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gpu [2018/06/12 13:50]
machacek.dominik [Servers with GPU units]
gpu [2018/06/12 14:17] (current)
popel
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   * First, read [[internal:​Linux network]] and [[:Grid]].   * 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''​.   * 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 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>​ +  * 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-ms.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. +  * 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-ms.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 ''​$CUDA_VISIBLE_DEVICES''​ will not be set correctly. E.g. <​code>​qrsh -q gpu.q -l gpu=1,​gpu_ram=2G -pty yes bash</​code>​In general: don't reserve a GPU (as described above) without actually using it for longer time. (E.g. try separating steps which need GPU and steps which do not and execute those separately on our GPU resp. CPU cluster.) Ondřej Bojar has a script /​home/​bojar/​tools/​servers/​watch_gpus for watching reserved but unused GPU on most machines which will e-mail you, but don't rely on in only. +  * For interactive jobs, you can use ''​qrsh'',​ but make sure to end your job as soon as you don't need the GPU (so don't use qrsh for long training). **Warning: ''​-pty yes bash''​ is necessary**,​ otherwise the variable ''​$CUDA_VISIBLE_DEVICES''​ will not be set correctly. E.g. <​code>​qrsh -q gpu-ms.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. 
-  * Note that the dll machines have typically 10 cards, but "​just"​ 250 GB RAM (DLL6 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 different machine.+  * Note that the dll machines have typically 10 cards, but "​just"​ 250 GB RAM (DLL6 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 different machine.
  
 ===== How to use cluster ===== ===== How to use cluster =====
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 ==== Set-up CUDA and CUDNN ==== ==== Set-up CUDA and CUDNN ====
  
-You should add the following commands into your ~/.bashrc +Multiple versions of ''​cuda''​ can be accessed in ''/​opt/​cuda''​. ​
- +
-  CUDNN_version=6.0 +
-  CUDA_version=8.0 +
-  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-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'':​ You need to set library path from your ''​~/​.bashrc'':​
  
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   * 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/​9.0/​lib64:/​opt/​cuda/​9.0/​cudnn/​7.0/​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/​$CUDA_version/​cudnn/''​ whether it is available for given ''​$CUDA_version''​.
-  * There is no default and you always need to set ''​LD_LIBRARY_PATH''​ 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 ==== ==== TensorFlow Environment ====
  
-Majority ​people at UFAL use TensorFlow. To start using it you need to create python virtual environment (virtualenv ​or use Anaconda for it). Into the environment you must place TensorFlow. The TF is either in CPU or GPU version.+Many people at UFAL use TensorFlow. To start using it it is recommended ​to create ​a [[python|Python ​virtual environment]] (or use Anaconda for it). Into the environment you must place TensorFlow. The TF is either in CPU or GPU version.
  
   pip install tensorflow   pip install tensorflow
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 As an alternative to ''​qsub'',​ you can use /​home/​bojar/​tools/​shell/​qsubmit As an alternative to ''​qsub'',​ you can use /​home/​bojar/​tools/​shell/​qsubmit
  
-  qsubmit --gpumem=2G --queue="​gpu.q"​ WHAT_SHOULD_BE_RUN+  qsubmit --gpumem=2G --queue="​gpu-ms.q" WHAT_SHOULD_BE_RUN
   ​   ​
 It is recommended to use priority lower than the default -100 if you are not rushing for the results and don't need to leap over your colleagues jobs. It is recommended to use priority lower than the default -100 if you are not rushing for the results and don't need to leap over your colleagues jobs.
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 ==== Individual acquisitions:​ NVIDIA Academic Hardware Grants ==== ==== Individual acquisitions:​ NVIDIA Academic Hardware Grants ====
  
-There is an easy way to get one high-end GPU: [[https://​developer.nvidia.com/​academic_gpu_seeding|ask NVIDIA for an Academic Hardware Grant]]. All it takes is writing a short grant application (at most ~2 hrs of work from scratch; if you have a GAUK, ~15 minutes of copy-pasting). Due to the GPU housing issues (mainly rack space and cooling), Milan F. said we should request the Tesla-line cards (2017 check with Milan about this issue). If you want to have a look at an application,​ feel free to ask at hajicj@ufal.mff.cuni.cz :)+There is an easy way to get one high-end GPU: [[https://​developer.nvidia.com/​academic_gpu_seeding|ask NVIDIA for an Academic Hardware Grant]]. All it takes is writing a short grant application (at most ~2 hrs of work from scratch; if you have a GAUK, ~15 minutes of copy-pasting). Due to the GPU housing issues (mainly rack space and cooling), Milan F. said we should request the Tesla-line cards (2017check with Milan about this issue). If you want to have a look at an application,​ feel free to ask at hajicj@ufal.mff.cuni.cz :)
  
 Take care, however, to coordinate the grant applications a little, so that not too many arrive from UFAL within a short time: these grants are explicitly //not// intended to build GPU clusters, they are "​seeding"​ grants meant for researchers to try out GPUs (and fall in love with them, and buy a cluster later). If you are planning to submit the hardware grant, have submitted one, or have already been awarded one, please add yourself here. Take care, however, to coordinate the grant applications a little, so that not too many arrive from UFAL within a short time: these grants are explicitly //not// intended to build GPU clusters, they are "​seeding"​ grants meant for researchers to try out GPUs (and fall in love with them, and buy a cluster later). If you are planning to submit the hardware grant, have submitted one, or have already been awarded one, please add yourself here.

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