使用Amazon AWS搭建GPU版tensorflow深度学习环境
原文链接： http://ramhiser.com/2016/01/05/installing-tensorflow-on-an-aws-ec2-instance-with-gpu-support/
原文作者在第一段就说啦，如果想省事的话，直接用他的AMI就好啦~
使用AWS的好处是便宜，使用竞价性的价格每小时只要几毛钱，而且以后随时随地都可以跑程序啦~~
还不太明白的小伙伴可以参考youtube上的教程，自备梯子：
CSC321: Using TensorFlow on AWS
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 ~/.bashrc.
The following things are installed:
- Essentials
- Cuda Toolkit 7.0
- cuDNN Toolkit 6.5
- Bazel 0.1.4 (Java 8 is a dependency)
- TensorFlow 0.6
To get going, I recommend requesting a spot instance. Can your instance go away? Sure. But 0.07/hrismuchnicerthan 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 pipTensorFlow requires installing CUDA Toolkit 7.0. To do this, run:
wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1410/x86_64/cuda-repo-ubuntu1410_7.0-28_amd64.deb
sudo dpkg -i cuda-repo-ubuntu1410_7.0-28_amd64.deb
rm cuda-repo-ubuntu1410_7.0-28_amd64.deb
sudo apt-get update
sudo apt-get install -y cudaAt 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:
sudo rebootNext up, we’ll add some environment variables. You may wish to add these to your ~/.bashrc.
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/lib64Getting closer. We need to install Bazel 0.1.4, which requires Java 8. For more details, see this comment.
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-installerNow for Bazel. (Thanks to Jim Simpson for this block.)
sudo apt-get install pkg-config zip g++ zlib1g-dev
https://github.com/bazelbuild/bazel/releases/download/0.1.4/bazel-0.1.4-installer-linux-x86_64.sh
chmod +x bazel-0.1.4-installer-linux-x86_64.sh
./bazel-0.1.4-installer-linux-x86_64.sh --user
rm bazel-0.1.4-installer-linux-x86_64.shOkay, almost done. Let’s clone the TensorFlow repo and initialize all submodules using their default settings.
git clone --recurse-submodules https://github.com/tensorflow/tensorflow
cd tensorflowFinally, 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 ./configureWhen 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"]: 3.0If 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 message:
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).
bazel build -c opt --config=cuda //tensorflow/cc:tutorials_example_trainer
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 --upgrade /tmp/tensorflow_pkg/tensorflow-0.6.0-cp27-none-linux_x86_64.whlCongrats! 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 up properly:
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.
python ~/tensorflow/tensorflow/models/image/mnist/convolutional.py# 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
pciBusID 0000:00:03.0
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
…
Initialized!
Epoch 0.00
Minibatch loss: 12.053, learning rate: 0.010000
Minibatch error: 90.6%
Validation error: 84.6%
Epoch 0.12
Minibatch loss: 3.282, learning rate: 0.010000
Minibatch error: 6.2%
Validation error: 6.9%
Epoch 0.23
Minibatch loss: 3.466, learning rate: 0.010000
Minibatch error: 12.5%
Validation error: 3.7%
Epoch 0.35
Minibatch loss: 3.191, learning rate: 0.010000
Minibatch error: 7.8%
Validation error: 3.4%
Epoch 0.47
Minibatch loss: 3.201, learning rate: 0.010000
Minibatch error: 4.7%
Validation error: 2.7%
…
I borrowed instructions from a few sources, so thanks very much to them. If you want more information about the various options, check out TensorFlow’s installation instructions.