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gpu [2021/07/02 13:19]
vodrazka [Servers with GPU units] 		gpu [2024/10/02 15:21] (current)
popel
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 This page summarizes which UFAL servers have some GPU card, and suggests basic diagnostic commands, paths to installed tools, etc., simply everything necessary at the very beginning of using GPUs for experiments. This page summarizes which UFAL servers have some GPU card, and suggests basic diagnostic commands, paths to installed tools, etc., simply everything necessary at the very beginning of using GPUs for experiments.
 +
 +**TODO: IN 2022 MOVING FROM SGE TO SLURM** (see [[slurm|slurm guidelines]]) -- **commands like ''qsub'' and ''qstat'' will stop working!**
 +
 +**IN 2024: Newly, all the documentation is at a dedicated wiki https://ufal.mff.cuni.cz/lrc (you need to use ufal and [[internal:welcome-at-ufal#small-linguistic-password|small-linguistic password]] to access the wiki from outside of the UFAL network).***
  
 ===== Servers with GPU units ===== ===== 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 |   455.23.05 |  6.1 |  10 |  11 |  248.8 |+| dll1 |  Quadro RTX 5000 |   455.23.05 |  7.5 |  8 |  16 |  366.2 | 
 +| dll3 |  NVIDIA RTX A4000 |   510.73.08 |  8.6 |  10 |  16 |  248.8 |
 | dll4 |  GeForce GTX 1080 Ti |   455.23.05 |  6.1 |  10 |  11 |  248.8 | | dll4 |  GeForce GTX 1080 Ti |   455.23.05 |  6.1 |  10 |  11 |  248.8 |
 | dll5 |  GeForce GTX 1080 Ti |   455.23.05 |  6.1 |  10 |  11 |  248.8 | | dll5 |  GeForce GTX 1080 Ti |   455.23.05 |  6.1 |  10 |  11 |  248.8 |
-| dll6 |  GeForce GTX 1080 Ti |   455.23.05 |  6.1 |  8 |  11 |  248.8 | +| dll6 |  NVIDIA RTX A4000 |   510.73.08 |  8.6 |  8 |  16 |  248.8 | 
-| dll7 |  GeForce RTX 2080 Ti |   455.23.05 |  7.5 |  8 |  11 |  248.8 |+| dll7 |  NVIDIA RTX A4000 |   510.73.08 |  8.6 |  8 |  16 |  248.8 | 
 +| dll8 |  Quadro RTX 5000 |   455.23.05 |  7.5 |  8 |  16 |  366.2 |
 | dll9 |  GeForce RTX 3090 |   455.23.05 |  8.6 |  4 |  25 |  183.0 | | dll9 |  GeForce RTX 3090 |   455.23.05 |  8.6 |  4 |  25 |  183.0 |
 | dll10 |  GeForce RTX 3090 |   455.23.05 |  8.6 |  4 |  25 |  183.0 | | dll10 |  GeForce RTX 3090 |   455.23.05 |  8.6 |  4 |  25 |  183.0 |
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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 |   455.23.05 |  6.1 |  8 |  17 |  245.0 | +| tdll1 |  Quadro P5000 |   455.23.05 |  6.1 |  8 |  16 |  245.0 | 
-| tdll2 |  Quadro P5000 |   455.23.05 |  6.1 |  8 |  17 |  245.0 | +| tdll2 |  Quadro P5000 |   455.23.05 |  6.1 |  8 |  16 |  245.0 | 
-| tdll3 |  Quadro P5000 |   455.23.05 |  6.1 |  8 |  17 |  245.0 | +| tdll3 |  Quadro P5000 |   455.23.05 |  6.1 |  8 |  16 |  245.0 | 
-| tdll4 |  Quadro P5000 |   455.23.05 |  6.1 |  8 |  17 |  245.0 | +| tdll4 |  Quadro P5000 |   455.23.05 |  6.1 |  8 |  16 |  245.0 | 
-| tdll5 |  Quadro P5000 |   455.23.05 |  6.1 |  8 |  17 |  245.0 |+| tdll5 |  Quadro P5000 |   455.23.05 |  6.1 |  8 |  16 |  245.0 |
  
 Desktop machines: Desktop machines:
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 You need to set library path from your ''~/.bashrc'': You need to set library path from your ''~/.bashrc'':
  
-  CUDA_version=10.1 +  CUDA_version=11.1 
-  CUDNN_version=7.6+  CUDNN_version=8.0
   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=$LD_LIBRARY_PATH:/opt/cuda/10.1/lib64:/opt/cuda/10.1/cudnn/7.6/lib64''.+  * When not using Theano, just Tensorflow this can be simplified to ''export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/opt/cuda/10.1/lib64:/opt/cuda/11.1/cudnn/8.0/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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 This environment have TensorFlow 1.8.0 and all necessary requirements for NeuralMonkey. This environment have TensorFlow 1.8.0 and all necessary requirements for NeuralMonkey.
  
-==== Pytorch Environment ====+==== PyTorch Environment ====
  
-If you want to use pytorch, there is a ready-made environment in+Install PyTorch following the instructions on https://pytorch.org.
  
-  /home/hajicj/anaconda3/envs/pytorch/bin +At the time of writing, the recommended setup for CUDA 11.1 (supported by all GPU nodes) is:
-   +
-It does rely on the CUDA and CuDNN setup above.+
  
 +   pip3 install torch==1.10.0+cu111 torchvision==0.11.1+cu111 torchaudio==0.10.0+cu111 -f https://download.pytorch.org/whl/cu111/torch_stable.html
 ==== Using cluster ==== ==== Using cluster ====
  

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