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--- gpu [2018/02/09 15:15]
popel [Set-up CUDA and CUDNN]
+++ gpu [2018/04/09 12:45]
vodrazka documenting default settings for cuda and cudnn libraries
@@ Line 5: / Line 5: @@
 ===== Servers with GPU units =====
 GPU cluster ''gpu.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)|
-| machine                    | GPU type | [[https://en.wikipedia.org/wiki/CUDA#GPUs_supported|cc]] | GPUs | GPU RAM | RAM | Comment |
+| dll1 |  GeForce GTX 1080 |  384.69 |  6.1 |  8 |  8 |  250 |
-| iridium                    | Quadro K2000        |  cc3.0 |   1|   2 GB | 52 GB | driver(iridium)=367.48 |
+| dll2 |  GeForce GTX 1080 |  387.34 |  6.1 |  8 |  8 |  250 |
-| titan-gpu                  | GeForce GTX Titan Z |  cc3.5 |   2|   6 GB | 32 GB | driver(titan-gpu)=381.22 |
+| dll3 |  GeForce GTX 1080 Ti |  375.66 |  6.1 |  9 |  11 |  250 |
-| twister1; twister2; kronos | Tesla K40c          |  cc3.5 |   1|  12 GB | 48 GB; 48GB; 128 GB | driver(twister*)=367.48, driver(kronos)=384.81 |
+| dll4 |  GeForce GTX 1080 Ti |  375.66 |  6.1 |  10 |  11 |  250 |
-| titan                      | GeForce GTX 1080    |  cc6.1 |   1|   8 GB | 32 GB  | driver(titan)=381.22|
+| dll5 |  GeForce GTX 1080 Ti |  384.69 |  6.1 |  10 |  11 |  250 |
-| dll1; dll2                 | GeForce GTX 1080    |  cc6.1 |   8|   8 GB | 250 GB | driver(dll1)=375.66, driver(dll2)=387.26 |
+| dll6 |  GeForce GTX 1080 Ti |  384.69 |  6.1 |  9 |  11 |  122 |
-| dll4; dll5                 | GeForce GTX 1080 Ti |  cc6.1 |  10|  11 GB | 250 GB | driver(dll4)=375.66, driver(dll5)=384.69 |
+| gpu |  GeForce GTX TITAN Z |  381.22 |  3.5 |  2 |  6 |  31 |
-| dll3                       | GeForce GTX 1080 Ti |  cc6.1 |   9|  11 GB | 250 GB | driver(dll3)=375.66 |
+| iridium |  Quadro K2000 |  367.48 |  3.0 |  1 |  2 |  504 |
-| dll6                       | GeForce GTX 1080 Ti |  cc6.1 |   9|  11 GB | 126 GB | driver(dll6)=384.69 |
+| kronos |  GeForce GTX 1080 Ti |  384.81 |  6.1 |  1 |  11 |  125 |
+| titan |  GeForce GTX 1080 |  381.22 |  6.1 |  1 |  8 |  31 |
+| twister1 |  Tesla K40c |  367.48 |  3.5 |  1 |  11 |  47 |
+| twister2 |  Quadro P5000 |  367.48 |  6.1 |  1 |  17 |  47 |
 Desktop machines:
@@ Line 30: / Line 33: @@
   * First, read [[internal:Linux network]] and [[:Grid]].
   * All the rules from [[:Grid]] apply, even more strictly than for CPU because there are too many GPU users and not as many GPUs available. So as a reminder: always use GPUs via ''qsub'' (or ''qrsh''), never via ''ssh''. You can ssh to any machine e.g. to run ''nvidia-smi'' or ''htop'', but not to start computing on GPU. Don't forget to specify you RAM requirements with e.g. ''-l mem_free=8G,act_mem_free=8G,h_vmem=12G''.
-  * 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 you GPU memory requirements (e.g. ''gpu_ram=2G''). Thus e.g. <code>qsub -q gpu.q -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.q -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.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.
@@ Line 60: / Line 63: @@
 You also need to use ''qsub -q gpu.q@dll[256]'' because only those machines have drivers which support CUDA 9.
+**Testing configuration (so far on twister2 only)**
+Multiple versions of ''cuda'' and ''cudnn'' can be accessed in ''/opt''.
+System default version for both libraries is configured in ''/etc/ld.so.conf.d/cuda.conf'' as:
+  /opt/cuda/lib64
+  /opt/cuda/extras/CUPTI/lib64
+  /opt/cudnn/lib64
+Actual version used depends on the link in ''/opt''. For example:
+  ls -l /opt
+  ...
+  lrwxrwxrwx 1 root root  8 dub  9 12:30 cuda -> cuda-9.0
+  lrwxrwxrwx 1 root root  9 dub  9 12:32 cudnn -> cudnn-7.1
+  ...
+This means that the system is using ''cuda 9.0'' and ''cudnn 7.1''.
+If system default version does not work for you, you can set library path from your ''~/.bashrc''.
 ==== TensorFlow Environment ====

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