Installing Tensorflow can be a hassle, especially if we want to use its GPU support abilities. After a long trial-and-error process, I managed to make it work.
Disclaimer: The following works for Ubuntu 16.04, not verified for other versions. Besides, as Tensorflow versions change you should always follow the official instructions first. Just so you know.
- Delete previous TF installations from ALL
/usr/lib/python2.7/dist-packages(in Python 2 and/or Python 3, DON’T FORGET THE ANACONDA FOLDER, for me was
- Install NVIDIA drivers https://blog.countableset.com/2016/08/27/ubuntu-install-nvidia-drivers/ (if you don’t find PCI in the BIOS, don’t worry)
Then on the Ubuntu desktop click on parameters on the top right of the screen, “System Parameters”, then “Software & Updates” and “Additional Drivers”, then select the driver you just installed and “Apply Changes”.
The last part (after “Special notes about full disk encryption”) is only useful if you get bugs
Don’t take the last version (Take 367 – it doesn’t seem to work with later versions).
AT LEAST check with the command
nvidia-smi: if not found, not good
http://askubuntu.com/questions/206283/how-can-i-uninstall-a-nvidia-driver-completely might be useful to get a clean install. I STRONGLY recommand to do it, especially if you had a previous installation.
- Follow tutorial here https://alliseesolutions.wordpress.com/2016/09/08/install-gpu-tensorflow-from-sources-w-ubuntu-16-04-and-cuda-8-0-rc/
WARNING: If python was installed with anaconda, make sure that the Python Path in TF configuration step are in the Anaconda Folder (and not what is told in the article) – was default for me
SECOND WARNING: the CUDNN Version to put in the configuration is
THIRD WARNING: might get an error at the “Build & Install Pip Package” part, but it usually is because YOU FORGOT TO HIT TAB
By the way, at this step you need to use
pip3if you use Python 3) WITHOUT
sudoif you use Anaconda. That is because the installation folder is different if you
sudo. You can try
sudo which pythonto check that.
FOURTH WARNING: Java jdk 9 seems to cause problems with bazel, you may need to delete java and install an older version (THANKS Reda Boumahdi)
FIFTH WARNING: If at step
./configure # tensorflowyou get
Invalid SYCL 1.2 library path. /usr/local/computecpp/lib/libComputeCpp.so cannot be foundtry (THANKS Aurélien Smith) https://www.codeplay.com/…/computes…/computecpp/download
sudo mkdir /usr/local/computecpp sudo cp -r ComputeCpp-CE-0.1.1-Linux/* /usr/local/computecpp/
- SIXTH WARNING: Check the “Compute Capability” of your GPU on this page https://developer.nvidia.com/cuda-gpus. If it is not
5.2, enter it (eg
3.0) in the last
- Test with
>>> import tensorflow as tf
- If that works, try
sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
It should display your GPU. If it doesn’t, or if
import tensorflow as tfled to error messages, THAT becomes interesting. Go to step 5.
If you didn’t get any error message but the GPU isn’t there, you probably don’t use the “right” TF, which means that you didn’t clean up all folders before install. Try doing that, as well as installing NVIDIA drivers if it doesn’t work
- You probably got a message like “Couldn’t open CUDA library libcuda.so.1”. It takes a few steps to solve this:
libcuda.so.367.57(or the version of your NVIDIA driver) with
sudo find / -iname "libcuda.so.1". It should appear at least in one folder apparently unrelated to CUDA, like
/usr/lib/x86_64-linux-gnu/. There you can also find the file
If it doesn’t exist, try
sudo apt-get install libcuda1-367
Go to your
/usr/local/cuda/lib64folder and create a symbolic link to the previous file:
sudo ln -s /usr/lib/x86_64-linux-gnu/libcuda.so.367.57 libcuda.so.1
If the destination file already exists, delete it and retry
Retry step 4
That’s where I won: FIREWORKS
Another problem: If you have an error that talks about libcuda.so.6, try
sudo cp -p /usr/lib/x86_64-linux-gnu/libstdc++.so.6 /home/YOUR_USER_NAME/anaconda3/lib/
A solution is also on the Tensorflow website, which is basically the same, but some other bugs are adressed there: https://www.tensorflow.org/get_started/os_setup#optional_install_cuda_gpus_on_linux