Differences

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--- gpu [2018/02/08 15:26]
popel [Rules]
+++ gpu [2018/05/30 09:54]
vodrazka [Set-up CUDA and CUDNN]
@@ 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 |
+| titan-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 ''$CUDA_VISIBLE_DEVICES'' will not be set correctly. E.g. <code>qrsh -q gpu.q -l gpu=1,gpu_ram=2G -pty yes bash</code>In general: don't reserve a GPU (as described above) without actually using it for longer time. (E.g. try separating steps which need GPU and steps which do not and execute those separately on our GPU resp. CPU cluster.) Ondřej Bojar has a script /home/bojar/tools/servers/watch_gpus for watching reserved but unused GPU on most machines which will e-mail you, but don't rely on in only.
@@ Line 54: / Line 57: @@
 When not using Theano, just Tensorflow this can be simplified to ''export LD_LIBRARY_PATH=$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.
+**THE NEW CLUSTER (SGE 8.1.9)**
+Multiple versions of ''cuda'' can be accessed in ''/opt/cuda''. **Compared to the old cluster there is a difference in setting the CUDA_DIR_OPT variable!!**
+You need to set library path from your ''~/.bashrc'':
+  CUDNN_version=7.0
+  CUDA_version=9.0
+  CUDA_DIR_OPT=/opt/cuda/$CUDA_version
+  if [ -d "$CUDA_DIR_OPT" ] ; then
+    CUDA_DIR=$CUDA_DIR_OPT
+    export CUDA_HOME=$CUDA_DIR
+    export THEANO_FLAGS="cuda.root=$CUDA_HOME,device=gpu,floatX=float32"
+    export PATH=$PATH:$CUDA_DIR/bin
+    export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CUDA_DIR/cudnn/$CUDNN_version/lib64:$CUDA_DIR/lib64
+    export CPATH=$CUDA_DIR/cudnn/$CUDNN_version/include:$CPATH
+  fi
+  * When not using Theano, just Tensorflow this can be simplified to ''export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/opt/cuda/9.0/cudnn/7.0/lib64:/opt/cuda/8.0/lib64''.
+  * There is no default and you always need to set ''LD_LIBRARY_PATH'' to suit explicitly.
+  * Note that ''cudnn'' library is compiled for specific version of ''cuda''. If you need specific version of ''cudnn'', you can look in ''/opt/cuda/$CUDA_version/cudnn/'' whether it is available for given ''$CUDA_version''.
 ==== TensorFlow Environment ====
@@ Line 125: / Line 163: @@
 | titan      | GeForce GTX 1080 Ti                |   10:45:11 | (new result with correct CUDA version) |
 | dll1       | (2 GPU) GeForce GTX 1080; cc6.1    |   12:34:34 | Probably only one GPU was used |
+| twister2   | Quadro P5000                       |   13:19:00 |  |
 | dll2       | GeForce GTX 1080; cc6.1            |   13:01:05 | Only one GPU was used |
 | titan-gpu  | (2 GPU) GeForce GTX Titan Z; cc3.5 |   16:05:24 | Probably only one GPU was used |
@@ Line 142: / Line 181: @@
 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.
-| GPU; Cuda capability       | GPU RAM |  Walltime | Batch size | Machine |
+| GPU; Cuda capability       | GPU RAM |  Walltime | Batch size | Machine   |
-| Tesla K40c; cc3.5          |   12 GB |           |        |  |
+| GeForce GTX 1080 Ti; cc6.1 |   11 GB |  00:55:56 |       2300 | dll5      |
-| 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      |
-| GeForce GTX 1080; cc6.1    |    8 GB |  01:10:57 |       1700 | dll1 |
+| Quadro P5000               |   16 GB |  01:17:00 |       3400 | twister2  |
 | 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 |
+| Quadro K2000; cc3.0        |    2 GB |  28:15:26 |         50 | iridium   |
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