Pablo Hernandez Cerdan

Pablo Hernandez Cerdan

Phd. in Biophysics and software development in C++

Installing TensorFlow in remote Ubuntu 16.04 via ssh

3 minute read

I will basically follow the TensorFlow instructions for Ubuntu 16.04. I do want to use GPU, and I am doing it via ssh (maybe useful if you are doing the same in a server in the cloud, AWS p2 , or similar)

I will use a virtualenv with python, python2 is the default in Ubuntu.

virtualenv --system-site-packages ~/tensorflow

This creates an environment that allow us to install stuff without interfering with the rest of the system, to activate it, run: source ~/tensorflow/bin/activate, and the shell name changes to something like: (tensorflow) user@remote-Ubuntu .

Now, install tensorflow in your virtualenv: pip install --upgrade tensorflow-gpu

tensorflow requires cuda 8.0 at the time of writting, but cuda in ubuntu is 7.5. Go to nvidia cuda developer page, and select your platform, in this case:

Operating System: Linux
Architecture: x86_64
Distro: Ubuntu
Version: 16.04
Installer Type: deb(network)

Instead of downloaded the file in my computer, I right clicked in the Download (2.6 Kb) link, and save the url. Then in the server, use cd ~/tensorflow; wget 'copied link'. If you have downloaded the link in your personal computer, you can also send the files to the server with scp ~/Downloads/cuda-repo-ubuntu1604_8.0.61-1_amd64.deb user@server_address ~/tensorflow/

In any case, go where you have the .deb file and install it:

sudo dpkg -i cuda-repo-ubuntu1604_8.0.61-1_amd64.deb
sudo apt-get update
sudo apt-get install cuda

We are done installing cuda-8.0, now we have to install the cuda functions for AI cudnn. To download them you have to register first as a cuda developer. Do it, and select the linux tar.gz files. Do the same, send them to the server copying the link or with scp, I have done: cd ~/tensorflow; tar -xf cudnn-8.0-linux-x64-v5.1.tgz, creating a ~/tensorflow/cuda/ folder with include and lib folders.

Now we have to put this files somewhere in the $PATH and $LD_LIBRARY_PATH for the OS to find them. I am going to do a little hack, I am going to create simlinks in /usr/local/cuda-8.0 where the rest of cuda files exist.

cd /usr/local/cuda-8.0
sudo ln -s ~/tensorflow/cuda/include/* ./include/
sudo ln -s ~/tensorflow/cuda/lib64/* ./lib/

This works because /usr/local/cuda wich is a simlink to the latest cuda, in our case cuda-8.0 is configured to be exported in /etc/ld.so.conf.d/cuda.conf. Check that it works. Because the simlink has changed from 7.5 to 8.0 run ldconfig, or restart your server if you are able to with sudo reboot.

Now we will try to validate the installation. Create a file: ~/tensorflow/hello_tensor.py from the simple example:

import tensorflow as tf
hello = tf.constant('Hello, TensorFlow!')
sess = tf.Session()
print(sess.run(hello))

Test it! and remember to activate your virtualenv:

source ~/tensorflow/bin/activate
python ~/tensorflow/hello_tensor.py

The output in my case was:

2017-05-19 16:46:02.752622: 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.
2017-05-19 16:46:02.752660: 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.
2017-05-19 16:46:02.752669: 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.
2017-05-19 16:46:02.849520: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2017-05-19 16:46:02.849850: I tensorflow/core/common_runtime/gpu/gpu_device.cc:887] Found device 0 with properties: 
name: Quadro K4000
major: 3 minor: 0 memoryClockRate (GHz) 0.8105
pciBusID 0000:05:00.0
Total memory: 2.95GiB
Free memory: 2.84GiB
2017-05-19 16:46:02.849876: I tensorflow/core/common_runtime/gpu/gpu_device.cc:908] DMA: 0 
2017-05-19 16:46:02.849884: I tensorflow/core/common_runtime/gpu/gpu_device.cc:918] 0:   Y 
2017-05-19 16:46:02.849900: I tensorflow/core/common_runtime/gpu/gpu_device.cc:977] Creating TensorFlow device (/gpu:0) -> (device: 0, name: Quadro K4000, pci bus id: 0000:05:00.0)
Hello tensorflow!

I have an OK card in the server, I am not sure what is the bare minimum cuda capabilities that you need, in my case, the Quadro K4000 has 3.0 capabilities.

Hope this is useful, enjoy playing with AI stuff, I will!

Note: Installing tensorflow from source will be nice for working directly in c++, but I am not comfortable installing bazel, the google build system, just for this. Hopefully somebody will publish a cmake workaround to avoid it.

You may also enjoy