- system update
$ sudo yum -y update
$ sudo yum -y install epel-release
$ sudo yum -y install gcc gcc-c++ python-pip python-devel atlas atlas-devel gcc-gfortran openssl-devel libffi-develInstall CUDA following the guide from http://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html#axzz4rhm3n5RO
- Install GPU driver
$ sudo rpm --install cuda-repo-<distro>-<version>.<architecture>.rpm
$ sudo yum clean expire-cache
$ sudo yum install cuda- Install CUDA® Toolkit 8.0
$ download from https://developer.nvidia.com/cuda-downloads
$ sudo sh cuda_8.0.61_375.26_linux.run
Do you accept the previously read EULA?
accept/decline/quit: accept
Install NVIDIA Accelerated Graphics Driver for Linux-x86_64 375.26?
(y)es/(n)o/(q)uit: n
Install the CUDA 8.0 Toolkit?
(y)es/(n)o/(q)uit: y
Enter Toolkit Location
[ default is /usr/local/cuda-8.0 ]:
Do you want to install a symbolic link at /usr/local/cuda?
(y)es/(n)o/(q)uit: y
Install the CUDA 8.0 Samples?
(y)es/(n)o/(q)uit: y
Enter CUDA Samples Location
[ default is /home/XXXXX ]: - install CUDNN
$ download from https://developer.nvidia.com/rdp/cudnn-download
$ tar zxf cudnn-8.0-linux-x64-v6.0.solitairetheme8
$ sudo cp cuda/include/cudnn.h /usr/local/cuda-8.0/include/
$ sudo cp cuda/lib64/libcudnn* /usr/local/cuda-8.0/lib64/.
$ sudo chmod a+x /usr/local/cuda-8.0/include/cudnn.h /usr/local/cuda-8.0/lib64/libcudnn*add the following lines into your .bashrc file
export PATH=/usr/local/cuda-8.0/bin${PATH:+:${PATH}}
export LD_LIBRARY_PATH=/usr/local/cuda-8.0/lib64\
${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}
export CUDA_HOME=/usr/local/cuda$ sudo pip install --upgrade pip
$ sudo pip install tensorflow-gpu
$ sudo pip install ipython$ ipython
Python 2.7.5 (default, Nov 6 2016, 00:28:07)
Type "copyright", "credits" or "license" for more information.
IPython 5.4.1 -- An enhanced Interactive Python.
? -> Introduction and overview of IPython's features.
%quickref -> Quick reference.
help -> Python's own help system.
object? -> Details about 'object', use 'object??' for extra details.
In [1]: import tensorflow as tf
In [2]: sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
2017-09-04 19:01:54.085247: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations.
2017-09-04 19:01:54.085335: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.
2017-09-04 19:01:54.085354: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
2017-09-04 19:01:54.085418: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.
2017-09-04 19:01:54.085439: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.
2017-09-04 19:01:56.229195: I tensorflow/core/common_runtime/gpu/gpu_device.cc:955] Found device 0 with properties:
name: GeForce GTX 1080 Ti
major: 6 minor: 1 memoryClockRate (GHz) 1.62
pciBusID 0000:04:00.0
Total memory: 10.91GiB
Free memory: 10.75GiB
2017-09-04 19:01:56.546687: W tensorflow/stream_executor/cuda/cuda_driver.cc:523] A non-primary context 0x5e8c1d0 exists before initializing the StreamExecutor. We haven't verified StreamExecutor works with that.
2017-09-04 19:01:56.547740: I tensorflow/core/common_runtime/gpu/gpu_device.cc:955] Found device 1 with properties:
name: GeForce GTX 1080 Ti
major: 6 minor: 1 memoryClockRate (GHz) 1.62
pciBusID 0000:81:00.0
Total memory: 10.91GiB
Free memory: 10.75GiB
2017-09-04 19:01:56.547901: I tensorflow/core/common_runtime/gpu/gpu_device.cc:847] Peer access not supported between device ordinals 0 and 1
2017-09-04 19:01:56.547928: I tensorflow/core/common_runtime/gpu/gpu_device.cc:847] Peer access not supported between device ordinals 1 and 0
2017-09-04 19:01:56.547961: I tensorflow/core/common_runtime/gpu/gpu_device.cc:976] DMA: 0 1
2017-09-04 19:01:56.547978: I tensorflow/core/common_runtime/gpu/gpu_device.cc:986] 0: Y N
2017-09-04 19:01:56.547999: I tensorflow/core/common_runtime/gpu/gpu_device.cc:986] 1: N Y
2017-09-04 19:01:56.548040: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1045] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 1080 Ti, pci bus id: 0000:04:00.0)
2017-09-04 19:01:56.548059: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1045] Creating TensorFlow device (/gpu:1) -> (device: 1, name: GeForce GTX 1080 Ti, pci bus id: 0000:81:00.0)
Device mapping:
/job:localhost/replica:0/task:0/gpu:0 -> device: 0, name: GeForce GTX 1080 Ti, pci bus id: 0000:04:00.0
/job:localhost/replica:0/task:0/gpu:1 -> device: 1, name: GeForce GTX 1080 Ti, pci bus id: 0000:81:00.0
2017-09-04 19:01:56.795901: I tensorflow/core/common_runtime/direct_session.cc:300] Device mapping:
/job:localhost/replica:0/task:0/gpu:0 -> device: 0, name: GeForce GTX 1080 Ti, pci bus id: 0000:04:00.0
/job:localhost/replica:0/task:0/gpu:1 -> device: 1, name: GeForce GTX 1080 Ti, pci bus id: 0000:81:00.0
##Now everything is done!
##Appendix If you meet the following bug report
$ nvidia-smi
Unable to determine the device handle for GPU 0000:81:00.0: Unable to communicate with GPU because it is insufficiently powered.
This may be because not all required external power cables are
attached, or the attached cables are not seated properly.then you should check your power supply of the GPU. This is not a software problem.
Please do not intsll the GPU driver from CUDA® Toolkit 8.0.
Thanks for the post. However, now conda takes care of most of these steps - https://anaconda.org/anaconda/tensorflow-gpu