This page summarizes which UFAL servers have some GPU card, and suggests basic diagnostic commands, paths to installed tools, etc., simply everything necessary at the very beginning of using GPUs for experiments.

Milan Fucik says that Troja servers can accommodate only Quadro K1200 4GB. The Quadro K2000 we have is a much more low end piece, so we can't test is in Troja.

This should mention where each interesting toolkit lives (on a particular machine).

This script installs TensorFlow 0.7.1 (and all other dependencies we need for Multimodal Translation) into `tf' and `tf-gpu' virtual environments. The GPU environment can be loaded by calling

source tf-gpu/bin/activate-gpu

OP: I created script which install Tensorflow 0.8 and test it if it uses GPU. TF is installed into `user` or `global` installation either for `python3.4` or `python2.7`

Use variable CUDA_VISIBLE_DEVICES to constrain tensorflow to compute only on the selected one. For the use of first GPU use:

export CUDA_VISIBLE_DEVICES=0

lspci
  # is any such hardware there?
nvidia-smi
  # more details, incl. running processes on the GPU
  # nvidia-* are typically located in /usr/bin
watch nvidia-smi
  # For monitoring GPU activity in a separate terminal (thanks to Jindrich Libovicky for this!)
nvcc --version
  # this should tell CUDA version
  # nvcc is typically installed in /usr/local/cuda/bin/
theano-test
  # dela to vubec neco uzitecneho? :-)
  # theano-* are typically located in /usr/local/bin/

• CUDA Supported GPUs (Wikipedia table of Compute Capability)

GPU specs for those GPUs we have:

• Tesla K40c