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gpu [2016/12/02 15:36] kocmanek |
gpu [2017/08/28 14:40] kocmanek [How to use cluster] |
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===== Servers with GPU units ===== | ===== Servers with GPU units ===== | ||
+ | GPU cluster '' | ||
- | | machine | GPU; [[https:// | + | | machine |
- | | titan-gpu | + | | iridium |
- | | twister1 | + | | titan-gpu |
- | | twister2 | + | | twister1; twister2; |
- | | kronos-dev | Tesla K40c; cc3.5 | + | | dll1; dll2 | GeForce GTX 1080 |
- | | iridium | + | | titan |
- | | victoria | + | | dll3; dll4; dll5 | GeForce GTX 1080 Ti | |
- | | arc | + | |
- | | athena | + | |
- | 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 | ||
- | Summary of future plans: | + | Not used at the moment: GeForce GTX 570 (from twister2) |
- | * Current | + | All machines have CUDA8.0 and should support both Theano and TensorFlow. |
- | * The old Quadro K2000 we have is a much more low end piece, so we can't test is in Troja. | + | |
- | | + | === Disk space === |
- | * It is impossible | + | All the GPU machines are at Malá Strana (not at Troja), so you should not use ''/ |
- | * Yes, there are grant applications | + | - '' |
+ | - '' | ||
+ | - ''/ | ||
+ | - ''/ | ||
+ | |||
+ | === Individual acquisitions: | ||
+ | |||
+ | There is an easy way to get one high-end GPU: [[https:// | ||
+ | |||
+ | Take care, however, to coordinate the grant applications | ||
+ | |||
+ | Known NVIDIA Academic Hardware Grants: | ||
+ | |||
+ | * Ondřej Plátek - granted (2015) | ||
+ | * Jan Hajič jr. - granted (early 2016) | ||
| | ||
- | === Performance tests === | + | ===== How to use cluster ===== |
- | * [[http:// | + | In this section will be explained how to use cluster properly. |
- | In the following table is the experiment conducted by Tom Kocmi. You can replicate experiment: / | + | ==== Set-up CUDA and CUDNN ==== |
- | | machine | GPU; [[https:// | + | You can add following command into your ~/.bashrc |
- | | titan-gpu | + | |
- | | twister1 | + | |
- | | twister2 | + | |
- | | kronos-dev | Tesla K40c; cc3.5 | + | |
- | | iridium | Quadro K2000; cc3.0 | | | | + | |
- | | victoria | + | |
- | | arc | GeForce GT 630; cc3.0 | + | |
- | | athena | + | |
- | === Individual acquisitions: NVIDIA Academic Hardware Grants | + | 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=$PATH:$CUDA_DIR/ | ||
+ | export LD_LIBRARY_PATH=$LD_LIBRARY_PATH: | ||
+ | export CPATH=$CUDA_DIR/ | ||
- | 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 " | + | ==== TensorFlow Environment ==== |
- | Known NVIDIA Academic Hardware Grants: | + | Majority people at UFAL use TensorFlow. To start using it you need to create python virtual environment (virtualenv or use Anaconda for it). Into the environment you must place TensorFlow. The TF is either in CPU or GPU version. |
- | | + | |
- | | + | |
- | | + | |
+ | You can use prepared environment by adding into your ~/.bashrc | ||
- | ===== Installed toolkits ===== | + | export PATH=/ |
- | //This should mention where each interesting toolkit lives (on a particular machine).// | + | And then you can activate your environment: |
- | ==== TensorFlow ==== | + | source activate tf1 |
+ | source activate tf1cpu | ||
- | [[https:// | + | This environment have TensorFlow |
- | OP: I created [[https:// | + | ==== Using cluster ==== |
- | === Select | + | Rule number one, always use the GPU queue (never log in machine by ssh). Always use qsub or qsubmit with proper arguments. |
- | Use variable CUDA_VISIBLE_DEVICES to constrain tensorflow to compute only on the selected one. For the use of first GPU use: | + | 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. |
- | < | + | |
- | To list available devices, use: | + | qrsh -q gpu.q -l gpu=1,gpu_ram=2G -pty yes bash |
- | < | + | |
+ | For running experiments you must use qsub command: | ||
- | ===== Basic commands | + | qsub -q gpu.q -l gpu=1, |
+ | |||
+ | Cleaner way to use cluster is with / | ||
+ | |||
+ | 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. | ||
+ | ==== Basic commands ==== | ||
lspci | lspci | ||
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/ | / | ||
# shows CUDA capability etc. | # shows CUDA capability etc. | ||
+ | | ||
+ | === Select GPU device === | ||
+ | |||
+ | Use variable CUDA_VISIBLE_DEVICES to constrain tensorflow to compute only on the selected one. For the use of first GPU use (GPU queue do this for you): | ||
+ | export CUDA_VISIBLE_DEVICES=0 | ||
+ | |||
+ | 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 | | ||
+ | | 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 | ||
+ | | 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 ===== |