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gpu [2016/05/02 12:33] ufal |
gpu [2018/11/01 14:41] vodrazka [Servers with GPU units] |
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===== Servers with GPU units ===== | ===== Servers with GPU units ===== | ||
+ | GPU cluster '' | ||
+ | | machine | GPU type | GPU driver version | [[https:// | ||
+ | | dll1 | GeForce GTX 1080 | 396.24 | 6.1 | 8 | 8 | 249 | yes | | ||
+ | | dll2 | GeForce GTX 1080 | 396.24 | 6.1 | 7 | 8 | 249 | yes | | ||
+ | | dll3 | GeForce GTX 1080 Ti | 396.24 | 6.1 | 10 | 11 | 249 | yes | | ||
+ | | dll4 | GeForce GTX 1080 Ti | 396.24 | 6.1 | 10 | 11 | 249 | yes | | ||
+ | | dll5 | GeForce GTX 1080 Ti | 396.24 | 6.1 | 10 | 11 | 249 | yes | | ||
+ | | dll6 | GeForce GTX 1080 Ti | 396.24 | 6.1 | 10 | 11 | 123 | yes | | ||
+ | | kronos | GeForce GTX 1080 Ti | 396.24 | 6.1 | 1 | 11 | 123 | yes | | ||
+ | | titan1 | GeForce GTX 1080 | 396.24 | 6.1 | 1 | 8 | 30 | yes | | ||
+ | | titan2 | Tesla K40c | 396.24 | 3.5 | 1 | 11 | 30 | yes | | ||
+ | | twister1 | Tesla K40c | 396.24 | 3.5 | 1 | 11 | 45 | no | | ||
+ | | twister2 | Tesla K40c | 396.24 | 3.5 | 1 | 11 | 45 | no | | ||
- | | machine | GPU; [[https:// | + | Desktop machines: |
- | | titan-gpu | + | | machine |
- | | twister1 | + | | victoria; arc |
- | | twister2 | + | | athena |
- | | kronos-dev | Tesla K40c; cc3.5? | + | |
- | | kronos-dev | Quadro K2000; cc3.0 | + | |
+ | Not used at the moment: GeForce GTX 570 (from twister2) | ||
+ | Multiple versions of CUDA library are accessible on each machine together with cudnn. Theano and TensorFlow is supported. | ||
- | Milan Fucik says that Troja servers can accommodate only [[http://www.czc.cz/hp-quadro-k1200-4gb/171662/produkt? | + | [[http://ufaladm2.ufal.hide.ms.mff.cuni.cz/munin/ufal.hide.ms.mff.cuni.cz/lrc-master.ufal.hide.ms.mff.cuni.cz/ |
- | ===== Installed toolkits ===== | ||
- | //This should mention where each interesting toolkit lives (on a particular | + | ===== Rules ===== |
+ | * First, read [[internal: | ||
+ | * 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 of GPU cards (e.g. '' | ||
+ | * If you need more than one GPU card (on a single | ||
+ | * For interactive jobs, you can use '' | ||
+ | * Note that the dll machines have typically 10 cards, but " | ||
- | ==== TensorFlow | + | ===== How to use cluster ===== |
- | [[https:// | + | ==== Set-up CUDA and CUDNN ==== |
- | ===== Select GPU device ===== | + | Multiple versions of '' |
- | Use variable CUDA_VISIBLE_DEVICES | + | You need to set library path from your '' |
- | ===== Basic commands | + | CUDNN_version=7.0 |
+ | CUDA_version=9.0 | ||
+ | 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 '' | ||
+ | * Note that the '' | ||
+ | |||
+ | |||
+ | ==== TensorFlow Environment ==== | ||
+ | |||
+ | Many people at UFAL use TensorFlow. To start using it it is recommended to create a [[python|Python virtual environment]] (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 | ||
+ | |||
+ | export PATH=/ | ||
+ | |||
+ | And then you can activate your environment: | ||
+ | |||
+ | source activate tf18 | ||
+ | source activate tf18cpu | ||
+ | |||
+ | This environment have TensorFlow 1.8.0 and all necessary requirements for NeuralMonkey. | ||
+ | |||
+ | ==== Pytorch Environment ==== | ||
+ | |||
+ | If you want to use pytorch, there is a ready-made environment in | ||
+ | |||
+ | / | ||
+ | |||
+ | It does rely on the CUDA and CuDNN setup above. | ||
+ | |||
+ | ==== Using cluster ==== | ||
+ | |||
+ | As an alternative to '' | ||
+ | |||
+ | qsubmit --gpumem=2G --queue=" | ||
+ | |||
+ | It is recommended to use priority lower than the default -100 if you are not rushing for the results and don't need to leap over your colleagues jobs. | ||
+ | ==== Basic commands ==== | ||
lspci | lspci | ||
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# dela to vubec neco uzitecneho? :-) | # dela to vubec neco uzitecneho? :-) | ||
# theano-* are typically located in / | # theano-* are typically located in / | ||
+ | / | ||
+ | # shows CUDA capability etc. | ||
+ | ssh dll1; ~popel/ | ||
+ | # who occupies which card on a given machine | ||
+ | | ||
+ | === Select GPU device === | ||
+ | |||
+ | The variable CUDA_VISIBLE_DEVICES constrains tensorflow and other toolkits to compute only on the selected GPUs. **Do not set this variable yourself** (unless debugging SGE), it is set for you automatically by SGE if you ask for some GPUs (see above). | ||
+ | |||
+ | To list available devices, use: | ||
+ | / | ||
+ | |||
+ | ===== Performance tests ===== | ||
+ | |||
+ | * [[http:// | ||
+ | |||
+ | In the following table is the experiment conducted by Tom Kocmi. You can replicate experiment: / | ||
+ | |||
+ | | machine | Setup; CPU/GPU; [[https:// | ||
+ | | athena | ||
+ | | dll2 | GeForce GTX 1080; cc6.1 | | ||
+ | | titan | GeForce GTX 1080 Ti | | ||
+ | | dll1 | (2 GPU) GeForce GTX 1080; cc6.1 | | ||
+ | | twister2 | ||
+ | | dll2 | GeForce GTX 1080; cc6.1 | | ||
+ | | titan-gpu | ||
+ | | kronos-dev | Tesla K40c; cc3.5 | | ||
+ | | twister2 | ||
+ | | twister1 | ||
+ | | helena1 | ||
+ | | belzebub | ||
+ | | iridium | ||
+ | | helena7 | ||
+ | | arc | GeForce GT 630; cc3.0 | 103:42:30 | (approximated after 66 hours) | | ||
+ | | lucifer4 | ||
+ | |||
+ | |||
+ | === Second Benchmark === | ||
+ | |||
+ | 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 | ||
+ | | 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 | | ||
+ | | Quadro P5000 | ||
+ | | 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 | ||
===== 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) | ||
+ | |||
+ | |||
+ |