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gpu [2017/05/16 10:48] kocmanek [Performance tests] |
gpu [2017/11/13 09:43] bojar [Rules] |
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===== Servers with GPU units ===== | ===== Servers with GPU units ===== | ||
+ | GPU cluster '' | ||
- | | machine | + | | machine |
- | | titan | + | | iridium |
- | | titan-gpu | + | | titan-gpu |
- | | twister1 | + | | twister1; twister2; kronos |
- | | twister2 | + | | dll1; dll2 | GeForce GTX 1080 |
- | | kronos-dev | Tesla K40c; cc3.5 | + | | titan |
- | | iridium | + | | dll3; dll4; dll5 | GeForce GTX 1080 Ti | |
- | | victoria | + | | dll6 | GeForce GTX 1080 Ti | |
- | | arc | GeForce GT 630; cc3.0 | 1 | 2 GB | Lucka' | + | |
- | | athena | + | |
- | | 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) | + | 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: | + | ===== Rules ===== |
- | * Current Troja servers won't get any GPUs (the only option would be [[http://www.czc.cz/hp-quadro-k1200-4gb/171662/ | + | * First, read [[internal:Linux network]] and [[:Grid]]. |
- | * The old Quadro K2000 we have is a much more low end piece, so we can' | + | * 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. '' |
- | * 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' | + | * If you need more than one GPU card (on a single machine), always require as many CPU cores ('' |
- | * 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. | + | * For interactive jobs, you can use '' |
+ | ===== How to use cluster ===== | ||
- | === Individual acquisitions: | + | ==== Set-up CUDA and CUDNN ==== |
- | There is an easy way to get one high-end GPU: [[https:// | + | You can add following command into your ~/.bashrc |
- | 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 | + | CUDNN_version=6.0 |
+ | CUDA_version=8.0 | ||
+ | CUDA_DIR_OPT=/opt/cuda-$CUDA_version | ||
+ | if [ -d "$CUDA_DIR_OPT" | ||
+ | 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 | ||
- | 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. | ||
==== 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 -pty yes bash | + | |
| | ||
- | 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 | 11:41:08 | | | + | | titan | GeForce GTX 1080 Ti | 10:45:11 | (new result with correct CUDA version) |
| 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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| arc | GeForce GT 630; cc3.0 | 103:42:30 | (approximated after 66 hours) | | | arc | GeForce GT 630; cc3.0 | 103:42:30 | (approximated after 66 hours) | | ||
| lucifer4 | | lucifer4 | ||
- | | victoria | ||
+ | === 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 | ||
+ | | Tesla K40c; cc3.5 | 12 GB | | ||
+ | | 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 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) | ||
+ | |||
+ | |||
+ |