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gpu [2019/04/17 15:13]
ufal [Servers with GPU units]
gpu [2019/10/02 14:34]
vodrazka [Servers with GPU units]
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 GPU cluster ''​gpu-ms.q''​ at Malá Strana: GPU cluster ''​gpu-ms.q''​ at Malá Strana:
  
-| machine | GPU type | GPU driver version | [[https://​en.wikipedia.org/​wiki/​CUDA#​GPUs_supported|cc]] | GPU cnt | GPU RAM (GB) | machine RAM (GB)| Comment | +| machine | GPU type | GPU driver version | [[https://​en.wikipedia.org/​wiki/​CUDA#​GPUs_supported|cc]] | GPU cnt | GPU RAM (GB) | machine RAM (GB)| 
-| dll1 |  GeForce GTX 1080 |  396.24 |  6.1 |  8 |  8 |  249 |  moved to AIC|                                                                      +| dll3 |  GeForce GTX 1080 Ti |  396.24 |  6.1 |  10 |  11.0 |  ​248.0 
-| dll2 |  GeForce GTX 1080 |  396.24 |  6.1 |  8 |  8 |  249 |                                                                          +| dll4 |  GeForce GTX 1080 Ti |  396.24 |  6.1 |  10 |  11.0 |  ​248.0 
-| dll3 |  GeForce GTX 1080 Ti |  396.24 |  6.1 |  10 |  11 |  ​249                                                                     +| dll5 |  GeForce GTX 1080 Ti |  396.24 |  6.1 |  10 |  11.0 |  ​248.0 
-| dll4 |  GeForce GTX 1080 Ti |  396.24 |  6.1 |  10 |  11 |  ​249                                                                     +| dll6 |  GeForce GTX 1080 Ti |  396.24 |  6.1 |  ​10 |  11.0 |  ​248.0 
-| dll5 |  GeForce GTX 1080 Ti |  396.24 |  6.1 |  10 |  11 |  ​249                                                                     +| dll7 |  ​GeForce RTX 2080 Ti (3x) GeForce GTX 1080 Ti (1x)|  ​418.39 |  ​7.5 (3x) 6.1 (1x) |  ​|  11.0 |  ​122.0 
-| dll6 |  GeForce GTX 1080 Ti |  396.24 |  6.1 |  ​|  11 |  ​123                                                                     ​ +| kronos |  Tesla K40c |  418.39 |  3.5 |  1 |  11.0 |  ​122.0 | 
-| dll7 |  GeForce GTX 1080 Ti |  ​396.24 |  6.1 |  ​|  11 |  ​123                                                                     ​ +                                                                        ​
-| kronos |  Tesla K40c |  418.39 |  3.5 |  1 |  11 |  ​123                                                                            +
  
 GPU cluster ''​gpu-troja.q''​ at Troja: GPU cluster ''​gpu-troja.q''​ at Troja:
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 | machine | GPU type | GPU driver version | [[https://​en.wikipedia.org/​wiki/​CUDA#​GPUs_supported|cc]] | GPU cnt | GPU RAM (GB) | machine RAM (GB)| | machine | GPU type | GPU driver version | [[https://​en.wikipedia.org/​wiki/​CUDA#​GPUs_supported|cc]] | GPU cnt | GPU RAM (GB) | machine RAM (GB)|
 | tdll1 |  Quadro P5000 |  410.48 |  6.1 |  8 |  17 |  245 |                                                                            | tdll1 |  Quadro P5000 |  410.48 |  6.1 |  8 |  17 |  245 |                                                                           
-| tdll2 |  Quadro P5000 |  410.48 |  6.1 |  8 |  17 |  ​249 |                                                                            +| tdll2 |  Quadro P5000 |  410.48 |  6.1 |  8 |  17 |  ​245 |                                                                            
-| tdll3 |  Quadro P5000 |  410.48 |  6.1 |  8 |  17 |  ​249 |                                                                           +| tdll3 |  Quadro P5000 |  410.48 |  6.1 |  8 |  17 |  ​245 |                                                                           
 | tdll4 |  Quadro P5000 |  410.48 |  6.1 |  8 |  17 |  245 |                                                                            | tdll4 |  Quadro P5000 |  410.48 |  6.1 |  8 |  17 |  245 |                                                                           
-| tdll5 |  Quadro P5000 |  410.48 |  6.1 |  8 |  17 |  ​249                                                                                                                                                     ​+| tdll5 |  Quadro P5000 |  410.48 |  6.1 |  8 |  17 |  ​245 
  
 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*'​ -l gpu=4,​gpu_cc_min3.5=1,​gpu_ram=7G -pe smp 4</​code>​   * 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*'​ -l gpu=4,​gpu_cc_min3.5=1,​gpu_ram=7G -pe smp 4</​code>​
   * 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 -l''​ is necessary**,​ otherwise the variable ''​$CUDA_VISIBLE_DEVICES''​ will not be set correctly. E.g. <​code>​qrsh -q '​gpu*'​ -l gpu=1,​gpu_ram=2G -pty yes bash -l</​code>​In general: don't reserve a GPU (as described above) without actually using it for longer time. (E.g. try separating steps which need GPU and steps which do not and execute those separately on our GPU resp. CPU cluster.) Ondřej Bojar has a script /​home/​bojar/​tools/​servers/​watch_gpus for watching reserved but unused GPU on most machines which will e-mail you, but don't rely on it only.   * For interactive jobs, you can use ''​qrsh'',​ but make sure to end your job as soon as you don't need the GPU (so don't use qrsh for long training). **Warning: ''​-pty yes bash -l''​ is necessary**,​ otherwise the variable ''​$CUDA_VISIBLE_DEVICES''​ will not be set correctly. E.g. <​code>​qrsh -q '​gpu*'​ -l gpu=1,​gpu_ram=2G -pty yes bash -l</​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 it only.
-  * 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 a different machine.+  * Note that the dll machines have typically 10 cards, but "​just"​ 250 GB RAM (DLL7 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 a different machine. 
 +  * If you know an approximate runtime of your job, please specify it with ''​-l s_rt=hh:​mm:​ss''​ - this is a soft constraint so your job won't be killed if it runs longer than specified. It will help SGE to better schedule the jobs, especially multi-gpu reservations (see ''​qconf -ssconf''​). 
  
 ===== How to use cluster ===== ===== How to use cluster =====

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