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gpu [2017/07/17 08:01] kocmanek [Performance tests] |
gpu [2017/11/23 14:15] bojar link to munin graphs |
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===== Servers with GPU units ===== | ===== Servers with GPU units ===== | ||
+ | GPU cluster '' | ||
- | | machine | + | | machine |
- | | titan | + | | iridium |
- | | titan-gpu | + | | titan-gpu |
- | | twister1; twister2; kronos | Tesla K40c; cc3.5 | 1 | 12 GB | + | | twister1; twister2; kronos | Tesla K40c |
- | | iridium | + | | dll1; dll2 | GeForce GTX 1080 |
- | | victoria; arc | GeForce GT 630; cc3.0 | 1 | 2 GB | desktop machine | + | | titan |
- | | athena | + | | dll3; dll4; dll5 | GeForce GTX 1080 Ti | |
- | | dll1; dll2 | GeForce GTX 1080; cc6.1 | 8 | + | | dll6 | GeForce GTX 1080 Ti | |
- | | dll3; dll4; dll5 | GeForce GTX 1080 Ti; cc6.1 | 10 | 11 GB each core | | | + | |
- | not used at the moment: GeForce GTX 570 (from twister2) | + | Desktop machines: |
+ | | machine | ||
+ | | victoria; arc | GeForce GT 630 | cc3.0 | 1 | 2 GB | desktop machine | | ||
+ | | athena | ||
+ | |||
+ | Not used at the moment: GeForce GTX 570 (from twister2) | ||
All machines have CUDA8.0 and should support both Theano and TensorFlow. | All machines have CUDA8.0 and should support both Theano and TensorFlow. | ||
- | Summary of future plans: | + | [[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# |
- | * Current Troja servers won't get any GPUs (the only option would be [[http://www.czc.cz/hp-quadro-k1200-4gb/171662/produkt? | + | |
- | * 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. 5x2 or something like that; more will be known in 2017. | + | |
- | === Individual acquisitions: NVIDIA Academic Hardware Grants | + | ===== Rules ===== |
+ | * First, read [[internal:Linux network]] and [[: | ||
+ | * 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 machine), always require as many CPU cores ('' | ||
+ | * For interactive jobs, you can use '' | ||
- | There is an easy way to get one high-end GPU: [[https:// | + | ===== How to use cluster ===== |
- | 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 " | + | ==== Set-up CUDA and CUDNN ==== |
- | Known NVIDIA Academic Hardware Grants: | + | You can add following command into your ~/.bashrc |
- | | + | |
- | | + | |
+ | 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 | ||
- | |||
- | | ||
- | |||
- | ===== How to use cluster ===== | ||
- | |||
- | In this section will be explained how to use cluster properly. | ||
==== 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. | + | |
- | For testing and using the cluster interactively | + | If you want to use pytorch, there is a ready-made environment in |
- | | + | |
| | ||
- | 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=" | ||
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/ | / | ||
# shows CUDA capability etc. | # shows CUDA capability etc. | ||
+ | ssh dll1; ~popel/ | ||
+ | # who occupies which card on a given machine | ||
| | ||
=== 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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* [[http:// | * [[http:// | ||
- | In the following table is the experiment conducted by Tom Kocmi. You can replicate experiment: / | + | In the following table is the experiment conducted by Tom Kocmi. You can replicate experiment: / |
| machine | Setup; CPU/GPU; [[https:// | | machine | Setup; CPU/GPU; [[https:// | ||
| athena | | athena | ||
- | | dll2 | + | | dll2 | GeForce GTX 1080; cc6.1 | |
| 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 | | ||
- | | dll2 | + | | dll2 | GeForce GTX 1080; cc6.1 | |
| titan-gpu | | titan-gpu | ||
| kronos-dev | Tesla K40c; cc3.5 | | | kronos-dev | Tesla K40c; cc3.5 | | ||
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=== Second Benchmark === | === 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 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 |
| GPU; Cuda capability | | GPU; Cuda capability | ||
- | | Tesla K40c; cc3.5 | 12 GB | | + | | Tesla K40c; cc3.5 | 12 GB | |
| 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 | | ||
- | | GeForce GTX 1080; cc6.1 | 8 GB | | ||
| 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 | | ||
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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) | ||
+ | |||
+ | |||
+ |