CUDA10 on Ubuntu 17.10

9 April 2019

With a previous installation of CUDA9 I wrote code for my GPU against CUDA directly, but mostly using TensorFlow and Keras. To upgrate to CUDA10, I heard that Anaconda was a quicker way to install the dependencies, including cuDNN. It was relatively simple, but required manual installation of the latest libraries - at least on Ubuntu.

10 Downing Street, SW1, London.
Number 10.

First, find the version you want to install and its md5sum. They’re listed here, and I’m using the latest (March 2019) values for Linux.

wget https://repo.anaconda.com/archive/Anaconda3-2019.03-Linux-x86_64.sh
echo '43caea3d726779843f130a7fb2d380a2 Anaconda3-2019.03-Linux-x86_64.sh' | md5sum -c
# If hash was OK, install
bash ./Anaconda3-2019.03-Linux-x86_64.sh

Towards the end of the installation, you’ll be informed that:

==> For changes to take effect, close and re-open your current shell. <==

If you'd prefer that conda's base environment not be activated on startup,
   set the auto_activate_base parameter to false:

conda config --set auto_activate_base false

Finally, to install the required libraries - including cuda and cuDNN (versions 10.0.130 and 7.3.1 for the above installation):

(base) iain@iain-ubuntu:~$ conda install keras tensorflow-gpu

If you’ve already installed a previous version of the CUDA libraries, you might have a problem with conflicts.

python -c "from keras import backend as b; b.tensorflow_backend._get_available_gpus()"

Fails with tensorflow.python.framework.errors_impl.InternalError: cudaGetDevice() failed. Status: CUDA driver version is insufficient for CUDA runtime version

The solution is to install the latest drivers from NVIDIA. Once complete, the latest versions installed are reported for me as:

+-----------------------------------------------------------------------------+
| NVIDIA-SMI 418.56       Driver Version: 418.56       CUDA Version: 10.1     |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|===============================+======================+======================|
|   0  GeForce GTX 106...  Off  | 00000000:01:00.0  On |                  N/A |
|  0%   51C    P0    30W / 120W |    314MiB /  6077MiB |      0%      Default |
+-------------------------------+----------------------+----------------------+

And the available devices now include the GPU:

2019-04-17 12:22:41.466674: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115]
Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 4509 MB memory)
-> physical GPU
(
  device: 0,
  name: GeForce GTX 1060 6GB,
  pci bus id: 0000:01:00.0,
  compute capability: 6.1
)

cuda
tensorflow
keras