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gpu [2017/10/11 11:46] popel [Basic commands] |
gpu [2018/03/21 15:29] ufal [Servers with GPU units] |
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GPU cluster '' | GPU cluster '' | ||
- | | machine | + | | machine | GPU type | GPU driver version |
- | | iridium | + | | dll1 | GeForce GTX 1080 | 384.69 | 6.1 | 8 | 8114 | 250 | |
- | | titan-gpu | + | | dll2 | GeForce GTX 1080 | 387.34 | 6.1 | 7 | 8114 | 250 | |
- | | twister1; twister2; kronos | + | | dll3 | GeForce GTX 1080 Ti | 375.66 | 6.1 | 9 | 11172 | 250 | |
- | | dll1; dll2 | GeForce GTX 1080 | cc6.1 | 8| 8 GB | | | + | | dll4 | GeForce GTX 1080 Ti | 375.66 | 6.1 | 10 | 11172 | 250 | |
- | | titan | + | | dll5 | GeForce GTX 1080 Ti | 384.69 | 6.1 | 10 | 11172 | 250 | |
- | | dll3; dll4; dll5 | GeForce GTX 1080 Ti | cc6.1 | 10| 11 GB | dll3 has only 9 GPUs since 2017/ | + | | dll6 | GeForce GTX 1080 Ti | 384.69 | 6.1 | 9 | 11172 | 122 | |
- | | dll6 | GeForce GTX 1080 Ti | cc6.1 | | + | | titan | GeForce GTX 1080 | 381.22 | 6.1 | 1 | 8114 | 31 | |
+ | | twister1 | Tesla K40c | 367.48 | 3.5 | 1 | 11439 | 47 | | ||
+ | | twister2 | Tesla K40c | 367.48 | 3.5 | 1 | 11439 | 47 | | ||
+ | | titan-gpu | GeForce GTX TITAN Z | 381.22 | 3.5 | 2 | 6082 | 31 | | ||
+ | | kronos | ||
+ | | iridium | Quadro K2000 | 367.48 | 3.0 | 1 | 1998 | 504 | | ||
Desktop machines: | Desktop machines: | ||
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All machines have CUDA8.0 and should support both Theano and TensorFlow. | All machines have CUDA8.0 and should support both Theano and TensorFlow. | ||
- | === Disk space === | + | [[https://ufaladm2.ufal.hide.ms.mff.cuni.cz/munin/ufal.hide.ms.mff.cuni.cz/lrc-headnode.ufal.hide.ms.mff.cuni.cz/index.html# |
- | All the GPU machines are at Malá Strana (not at Troja), so you should not use ''/ | + | |
- | - '' | + | |
- | - ''/ | + | |
- | - ''/ | + | |
- | - ''/ | + | |
- | === Individual acquisitions: | ||
- | There is an easy way to get one high-end GPU: [[https:// | + | ===== Rules ===== |
- | + | * First, read [[internal:Linux network]] and [[:Grid]]. | |
- | Take care, however, to coordinate | + | * All the rules from [[:Grid]] apply, even more strictly than for CPU because there are too many GPU users and not as many GPUs available. So as a reminder: always use GPUs via '' |
- | + | * Always specify | |
- | Known NVIDIA Academic Hardware Grants: | + | * If you need more than one GPU card (on a single machine), always require as many CPU cores ('' |
- | + | * For interactive jobs, you can use '' | |
- | * Ondřej Plátek | + | * Note that the dll machines have typically 10 cards, but "just" |
- | * Jan Hajič jr. - granted (early 2016) | + | |
- | + | ||
- | + | ||
- | | + | |
===== How to use cluster ===== | ===== How to use cluster ===== | ||
- | |||
- | In this section will be explained how to use cluster properly. | ||
==== Set-up CUDA and CUDNN ==== | ==== Set-up CUDA and CUDNN ==== | ||
- | You can add following | + | You should |
CUDNN_version=6.0 | CUDNN_version=6.0 | ||
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export CPATH=$CUDA_DIR/ | export CPATH=$CUDA_DIR/ | ||
fi | fi | ||
+ | |||
+ | When not using Theano, just Tensorflow this can be simplified to '' | ||
+ | |||
+ | TensorFlow 1.5 precompiled binaries need CUDA 9.0, for this you need to | ||
+ | |||
+ | export LD_LIBRARY_PATH=/ | ||
+ | |||
+ | You also need to use '' | ||
==== TensorFlow Environment ==== | ==== TensorFlow Environment ==== | ||
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This environment have TensorFlow 1.0 and all necessary requirements for NeuralMonkey. | This environment have TensorFlow 1.0 and all necessary requirements for NeuralMonkey. | ||
- | ==== Using cluster | + | ==== Pytorch Environment |
- | Rule number one, always use the GPU queue (never log in machine by ssh). Always use qsub or qsubmit with proper arguments. | + | If you want to use pytorch, there is a ready-made environment |
- | 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, | + | |
| | ||
- | For running experiments you must use qsub command: | + | It does rely on the CUDA and CuDNN setup above. |
- | qsub -q gpu.q -l gpu=1, | + | ==== Using cluster ==== |
- | + | ||
- | Cleaner way to use cluster is with / | + | As an alternative |
qsubmit --gpumem=2G --queue=" | qsubmit --gpumem=2G --queue=" | ||
| | ||
- | It is recommended to use priority -100 if you are not rushing for the results and don't need to leap over your colleagues jobs. | + | It is recommended to use priority |
==== Basic commands ==== | ==== Basic commands ==== | ||
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=== Select GPU device === | === Select GPU device === | ||
- | Use variable CUDA_VISIBLE_DEVICES | + | The variable CUDA_VISIBLE_DEVICES |
- | export CUDA_VISIBLE_DEVICES=0 | + | |
To list available devices, use: | To list available devices, use: | ||
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GPU specs for those GPUs we have: | GPU specs for those GPUs we have: | ||
* [[http:// | * [[http:// | ||
+ | |||
+ | ==== Individual acquisitions: | ||
+ | |||
+ | There is an easy way to get one high-end GPU: [[https:// | ||
+ | |||
+ | 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 " | ||
+ | |||
+ | Known NVIDIA Academic Hardware Grants: | ||
+ | |||
+ | * Ondřej Plátek - granted (2015) | ||
+ | * Jan Hajič jr. - granted (early 2016) | ||
+ | |||
+ | |||
+ |