use the current tensorflow master. It is not Python 3 compatible. Checkout the 0.6.0 tag.
  • Modify tensorflow/bazel-bin/tensorflow/tools/pip_package/build_pip_package. Add --python-tag py34 when building the wheel:
...
echo $(date) : "=== Building wheel"
python setup.py bdist_wheel --python-tag py34 >/dev/null
...
  • Make sure to use the correct wheel name when installing: sudo pip3 install /tmp/tensorflow_pkg/tensorflow-0.6.0-py34-none-any.whl

@Nodice
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Nodice commented Jan 14, 2016

Just for reference. Geforce 970, i7 Local Machine, examples/sec 903.3 examples/sec .142 sec/batch

@ggonzale
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Just got to run. Thank you for the code. My timing - measured at step 50

Instance Type s/batch
g2.2xlarge 0.26
g2.8xlarge 0.11 (with --num_gpus=4)
macbook-pro i7 quad end 2013 0.56

Quite unimpressive the usage of gpus, as @marcotrombetti says. I was also expecting an order of magnitude improvement.

@Razorwind
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I have done all the steps but the last pip install seems to be an issue
"Requirement '/tmp/tensorflow_pkg/tensorflow-0.5.0-cp27-none-linux_x86_64.whl' looks like a filename, but the file does not exist"

I used git "checkout tags/0.1.4" for basel instead.

*Edit ok silly me: Line 80 needs to be changed to 0.60 instead.

@Andyccs
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Andyccs commented Jan 31, 2016

@cancan101
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Using keras mnist_cnn script to compare the performance of Theano to Tensorflow on g2.2xlarges, I see 8s / epoch with Theano and 97s / epoch with Tensorflow!

@chrisconley
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In case anyone's interested, we documented how we installed TensorFlow along with Python 3.4 and Jupyter on EC2 based on this gist and many of the comments here. Thank you everyone!

@erikbern
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Author

erikbern commented Feb 4, 2016

Didn't notice all the comments here – Github doesn't send notifications on gists I guess. Anyway you should check out @chrisconley's link instead!

@AlexJoz
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AlexJoz commented Feb 20, 2016

Nice work! Thnx for guide ^^
Made another one with Python3, TensorFlow 0.7 and OpenCV 3.1:
https://gist.github.com/AlexJoz/1670baf0b32573ca7923
Public ami with my setup in N.Virginia: ami-9d0f3ff7

@axeltidemann
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If you for some reason found / to fill up unexplainably when compiling with bazel, this is due to bazel putting cache files in ~/.cache/bazel by default. Set export TEST_TMPDIR=/tmp/.cache to avoid this.

@ccywch
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ccywch commented Apr 10, 2016

There seems to be a new solution: https://aws.amazon.com/marketplace/pp/B01AOE205O

@deeprnd
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deeprnd commented May 24, 2016

@AlexJoz's ami works great

@SpencerC
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Published new AMI in N. Virginia with 0.8.0 support: ami-1e19ee73

@hpssjellis
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Thanks. I will look into my changing my bash file that installs the cpu version of tensorflow (with video) to a gpu version on cloud9 http://c9.io

https://github.com/hpssjellis/forth-tensorflow

@shamak
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shamak commented Jul 9, 2016

When I run line 73, I get an error: Unrecognized option: --host_force_python=py2

Any idea why?

@zaheersm
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zaheersm commented Jul 11, 2016

@shamak I'm getting the exact same error.

@zaheersm
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zaheersm commented Jul 11, 2016

@shamak Install the latest version of Bazel from here

Later, you might also need to setup CUDNN v4 (7.0) instead of v2 (6.5). See this issue

@rnditdev
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It seems that Cudnn can be downloaded with

curl -fvSL http://developer.download.nvidia.com/compute/redist/cudnn/v2/cudnn-6.5-linux-x64-v2.tgz -o cudnn-6.5-linux-x64-v2.tgz

@standy66
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standy66 commented Aug 15, 2016

Just for reference:

GTX 1070, i7 6700k, local machine, tensorflow inside docker container, using nvidia-docker (but I doubt it adds any overhead)

1744.3 examples/sec; 0.073 sec/batch

@alexstaravoitau
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I've recently prepared a couple of convenience scripts for firing up your AWS instance with Jupyter Notebook on board that you may find useful:

@pvels
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pvels commented Sep 4, 2016

For stat:

Zotac GTX 1080 AMP Extreme, 2560 CUDA cores, 1771 MHz core clock, 10000 MHz mem clock. i7 930 3.8 GHz boost clock.

step 100000, loss = 0.72 (1780.0 examples/sec; 0.072 sec/batch); time: 2h 5m.

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