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||Comment|
|titan-gpu||GeForce GTX Titan Z; cc3.5||2||6 GB each core|
|twister1||Tesla K40c; cc3.5||1||12 GB|
|twister2||Tesla K40c; cc3.5||1||12 GB|
|kronos-dev||Tesla K40c; cc3.5||1||12 GB|
|iridium||Quadro K2000; cc3.0||1||2 GB|
|victoria||GeForce GT 630; cc3.0||1||2 GB||Ondrej Bojar's desktop machine|
|arc||GeForce GT 630; cc3.0||1||2 GB||Ales's desktop machine|
|athena||GeForce GTX 1080; cc6.1||1||8 GB||Tom's desktop machine|
|dll1||GeForce GTX 1080; cc6.1||8||8 GB each core|
|dll2||GeForce GTX 1080; cc6.1||8||8 GB each core|
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.
Individual acquisitions: NVIDIA Academic Hardware Grants
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 (2017 check with Milan about this issue). If you want to have a look at an application, feel free to ask at firstname.lastname@example.org :)
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)
How to use cluster
In this section will be explained how to use cluster properly.
Majority people at UFAL use TensorFlow. To start using it you need to create python virtual environment (virtualenv or use Anaconda for it). Into the environment you must place TensorFlow. The TF is either in CPU or GPU version.
pip install tensorflow pip install tensorflow-gpu
You can use prepared environment by adding into your ~/.bashrc
And then you can activate your environment:
source activate tf1 source activate tf1cpu
This environment have TensorFlow 1.0 and all necessary requirements for NeuralMonkey.
Rule number one, always use the GPU queue (never log in machine by ssh). Always use qsub or qsubmit with proper arguments.
For testing and using the cluster interactively you can use qrsh (this should not be used for long running experiments since the console is not closed on the end of the experiment). Following command will assign you a GPU and creates interactive console.
qrsh -q gpu.q -l gpu=1 -pty yes bash
For running experiments you must use qsub command:
qsub -q gpu.q -l gpu=1,gpu_cc_min3.5=1,gpu_ram=2G WHAT_SHOULD_BE_RUN
Cleaner way to use cluster is with /home/bojar/tools/shell/qsubmit
qsubmit --gpumem=2G --queue="gpu.q" WHAT_SHOULD_BE_RUN
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.
Select GPU device
Use variable CUDA_VISIBLE_DEVICES to constrain tensorflow to compute only on the selected one. For the use of first GPU use (GPU queue do this for you):
To list available devices, use:
/opt/cuda/samples/1_Utilities/deviceQuery/deviceQuery | grep ^Device
In the following table is the experiment conducted by Tom Kocmi. You can replicate experiment: /a/merkur3/kocmanek/Projects/GPUBenchmark (you will need to prepare environment of TensorFlow11 or use my ANACONDA)
|machine||Setup; CPU/GPU; Capability [cc]||Walltime||Note|
|athena||GeForce GTX 1080; cc6.1||9:55:58||Tom's desktop|
|dll2||(2 GPU) GeForce GTX 1080; cc6.1||10:19:40||with CUDA_VISIBLE_DEVICES=0|
|dll1||(2 GPU) GeForce GTX 1080; cc6.1||12:34:34||Probably only one GPU was used|
|dll2||(2 GPU) GeForce GTX 1080; cc6.1||13:01:05||Only one GPU was used|
|titan-gpu||(2 GPU) GeForce GTX Titan Z; cc3.5||16:05:24||Probably only one GPU was used|
|kronos-dev||Tesla K40c; cc3.5||22:41:01|
|twister2||Tesla K40c; cc3.5||22:43:10|
|twister1||Tesla K40c; cc3.5||24:19:45|
|helena1||16x cores CPU||46:33:14|
|belzebub||16x cores CPU||52:36:56|
|iridium||Quadro K2000; cc3.0||59:47:58|
|helena7||8x cores CPU||60:39:17|
|arc||GeForce GT 630; cc3.0||103:42:30||(approximated after 66 hours)|
|lucifer4||8x cores CPU||134:41:22|
|victoria||GeForce GT 630; cc3.0||—||never run, same GPU as Arc|
GPU specs for those GPUs we have: