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gpu [2017/10/12 13:42] ufal [How to use cluster] |
gpu [2018/06/12 13:50] machacek.dominik [Servers with GPU units] |
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===== Servers with GPU units ===== | ===== Servers with GPU units ===== | ||
- | GPU cluster '' | + | GPU cluster '' |
+ | | machine | GPU type | GPU driver version | [[https:// | ||
+ | | dll1 | GeForce GTX 1080 | 396.24 | 6.1 | 8 | 8 | 249 | | ||
+ | | dll2 | GeForce GTX 1080 | 396.24 | 6.1 | 8 | 8 | 249 | | ||
+ | | dll3 | GeForce GTX 1080 Ti | 396.24 | 6.1 | 9 | 11 | 249 | | ||
+ | | dll4 | GeForce GTX 1080 Ti | 396.24 | 6.1 | 10 | 11 | 249 | | ||
+ | | dll5 | GeForce GTX 1080 Ti | 396.24 | 6.1 | 10 | 11 | 249 | | ||
+ | | dll6 | GeForce GTX 1080 Ti | 396.24 | 6.1 | 9 | 11 | 123 | | ||
+ | |||
+ | To be migrated to new cluster: | ||
+ | |||
+ | | titan-gpu | GeForce GTX TITAN Z | 381.22 | 3.5 | 2 | 6 | 31 | | ||
+ | | kronos | GeForce GTX 1080 Ti | 384.81 | 6.1 | 1 | 11 | 125 | | ||
+ | | titan | GeForce GTX 1080 | 381.22 | 6.1 | 1 | 8 | 31 | | ||
+ | | twister1 | Tesla K40c | ? | ? | 1 | 11 | 47 | | ||
+ | | twister2 | Tesla K40c | 384.81 | 3.5 | 1 | 11 | 47 | | ||
- | | machine | ||
- | | iridium | ||
- | | titan-gpu | ||
- | | twister1; twister2; kronos | Tesla K40c | cc3.5 | | ||
- | | dll1; dll2 | GeForce GTX 1080 | cc6.1 | | ||
- | | titan | GeForce GTX 1080 | cc6.1 | | ||
- | | dll3; dll4; dll5 | GeForce GTX 1080 Ti | cc6.1 | 10| 11 GB | dll3 has only 9 GPUs since 2017/07 | | ||
- | | dll6 | GeForce GTX 1080 Ti | cc6.1 | | ||
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]]. | ||
+ | * 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 the number | ||
+ | * If you need more than one GPU card (on a single machine), always require as many CPU cores ('' | ||
+ | * For interactive jobs, you can use '' | ||
+ | * Note that the dll machines have typically 10 cards, but " | ||
- | Take care, however, | + | ===== How to use cluster |
- | Known NVIDIA Academic Hardware Grants: | + | ==== Set-up CUDA and CUDNN ==== |
- | * Ondřej Plátek - granted (2015) | + | You should add the following commands into your ~/.bashrc |
- | * Jan Hajič jr. - granted (early 2016) | + | |
+ | CUDNN_version=6.0 | ||
+ | CUDA_version=8.0 | ||
+ | CUDA_DIR_OPT=/ | ||
+ | if [ -d " | ||
+ | CUDA_DIR=$CUDA_DIR_OPT | ||
+ | export CUDA_HOME=$CUDA_DIR | ||
+ | export THEANO_FLAGS=" | ||
+ | export PATH=$PATH: | ||
+ | export LD_LIBRARY_PATH=$LD_LIBRARY_PATH: | ||
+ | export CPATH=$CUDA_DIR/ | ||
+ | fi | ||
- | | + | When not using Theano, just Tensorflow this can be simplified to '' |
- | ===== How to use cluster ===== | + | TensorFlow 1.5 precompiled binaries need CUDA 9.0, for this you need to |
- | In this section will be explained how to use cluster properly. | + | export LD_LIBRARY_PATH=/ |
- | ==== Set-up CUDA and CUDNN ==== | + | You also need to use '' |
- | You can add following command into your ~/.bashrc | + | **THE NEW CLUSTER (SGE 8.1.9)** |
- | | + | Multiple versions of '' |
- | CUDA_version=8.0 | + | |
- | CUDA_DIR_OPT=/ | + | You need to set library path from your '' |
+ | |||
+ | | ||
+ | CUDA_version=9.0 | ||
+ | CUDA_DIR_OPT=/ | ||
if [ -d " | if [ -d " | ||
CUDA_DIR=$CUDA_DIR_OPT | CUDA_DIR=$CUDA_DIR_OPT | ||
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export CPATH=$CUDA_DIR/ | export CPATH=$CUDA_DIR/ | ||
fi | fi | ||
+ | |||
