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Table of Contents
GPU at ÚFAL
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.
Servers with GPU units
machine | GPU; Capability [cc] | cores | GPU RAM | Cuda | Theano | TensorFlow | Comment |
titan-gpu | GeForce GTX Titan Z; cc3.5 | 2 | 6 GB each core | V7.5.17 | ? | works | |
twister1 | …has burnt | ||||||
twister2 | GeForce GTX 570; cc2.0 | 1 | 1 GB | V7.5.17 | ? | no, needs cc3.0+ | |
kronos-dev | Tesla K40c; cc3.5? | 1 | 12 GB | V6.5.12 | 0.6.0 | ? | |
kronos-dev | Quadro K2000; cc3.0 | 1 | 2 GB | as above | as above | ? |
Milan Fucik says that Troja servers can accommodate only Quadro K1200 4GB. We should probably test Quadro K2000 there before buying any of those.
Installed toolkits
This should mention where each interesting toolkit lives (on a particular machine).
TensorFlow
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
.
Select GPU device
Use variable CUDA_VISIBLE_DEVICES to constrain tensorflow to compute only on the selected one.
Basic commands
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/
Links
GPU specs for those GPUs we have: