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Both sides previous revision Previous revision 2018/06/12 12:42 vodrazka [Servers with GPU units] 2018/06/12 12:08 ufal [TensorFlow Environment] 2018/05/31 10:30 popel [Set-up CUDA and CUDNN] 2018/05/30 09:57 vodrazka [Set-up CUDA and CUDNN] 2018/05/30 09:54 vodrazka [Set-up CUDA and CUDNN] 2018/04/27 18:22 popel [Servers with GPU units] 2018/04/24 10:01 vodrazka [Set-up CUDA and CUDNN] 2018/04/23 12:53 vodrazka [Set-up CUDA and CUDNN] 2018/04/16 11:35 kocmanek [Performance tests] 2018/04/16 11:29 kocmanek [Performance tests] 2018/04/16 11:18 kocmanek [Performance tests] 2018/04/13 12:50 kocmanek [Performance tests] 2018/04/12 17:11 kocmanek [Performance tests] 2018/04/12 16:02 kocmanek [Performance tests] 2018/04/12 15:17 kocmanek [Performance tests] 2018/04/09 12:45 vodrazka documenting default settings for cuda and cudnn libraries2018/04/05 14:41 vodrazka table update - Quadro P5000 for testing in twister22018/03/22 11:14 vodrazka Updated cluster GPU table2018/03/21 15:29 ufal [Servers with GPU units] 2018/02/20 17:54 kruza [Rules] typo2018/02/09 15:15 popel [Set-up CUDA and CUDNN] 2018/02/08 15:26 popel [Rules] 2018/02/08 15:21 kocmanek [Servers with GPU units] 2018/02/08 15:20 kocmanek [Rules] 2018/02/08 15:16 kocmanek [Servers with GPU units] 2018/02/08 15:10 kocmanek [Servers with GPU units] 2018/02/08 14:56 kocmanek [Servers with GPU units] 2018/01/25 17:27 popel [Using cluster] 2018/01/23 23:16 popel [Servers with GPU units] 2018/01/15 21:51 popel [Rules] 2018/01/15 10:39 popel [Set-up CUDA and CUDNN] 2018/01/08 13:25 popel dll6 now has 9 cards, so it is the same config as dll32018/01/06 21:17 popel [Rules] 2017/12/05 18:02 popel [Rules] 2017/11/23 14:15 bojar link to munin graphs2017/11/13 09:43 bojar [Rules] 2017/10/17 16:51 popel [Rules] 2017/10/17 16:43 popel [Rules] 2017/10/17 16:39 popel [Using cluster] 2017/10/17 16:38 popel [Rules] 2017/10/17 16:37 popel 2017/10/12 13:42 ufal [How to use cluster] 2017/10/11 13:07 bojar people should not set CUDA_VISIBLE_DEVICES2017/10/11 11:46 popel [Basic commands] 2017/09/11 11:26 fucik 2017/08/28 14:49 kocmanek [Set-up CUDA and CUDNN] 2017/08/28 14:46 kocmanek [Set-up CUDA and CUDNN] 2017/08/28 14:40 kocmanek [Set-up CUDA and CUDNN] 2017/08/28 14:40 kocmanek [How to use cluster] 2017/07/28 23:14 popel [GPU at ÚFAL] 2017/07/28 22:31 popel nicer table, delete outdated future plans, note on disk space Go Next revision		Previous revision 2018/06/21 10:04 vodrazka [Servers with GPU units] 2018/06/21 10:04 vodrazka [Servers with GPU units] 2018/06/12 14:17 popel 2018/06/12 13:50 machacek.dominik [Servers with GPU units] 2018/06/12 12:42 vodrazka [Servers with GPU units] 2018/06/12 12:42 vodrazka [Servers with GPU units] 2018/06/12 12:08 ufal [TensorFlow Environment] 2018/05/31 10:30 popel [Set-up CUDA and CUDNN] 2018/05/30 09:57 vodrazka [Set-up CUDA and CUDNN] 2018/05/30 09:54 vodrazka [Set-up CUDA and CUDNN] 2018/04/27 18:22 popel [Servers with GPU units] 2018/04/24 10:01 vodrazka [Set-up CUDA and CUDNN] 2018/04/23 12:53 vodrazka [Set-up CUDA and CUDNN] 2018/04/16 11:35 kocmanek [Performance tests] 2018/04/16 11:29 kocmanek [Performance tests] 2018/04/16 11:18 kocmanek [Performance tests] 2018/04/13 12:50 kocmanek [Performance tests] 2018/04/12 17:11 kocmanek [Performance tests] 2018/04/12 16:02 kocmanek [Performance tests] 2018/04/12 15:17 kocmanek [Performance tests] 2018/04/09 12:45 vodrazka documenting default settings for cuda and cudnn libraries2018/04/05 14:41 vodrazka table update - Quadro P5000 for testing in twister22018/03/22 11:14 vodrazka Updated cluster GPU table2018/03/21 15:29 ufal [Servers with GPU units] 2018/02/20 17:54 kruza [Rules] typo2018/02/09 15:15 popel [Set-up CUDA and CUDNN] 2018/02/08 15:26 popel [Rules] 2018/02/08 15:21 kocmanek [Servers with GPU units] 2018/02/08 15:20 kocmanek [Rules] 2018/02/08 15:16 kocmanek [Servers with