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gpu [2017/03/16 17:01] kocmanek [How to use cluster] |
gpu [2017/10/11 11:46] popel [Basic commands] |
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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 |
- | | kronos-dev | Tesla K40c; cc3.5 | + | | dll1; dll2 | GeForce GTX 1080 |
- | | iridium | + | | titan |
- | | victoria | + | | dll3; dll4; dll5 | GeForce GTX 1080 Ti | |
- | | arc | GeForce GT 630; cc3.0 | 1 | 2 GB | Ales's desktop machine | | + | | dll6 | GeForce GTX 1080 Ti | |
- | | 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: |
- | All machines have CUDA8.0 and should support both Theano and TensorFlow. | + | | 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 Troja servers won't get any GPUs (the only option would be [[http:// | + | All machines |
- | * 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 | + | |
- | * Yes, there are grant applications under review which include rack machines | + | |
+ | === Disk space === | ||
+ | All the GPU machines are at Malá Strana (not at Troja), so you should not use ''/ | ||
+ | - ''/ | ||
+ | - ''/ | ||
+ | - ''/ | ||
+ | - ''/ | ||
=== Individual acquisitions: | === Individual acquisitions: | ||
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* Ondřej Plátek - granted (2015) | * Ondřej Plátek - granted (2015) | ||
* Jan Hajič jr. - granted (early 2016) | * Jan Hajič jr. - granted (early 2016) | ||
- | * Jindra Helcl - planning to apply (fall 2016) | ||
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===== How to use cluster ===== | ===== How to use cluster ===== | ||
- | In this section will be explained how to use cluster properly. | + | In this section will be explained how to use cluster properly. |
+ | |||
+ | ==== Set-up CUDA and CUDNN ==== | ||
+ | |||
+ | You can add following command into your ~/.bashrc | ||
+ | |||
+ | 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 | ||
==== TensorFlow Environment ==== | ==== TensorFlow 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. | ||
+ | |||
+ | 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, | ||
+ | | ||
+ | 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=" | ||
+ | | ||
+ | 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 | ||
+ | # is any such hardware there? | ||
+ | nvidia-smi | ||
+ | # more details, incl. running processes on the GPU | ||
+ | # nvidia-* are typically located in /usr/bin | ||
+ | watch nvidia-smi | ||
+ | # For monitoring GPU activity in a separate terminal (thanks to Jindrich Libovicky for this!) | ||
+ | nvcc --version | ||
+ | # this should tell CUDA version | ||
+ | # nvcc is typically installed in / | ||
+ | theano-test | ||
+ | # dela to vubec neco uzitecneho? :-) | ||
+ | # theano-* are typically located in / | ||
+ | / | ||
+ | # shows CUDA capability etc. | ||
+ | ssh dll1; ~popel/ | ||
+ | # who occupies which card on a given machine | ||
+ | | ||
+ | === 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 ===== | ===== Performance tests ===== | ||
* [[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 | | ||
| 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 | ||
- | ===== Installed toolkits ===== | + | === Second Benchmark |
- | //This should mention where each interesting toolkit lives (on a particular machine).// | + | 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. |
- | ==== TensorFlow ==== | + | | GPU; Cuda capability |
- | + | | Tesla K40c; cc3.5 | 12 GB | | |
- | [[https:// | + | | 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 | | |
- | OP: I created [[https:// | + | | 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 | | |
- | === 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: | + | |
- | < | + | |
- | + | ||
- | To list available devices, use: | + | |
- | < | + | |
- | + | ||
- | ===== Basic commands ===== | + | |
- | + | ||
- | lspci | + | |
- | # is any such hardware there? | + | |
- | nvidia-smi | + | |
- | # more details, incl. running processes on the GPU | + | |
- | # nvidia-* are typically located in /usr/bin | + | |
- | watch nvidia-smi | + | |
- | # For monitoring GPU activity in a separate terminal (thanks to Jindrich Libovicky for this!) | + | |
- | nvcc --version | + | |
- | | + | |
- | # nvcc is typically installed in / | + | |
- | theano-test | + | |
- | # dela to vubec neco uzitecneho? | + | |
- | | + | |
- | / | + | |
- | # shows CUDA capability etc. | + | |
===== Links ===== | ===== Links ===== |