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gpu [2019/11/08 09:57]
vodrazka [Servers with GPU units]
gpu [2020/03/14 15:49] (current)
popel [Servers with GPU units]
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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)|
-| dll3 |  GeForce GTX 1080 Ti |  ​396.24 |  6.1 |  10 |  11.0 |  248.0 | +| dll3 |  GeForce GTX 1080 Ti |  ​440.33 |  6.1 |  10 |  11.0 |  248.0 | 
-| dll4 |  GeForce GTX 1080 Ti |  ​396.24 |  6.1 |  10 |  11.0 |  248.0 | +| dll4 |  GeForce GTX 1080 Ti |  ​440.33 |  6.1 |  10 |  11.0 |  248.0 | 
-| dll5 |  GeForce GTX 1080 Ti |  ​396.24 |  6.1 |  10 |  11.0 |  248.0 | +| dll5 |  GeForce GTX 1080 Ti |  ​440.33 |  6.1 |  10 |  11.0 |  248.0 | 
-| dll6 |  GeForce GTX 1080 Ti |  ​396.24 |  6.1 |  ​10 |  11.0 |  248.0 | +| dll6 |  GeForce GTX 1080 Ti |  ​440.33 |  6.1 |  ​|  11.0 |  248.0 | 
-| dll7 |  GeForce RTX 2080 Ti |  418.39 |  7.5 |   ​8 |  11.0 |  248.0 | +| dll7 |  GeForce RTX 2080 Ti |  418.39 |  7.5 |  8 |  11.0 |  248.0 |                                                                 ​ 
-| kronos |  Tesla K40c |  418.39 |  3.5 |  1 |  11.0 |  122.0 | +| kronos |  Tesla K40c |  418.39 |  3.5 |  1 |  11.0 |  122.0 |                                                                                                                                               ​
-                                                                        ​+
  
 GPU cluster ''​gpu-troja.q''​ at Troja: GPU cluster ''​gpu-troja.q''​ at Troja:
  
 | 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 |  17.0 |  245.0                                                                       ​ 
-| tdll2 |  Quadro P5000 |  410.48 |  6.1 |  8 |  17 |  245 |                                                                           ​ +| tdll2 |  Quadro P5000 |  410.48 |  6.1 |  8 |  17.0 |  245.0                                                                       ​ 
-| tdll3 |  Quadro P5000 |  410.48 |  6.1 |  8 |  17 |  245 |                                                                           ​ +| tdll3 |  Quadro P5000 |  410.48 |  6.1 |  8 |  17.0 |  245.0                                                                       ​ 
-| tdll4 |  Quadro P5000 |  410.48 |  6.1 |  8 |  17 |  245 |                                                                           ​ +| tdll4 |  Quadro P5000 |  410.48 |  6.1 |  8 |  17.0 |  245.0                                                                       ​ 
-| tdll5 |  Quadro P5000 |  410.48 |  6.1 |  8 |  17 |  245 | +| tdll5 |  Quadro P5000 |  410.48 |  6.1 |  8 |  17.0 |  245.0 
  
 Desktop machines: Desktop machines:
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     * **Note that you need to use ''​h_data''​ instead of ''​h_vmem''​ for GPU jobs.** CUDA driver allocates a lot of "​unused"​ virtual memory (tens of GB per card), which is counted in ''​h_vmem'',​ but not in ''​h_data''​. All usual allocations (''​malloc'',​ ''​new'',​ Python allocations) seem to be included in ''​h_data''​.     * **Note that you need to use ''​h_data''​ instead of ''​h_vmem''​ for GPU jobs.** CUDA driver allocates a lot of "​unused"​ virtual memory (tens of GB per card), which is counted in ''​h_vmem'',​ but not in ''​h_data''​. All usual allocations (''​malloc'',​ ''​new'',​ Python allocations) seem to be included in ''​h_data''​.
   * Always specify the number of GPU cards (e.g. ''​gpu=1''​),​ the minimal Cuda capability you need (e.g. ''​gpu_cc_min3.5=1''​) and your GPU memory requirements (e.g. ''​gpu_ram=2G''​). Thus e.g. <​code>​qsub -q '​gpu*'​ -l gpu=1,​gpu_cc_min3.5=1,​gpu_ram=2G</​code>​   * Always specify the number of GPU cards (e.g. ''​gpu=1''​),​ the minimal Cuda capability you need (e.g. ''​gpu_cc_min3.5=1''​) and your GPU memory requirements (e.g. ''​gpu_ram=2G''​). Thus e.g. <​code>​qsub -q '​gpu*'​ -l gpu=1,​gpu_cc_min3.5=1,​gpu_ram=2G</​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>​+  * If you need more than one GPU card (on a single machine), always require ​at least 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 (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.   * 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.
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 You need to set library path from your ''​~/​.bashrc'':​ You need to set library path from your ''​~/​.bashrc'':​
  
-  ​CUDNN_version=7.0 +  ​CUDA_version=10.1 
-  ​CUDA_version=9.0+  ​CUDNN_version=7.6
   CUDA_DIR_OPT=/​opt/​cuda/​$CUDA_version   CUDA_DIR_OPT=/​opt/​cuda/​$CUDA_version
   if [ -d "​$CUDA_DIR_OPT"​ ] ; then   if [ -d "​$CUDA_DIR_OPT"​ ] ; then
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   fi   fi
  
-  * When not using Theano, just Tensorflow this can be simplified to ''​export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/​opt/​cuda/​9.0/​lib64:/​opt/​cuda/​9.0/cudnn/7.0/​lib64''​.+  * When not using Theano, just Tensorflow this can be simplified to ''​export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/​opt/​cuda/​10.1/​lib64:/​opt/​cuda/​10.1/cudnn/7.6/​lib64''​.
   * Note that the ''​cudnn''​ library is compiled for specific version of ''​cuda''​. If you need specific version of ''​cudnn'',​ you can look in ''/​opt/​cuda/​$CUDA_version/​cudnn/''​ whether it is available for a given ''​$CUDA_version''​.   * Note that the ''​cudnn''​ library is compiled for specific version of ''​cuda''​. If you need specific version of ''​cudnn'',​ you can look in ''/​opt/​cuda/​$CUDA_version/​cudnn/''​ whether it is available for a given ''​$CUDA_version''​.
  

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