GPU units] 2018/02/08 15:10 kocmanek [Servers with GPU units] 2018/02/08 14:56 kocmanek [Servers with GPU units] 2018/01/25 17:27 popel [Using cluster] 2018/01/23 23:16 popel [Servers with GPU units] 2018/01/15 21:51 popel [Rules] 2018/01/15 10:39 popel [Set-up CUDA and CUDNN] 2018/01/08 13:25 popel dll6 now has 9 cards, so it is the same config as dll32018/01/06 21:17 popel [Rules] 2017/12/05 18:02 popel [Rules] 2017/11/23 14:15 bojar link to munin graphs2017/11/13 09:43 bojar [Rules] 2017/10/17 16:51 popel [Rules] 2017/10/17 16:43 popel [Rules] 2017/10/17 16:39 popel [Using cluster] 2017/10/17 16:38 popel [Rules] 2017/10/17 16:37 popel 2017/10/12 13:42 ufal [How to use cluster] 2017/10/11 13:07 bojar people should not set CUDA_VISIBLE_DEVICES2017/10/11 11:46 popel [Basic commands] 2017/09/11 11:26 fucik 2017/08/28 14:49 kocmanek [Set-up CUDA and CUDNN] Go Next revision Both sides next revision
gpu [2018/01/23 23:16] popel [Servers with GPU units]		gpu [2018/02/09 15:15] popel [Set-up CUDA and CUDNN]
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	GPU cluster ''gpu.q'' at Malá Strana:		GPU cluster ''gpu.q'' at Malá Strana:
-	| machine                    | GPU type | [[https://en.wikipedia.org/wiki/CUDA#GPUs_supported|cc]] | GPUs | GPU RAM | Comment |	+	| machine                    | GPU type | [[https://en.wikipedia.org/wiki/CUDA#GPUs_supported|cc]] | GPUs | GPU RAM | RAM | Comment |
-	| iridium                    | Quadro K2000        |  cc3.0 |   1|   2 GB | driver(iridium)=367.48 |	+	| iridium                    | Quadro K2000        |  cc3.0 |   1|   2 GB | 52 GB | driver(iridium)=367.48 |
-	| titan-gpu                  | GeForce GTX Titan Z |  cc3.5 |   2|   6 GB | driver(titan-gpu)=381.22 |	+	| titan-gpu                  | GeForce GTX Titan Z |  cc3.5 |   2|   6 GB | 32 GB | driver(titan-gpu)=381.22 |
-	| twister1; twister2; kronos | Tesla K40c          |  cc3.5 |   1|  12 GB | driver(twister*)=367.48, driver(kronos)=384.81 |	+	| twister1; twister2; kronos | Tesla K40c          |  cc3.5 |   1|  12 GB | 48 GB; 48GB; 128 GB | driver(twister*)=367.48, driver(kronos)=384.81 |
-	| dll1; dll2                 | GeForce GTX 1080    |  cc6.1 |   8|   8 GB | driver(dll1)=375.66, driver(dll2)=387.26 |	+	| titan                      | GeForce GTX 1080    |  cc6.1 |   1|   8 GB | 32 GB  | driver(titan)=381.22|
-	| titan                      | GeForce GTX 1080    |  cc6.1 |   1|   8 GB | driver(titan)=381.22|	+	| dll1; dll2                 | GeForce GTX 1080    |  cc6.1 |   8|   8 GB | 250 GB | driver(dll1)=375.66, driver(dll2)=387.26 |
-	| dll4; dll5                 | GeForce GTX 1080 Ti |  cc6.1 |  10|  11 GB | driver(dll4)=375.66, driver(dll5)=384.69 |	+	| dll4; dll5                 | GeForce GTX 1080 Ti |  cc6.1 |  10|  11 GB | 250 GB | driver(dll4)=375.66, driver(dll5)=384.69 |
-	| dll3; dll6                 | GeForce GTX 1080 Ti |  cc6.1 |   9|  11 GB | driver(dll3)=375.66, driver(dll6)=384.69 |	+	| dll3                       | GeForce GTX 1080 Ti |  cc6.1 |   9|  11 GB | 250 GB | driver(dll3)=375.66 |
		+	| dll6                       | GeForce GTX 1080 Ti |  cc6.1 |   9|  11 GB | 126 GB | driver(dll6)=384.69 |
	Desktop machines:		Desktop machines:
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	* If you need more than one GPU card (on a single machine), always require as many CPU cores (''-pe smp X'') as many GPU cards you need. E.g. <code>qsub -q gpu.q -l gpu=4,gpu_cc_min3.5=1,gpu_ram=7G -pe smp 4</code> **Warning**: currently, this does not work, so you can omit the ''-pe smp X'' part. Milan Fučík is working on a fix.		* If you need more than one GPU card (on a single machine), always require as many CPU cores (''-pe smp X'') as many GPU cards you need. E.g. <code>qsub -q gpu.q -l gpu=4,gpu_cc_min3.5=1,gpu_ram=7G -pe smp 4</code> **Warning**: currently, this does not work, so you can omit the ''-pe smp X'' part. Milan Fučík is working on a fix.