+ | * When not using Theano, just Tensorflow this can be simplified to '' | ||
+ | |||
+ | * There is no default and you always need to set '' | ||
+ | |||
+ | * Note that '' | ||
+ | |||
+ | |||
+ | |||
+ | |||
==== TensorFlow Environment ==== | ==== TensorFlow Environment ==== | ||
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And then you can activate your environment: | And then you can activate your environment: | ||
- | source activate | + | source activate |
- | source activate | + | source activate |
- | This environment have TensorFlow 1.0 and all necessary requirements for NeuralMonkey. | + | This environment have TensorFlow 1.8.0 and all necessary requirements for NeuralMonkey. |
==== Pytorch Environment ==== | ==== Pytorch Environment ==== | ||
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==== Using cluster ==== | ==== Using cluster ==== | ||
- | Rule number one, always use the GPU queue (never log in machine by ssh). Always use qsub or qsubmit with proper arguments. | + | As an alternative to '' |
- | + | ||
- | For testing and using the cluster interactively | + | |
- | + | ||
- | qrsh -q gpu.q -l gpu=1, | + | |
- | + | ||
- | For running experiments you must use qsub command: | + | |
- | + | ||
- | qsub -q gpu.q -l gpu=1, | + | |
- | + | ||
- | Cleaner way to use cluster is with / | + | |
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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| titan | GeForce GTX 1080 Ti | | | titan | GeForce GTX 1080 Ti | | ||
| dll1 | (2 GPU) GeForce GTX 1080; cc6.1 | | | dll1 | (2 GPU) GeForce GTX 1080; cc6.1 | | ||
+ | | twister2 | ||
| dll2 | GeForce GTX 1080; cc6.1 | | | dll2 | GeForce GTX 1080; cc6.1 | | ||
| titan-gpu | | titan-gpu | ||
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The previous benchmark only compares the speed of processing units within the GPUs and do not take into account the size of memory. Therefore I have conducted another benchmark, this time for each graphic card I have increased the batch size as much as possible so the model still could fit into the GPU (the previous benchmark model had batch size 20). This way the results should be more representative of the power for each GPU. | The previous benchmark only compares the speed of processing units within the GPUs and do not take into account the size of memory. Therefore I have conducted another benchmark, this time for each graphic card I have increased the batch size as much as possible so the model still could fit into the GPU (the previous benchmark model had batch size 20). This way the results should be more representative of the power for each GPU. | ||
- | | GPU; Cuda capability | + | | GPU; Cuda capability |
- | | Tesla K40c; cc3.5 | | + | | GeForce GTX 1080 Ti; cc6.1 | 11 GB | 00:55:56 | 2300 | dll5 | |
- | | GeForce GTX 1080 Ti; cc6.1 | 11 GB | 00:55:56 | 2300 | dll5 | | + | | GeForce GTX 1080; cc6.1 | 8 GB | 01:10:57 | 1700 | dll1 | |
- | | GeForce GTX 1080; cc6.1 | 8 GB | 01:10:57 | 1700 | dll1 | | + | | Quadro P5000 |
| GeForce GTX Titan Z; cc3.5 | 6 GB | 02:20:47 | 1100 | titan-gpu | | | GeForce GTX Titan Z; cc3.5 | 6 GB | 02:20:47 | 1100 | titan-gpu | | ||
- | | Quadro K2000; cc3.0 | 2 GB | 28:15:26 | 50 | iridium | | + | | Quadro K2000; cc3.0 | 2 GB | 28:15:26 | 50 | iridium |
===== Links ===== | ===== Links ===== | ||
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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) | ||
+ | |||
+ | |||
+ |