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.

not used at the moment: GeForce GTX 570 (from twister2)
All machines have CUDA8.0 and should support both Theano and TensorFlow.

Summary of future plans:

• Current Troja servers won't get any GPUs (the only option would be Quadro K1200 4GB, horribly cost-inefficient)
• The old Quadro K2000 we have is a much more low end piece, so we can't test is in Troja.
• There is MetaCentrum which also has GPUs, so testing can be done there.
• It is impossible (wasteful in terms of space and forbidden by a dean regulation) to put non-rack machines to our servers rooms. So we won't be buying GeForce GTX 1080 (~20000CZK, out of stock now), for a non-rack machine since we most likely don't have any available.
• Yes, there are grant applications under review which include rack machines with GPUs, e.g. 5×2 or something like that; more will be known in 2017.

There is an easy way to get one high-end GPU: ask NVIDIA for an Academic Hardware Grant. All it takes is writing a short grant application (at most ~2 hrs of work from scratch; if you have a GAUK, ~15 minutes of copy-pasting). Due to the GPU housing issues (mainly rack space and cooling), Milan F. said we should request the Tesla-line cards. If you want to have a look at an application, feel free to ask at hajicj@ufal.mff.cuni.cz :)

Take care, however, to coordinate the grant applications a little, so that not too many arrive from UFAL within a short time: these grants are explicitly not intended to build GPU clusters, they are “seeding” grants meant for researchers to try out GPUs (and fall in love with them, and buy a cluster later). If you are planning to submit the hardware grant, have submitted one, or have already been awarded one, please add yourself here.

Known NVIDIA Academic Hardware Grants:

• Ondřej Plátek - granted (2015)
• Jan Hajič jr. - granted (early 2016)
• Jindra Helcl - planning to apply (fall 2016)

* 980 vs 1080 vs Titan X (not the Titan Z we have)

In the following table is the experiment conducted by Tom Kocmi. You can replicate experiment: /a/merkur3/kocmanek/GPUBenchmark (you will need to prepare environment of TensorFlow11 or use my ANACONDA)

I am preparing department-wide benchmark, but meanwhile the results with same setting:
* Athena (GTX 1080) - 2 hodiny 32 minut
* Twister (Tesla K40c) - 6 hodin 46 minut

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

To list available devices, use:

/opt/cuda/samples/1_Utilities/deviceQuery/deviceQuery | grep ^Device

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/
/usr/local/cuda/samples/1_Utilities/deviceQuery/deviceQuery
  # shows CUDA capability etc.

• CUDA Supported GPUs (Wikipedia table of Compute Capability)

GPU specs for those GPUs we have:

• Tesla K40c