Installing TensorFlow on an AWS EC2 Instance with GPU Support
05 Jan 2016
The following post describes how to install TensorFlow 0.6 on an Amazon EC2
Instance with GPU Support. I also created a
Public AMI (ami-e191b38b) with the resulting setup. Feel free to use it.
UPDATED (28 Jan 2016): The latest TensorFlow build requires Bazel 0.1.4. Post now reflects
this. Thanks to Jim Simpson for his assistance.
UPDATED (28 Jan 2016): The AMI provided now exports env variables in
The following things are installed:
Cuda Toolkit 7.0
cuDNN Toolkit 6.5
Bazel 0.1.4 (Java 8 is a dependency)
To get going, I recommend requesting a spot instance. Can your instance go away?
Sure. But $0.07/hr is much nicer than $0.65/hr when you are figuring things out.
I launched a single
g2.2xlarge instance using the Ubuntu Server 14.04 LTS AMI.
After launching your instance, install the essentials:
sudo apt-get update
sudo apt-get upgrade
sudo apt-get install -y build-essential git python-pip libfreetype6-dev libxft-dev libncurses-dev libopenblas-dev gfortran python-matplotlib libblas-dev liblapack-dev libatlas-base-dev python-dev python-pydot linux-headers-generic linux-image-extra-virtual unzip python-numpy swig python-pandas python-sklearn unzip wget pkg-config zip g++ zlib1g-dev
sudo pip install -U pip
TensorFlow requires installing CUDA Toolkit 7.0. To do this, run:
sudo dpkg -i cuda-repo-ubuntu1410_7.0-28_amd64.deb
sudo apt-get update
sudo apt-get install -y cuda
At some point, you get the following message:
Reboot your computer and verify
that the NVIDIA graphics driver can be loaded. I mean, it’s 2016. But
whatevs. We’ll reboot in a moment. Now, we need to download
cuDNN from Nvidia’s site.
After filling out an annoying questionnaire, you’ll download a file named
cudnn-6.5-linux-x64-v2.tgz. You need to transfer it to your EC2 instance: I
did this by adding it to my Dropbox folder and using
wget to upload it. Once you have uploaded
it to your home directory, run the following:
tar -zxf cudnn-6.5-linux-x64-v2.tgz && rm cudnn-6.5-linux-x64-v2.tgz
sudo cp -R cudnn-6.5-linux-x64-v2/lib * /usr/local/cuda/lib64/
sudo cp cudnn-6.5-linux-x64-v2/cudnn.h /usr/local/cuda/include/
Okay, now reboot:
Next up, we’ll add some environment variables. You may wish to add these to your
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
Getting closer. We need to install
Bazel 0.1.4, which
requires Java 8. For more details, see
Install Java 8 first.
sudo add-apt-repository -y ppa:webupd8team/java
sudo apt-get update
# Hack to silently agree license agreement
echo debconf shared/accepted-oracle-license-v1-1 select true | sudo debconf-set-selections
echo debconf shared/accepted-oracle-license-v1-1 seen true | sudo debconf-set-selections
sudo apt-get install -y oracle-java8-installer
Now for Bazel. (Thanks to Jim Simpson for this block.)
sudo apt-get install pkg-config zip g++ zlib1g-dev
chmod +x bazel-0.1.4-installer-linux-x86_64.sh
Okay, almost done. Let’s clone the TensorFlow repo and initialize all submodules
using their default settings.
Finally, we are going to build TensorFlow with GPU support using CUDA version
3.0 (currently required on AWS) via the unofficial settings.
TF_UNOFFICIAL_SETTING =1 ./configure
When you see the following message, type
3.0 to use CUDA version 3.0:
Please specify a list of comma-separated Cuda compute capabilities you want to build with.
You can find the compute capability of your device at: https://developer.nvidia.com/cuda-gpus.
Please note that each additional compute capability significantly increases your build
time and binary size.
[Default is: "3.5,5.2" ]: 3.0
If you forget to type
3.0, you’ll get the following error later on:
Ignoring gpu device (device: 0, name: GRID K520, pci bus id: 0000:00:03.0) with Cuda compute capability 3.0. The minimum required Cuda capability is 3.5.
Other than that, I went with all the default options, resulting in the nice
WARNING: You are configuring unofficial settings in TensorFlow. Because some
external libraries are not backward compatible, these settings are largely
untested and unsupported.
Pffft. Anyway, last steps. These take quite a while (~24 minutes for me).
-c opt --config =cuda //tensorflow/cc:tutorials_example_trainer
bazel build -c opt --config =cuda //tensorflow/tools/pip_package:build_pip_package
sudo pip install --upgrade /tmp/tensorflow_pkg/tensorflow-0.6.0-cp27-none-linux_x86_64.whl
Congrats! TensorFlow is installed. At this point, if you launch Python and run
the following code, you’ll see a lot of nice messages indicating your GPU is set
import tensorflow as tf
tf_session = tf . Session ()
x = tf . constant ( 1 )
y = tf . constant ( 1 )
tf_session . run ( x + y )
You can also check that TensorFlow is working by training a
CNN on the
MNIST data set.
# Lots of output followed by GPU-related things...
I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:909] successful NUMA node read from SysFS had negative value ( -1 ), but there must be at least one NUMA node, so returning NUMA node zero
I tensorflow/core/common_runtime/gpu/gpu_init.cc:103] Found device 0 with properties:
name: GRID K520
major: 3 minor: 0 memoryClockRate (GHz ) 0.797
Total memory: 4.00GiB
Free memory: 3.95GiB
I tensorflow/core/common_runtime/gpu/gpu_init.cc:127] DMA: 0
I tensorflow/core/common_runtime/gpu/gpu_init.cc:137] 0: Y
I tensorflow/core/common_runtime/gpu/gpu_device.cc:702] Creating TensorFlow device (/gpu:0 ) -> (device: 0, name: GRID K520, pci bus id: 0000:00:03.0 )
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:42] Allocating 3.66GiB bytes.
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:52] GPU 0 memory begins at 0x7023e0000 extends to 0x7ec556000
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:66] Creating bin of max chunk size 1.0KiB
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:66] Creating bin of max chunk size 2.0KiB
Minibatch loss: 12.053, learning rate: 0.010000
Minibatch error: 90.6%
Validation error: 84.6%
Minibatch loss: 3.282, learning rate: 0.010000
Minibatch error: 6.2%
Validation error: 6.9%
Minibatch loss: 3.466, learning rate: 0.010000
Minibatch error: 12.5%
Validation error: 3.7%
Minibatch loss: 3.191, learning rate: 0.010000
Minibatch error: 7.8%
Validation error: 3.4%
Minibatch loss: 3.201, learning rate: 0.010000
Minibatch error: 4.7%
Validation error: 2.7%
I borrowed instructions from
sources, so thanks
very much to them. If you want more information about the various options, check
out TensorFlow’s installation instructions.