	* For interactive jobs, you can use ''qrsh'', but make sure to end your job as soon as you don't need the GPU (so don't use qrsh for long training). **Warning: ''-pty yes bash'' is necessary**, otherwise the variable ''$CUDA_VISIBLE_DEVICES'' will not be set correctly. E.g. <code>qrsh -q gpu.q -l gpu=1,gpu_ram=2G -pty yes bash</code>In general: don't reserve a GPU (as described above) without actually using it for longer time. (E.g. try separating steps which need GPU and steps which do not and execute those separately on our GPU resp. CPU cluster.) Ondřej Bojar has a script /home/bojar/tools/servers/watch_gpus for watching reserved but unused GPU on most machines which will e-mail you, but don't rely on in only.		* For interactive jobs, you can use ''qrsh'', but make sure to end your job as soon as you don't need the GPU (so don't use qrsh for long training). **Warning: ''-pty yes bash'' is necessary**, otherwise the variable ''$CUDA_VISIBLE_DEVICES'' will not be set correctly. E.g. <code>qrsh -q gpu.q -l gpu=1,gpu_ram=2G -pty yes bash</code>In general: don't reserve a GPU (as described above) without actually using it for longer time. (E.g. try separating steps which need GPU and steps which do not and execute those separately on our GPU resp. CPU cluster.) Ondřej Bojar has a script /home/bojar/tools/servers/watch_gpus for watching reserved but unused GPU on most machines which will e-mail you, but don't rely on in only.
-	* Note that the dll machines have typically 10 cards, but "just" 250 GB RAM. So the expected (maximal) ''mem_free'' requirement for jobs with 1 GPU is 25GB. If your 1-GPU job takes e.g. 80 GB and you submit three such jobs on the same machine, you have effectively blocked the whole machine and seven GPUs remain unused.	+	* Note that the dll machines have typically 10 cards, but "just" 250 GB RAM (DLL6 has only 128 GB). So the expected (maximal) ''mem_free'' requirement for jobs with 1 GPU is 25GB. If your 1-GPU job takes e.g. 80 GB and you submit three such jobs on the same machine, you have effectively blocked the whole machine and seven GPUs remain unused. If you really need to submit more high-memory jobs, send each on different machine.
	===== How to use cluster =====		===== How to use cluster =====
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	When not using Theano, just Tensorflow this can be simplified to ''export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/opt/cuda-8.0/cudnn/6.0/lib64:/opt/cuda-8.0/lib64''. Note that on some machines (dll*, twister*), this is the current default even without setting LD_LIBRARY_PATH, but on other machines (kronos, titan, titan-gpu, iridium) you need to set LD_LIBRARY_PATH explicitly.		When not using Theano, just Tensorflow this can be simplified to ''export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/opt/cuda-8.0/cudnn/6.0/lib64:/opt/cuda-8.0/lib64''. Note that on some machines (dll*, twister*), this is the current default even without setting LD_LIBRARY_PATH, but on other machines (kronos, titan, titan-gpu, iridium) you need to set LD_LIBRARY_PATH explicitly.
		+
		+	TensorFlow 1.5 precompiled binaries need CUDA 9.0, for this you need to
		+
		+	export LD_LIBRARY_PATH=/opt/cuda-9.0/lib64/:/opt/cuda/cudnn/7.0/lib64/
		+
		+	You also need to use ''qsub -q gpu.q@dll[256]'' because only those machines have drivers which support CUDA 9.
		+
	==== TensorFlow Environment ====		==== TensorFlow Environment ====
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	qsubmit --gpumem=2G --queue="gpu.q" WHAT_SHOULD_BE_RUN		qsubmit --gpumem=2G --queue="gpu.q" WHAT_SHOULD_BE_RUN
-	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.	+	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 ====		==== Basic commands ====

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