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gpu [2020/09/14 15:29]
fucik [Rules] 		gpu [2022/09/20 09:39] (current)
rosa [GPU at ÚFAL] todo slurm
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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!**
 ===== Servers with GPU units ===== ===== Servers with GPU units =====
 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)| | machine | GPU type | GPU driver version | [[https://en.wikipedia.org/wiki/CUDA#GPUs_supported|cc]] | GPU cnt | GPU RAM (GB) | machine RAM (GB)|
-| dll1 | Quadro RTX 5000      |  440.33 |  7.5 |   8 |  16.0 |  366.0 |  +| dll1 |  Quadro RTX 5000 |   455.23.05 |  7.5 |  8 |  16 |  366.2 | 
-| dll3 |  GeForce GTX 1080 Ti |  440.33 |  6.1 |  10 |  11.0 |  248.0 | +| dll3 |  NVIDIA RTX A4000 |   510.73.08 |  8.6 |  10 |  16 |  248.8 | 
-| dll4 |  GeForce GTX 1080 Ti |  440.33 |  6.1 |  10 |  11.0 |  248.0 | +| dll4 |  GeForce GTX 1080 Ti |   455.23.05 |  6.1 |  10 |  11 |  248.8 | 
-| dll5 |  GeForce GTX 1080 Ti |  440.33 |  6.1 |  10 |  11.0 |  248.0 | +| dll5 |  GeForce GTX 1080 Ti |   455.23.05 |  6.1 |  10 |  11 |  248.8 | 
-| dll6 |  GeForce GTX 1080 Ti |  440.33 |  6.1 |   9 |  11.0 |  248.0 | +| dll6 |  NVIDIA RTX A4000 |   510.73.08 |  8.6 |  8 |  16 |  248.8 | 
-| dll7 |  GeForce RTX 2080 Ti |  418.39 |  7.5 |   8 |  11.0 |  248.0 |                                                                  +| dll7 |  NVIDIA RTX A4000 |   510.73.08 |  8.6 |  8 |  16 |  248.8 | 
-| dll8 | Quadro RTX 5000      |  440.33 |  7.5 |   8 |  16.0 |  366.0 |  +| dll8 |  Quadro RTX 5000 |   455.23.05 |  7.5 |  8 |  16 |  366.2 | 
-| kronos |  Tesla K40c        |  418.39 |  3.5 |   1 |  11.0 |  122.0 |                                                                                                                                               +| 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 |
  
 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 |  16.0 |  245.0 |                                                                        +| tdll1 |  Quadro P5000 |   455.23.05 |  6.1 |  8 |  16 |  245.0 | 
-| tdll2 |  Quadro P5000 |  410.48 |  6.1 |  8 |  16.0 |  245.0 |                                                                        +| tdll2 |  Quadro P5000 |   455.23.05 |  6.1 |  8 |  16 |  245.0 | 
-| tdll3 |  Quadro P5000 |  410.48 |  6.1 |  8 |  16.0 |  245.0 |                                                                        +| tdll3 |  Quadro P5000 |   455.23.05 |  6.1 |  8 |  16 |  245.0 | 
-| tdll4 |  Quadro P5000 |  410.48 |  6.1 |  8 |  16.0 |  245.0 |                                                                        +| tdll4 |  Quadro P5000 |   455.23.05 |  6.1 |  8 |  16 |  245.0 | 
-| tdll5 |  Quadro P5000 |  410.48 |  6.1 |  8 |  16.0 |  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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   watch nvidia-smi   watch nvidia-smi
     # For monitoring GPU activity in a separate terminal (thanks to Jindrich Libovicky for this!)     # For monitoring GPU activity in a separate terminal (thanks to Jindrich Libovicky for this!)
 +    # You can also use nvidia-smi -l TIME
   nvcc --version   nvcc --version
     # this should tell CUDA version     # this should tell CUDA version

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