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gpu [2017/03/16 16:46]
kocmanek [Performance tests] 		gpu [2017/05/16 10:48]
kocmanek [Performance tests]
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 ===== Servers with GPU units ===== ===== Servers with GPU units =====
  
-| machine | GPU; [[https://en.wikipedia.org/wiki/CUDA#Supported_GPUs|Capability]] [cc]  | cores | GPU RAM | Comment |+| machine    | GPU; [[https://en.wikipedia.org/wiki/CUDA#Supported_GPUs|Capability]] [cc]  | cores | GPU RAM | Comment | 
 +| titan      | GeForce GTX 1080 Ti; cc6.1 | 1 | 12 GB |        |
 | titan-gpu  | GeForce GTX Titan Z; cc3.5 | 2 | 6 GB each core |        | | titan-gpu  | GeForce GTX Titan Z; cc3.5 | 2 | 6 GB each core |        |
 | twister1   | Tesla K40c; cc3.5          | 1 | 12 GB          |        | | twister1   | Tesla K40c; cc3.5          | 1 | 12 GB          |        |
Line 12: Line 13:
 | iridium    | Quadro K2000; cc3.0        | 1 | 2 GB           |        | | iridium    | Quadro K2000; cc3.0        | 1 | 2 GB           |        |
 | victoria   | GeForce GT 630; cc3.0      | 1 | 2 GB           | Ondrej Bojar's desktop machine | | victoria   | GeForce GT 630; cc3.0      | 1 | 2 GB           | Ondrej Bojar's desktop machine |
-| arc        | GeForce GT 630; cc3.0      | 1 | 2 GB           | Ales's desktop machine |+| arc        | GeForce GT 630; cc3.0      | 1 | 2 GB           | Lucka's desktop machine |
 | athena     | GeForce GTX 1080; cc6.1    | 1 | 8 GB           | Tom's desktop machine | | athena     | GeForce GTX 1080; cc6.1    | 1 | 8 GB           | Tom's desktop machine |
-| dll1     | GeForce GTX 1080; cc6.1    | 8 | 8 GB each core |  | +| dll1       | GeForce GTX 1080; cc6.1    | 8 | 8 GB each core |  | 
-| dll2     | GeForce GTX 1080; cc6.1    | 8 | 8 GB each core |  |+| dll2       | GeForce GTX 1080; cc6.1    | 8 | 8 GB each core |  |
  
 not used at the moment: GeForce GTX 570 (from twister2) not used at the moment: GeForce GTX 570 (from twister2)
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 ===== How to use cluster ===== ===== How to use cluster =====
  
-bla bla+In this section will be explained how to use cluster properly.  
 +==== TensorFlow Environment ====
  
-===== Performance tests =====+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.
  
-* [[http://www.trustedreviews.com/nvidia-geforce-gtx-1080-review-performance-benchmarks-and-conclusion-page-2| 980 vs 1080 vs Titan X (not the Titan Z we have)]]+  pip install tensorflow 
 +  pip install tensorflow-gpu 
 +   
 +You can use prepared environment by adding into your ~/.bashrc
  
-In the following table is the experiment conducted by Tom Kocmi. You can replicate experiment: /a/merkur3/kocmanek/Projects/GPUBenchmark (you will need to prepare environment of TensorFlow11 or use my ANACONDA)+  export PATH=/a/merkur3/kocmanek/ANACONDA/bin:$PATH
  
-| machine | Setup; CPU/GPU; [[https://en.wikipedia.org/wiki/CUDA#Supported_GPUs|Capability]] [cc] | Walltime | Note | +And then you can activate your environment:
-| athena     | GeForce GTX 1080; cc6.1            |    9:55:58 | Tom's desktop  | +
-| dll2       | (2 GPU) GeForce GTX 1080; cc6.1    |   10:19:40 | with CUDA_VISIBLE_DEVICES=0 | +
-| dll1       | (2 GPU) GeForce GTX 1080; cc6.1    |   12:34:34 | Probably only one GPU was used | +
-| dll2       | (2 GPU) 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 | +
-| kronos-dev | Tesla K40c; cc3.5                  |   22:41:01 |  | +
-| twister2   | Tesla K40c; cc3.5                  |   22:43:10 |  | +
-| twister1   | Tesla K40c; cc3.5                  |   24:19:45 |  | +
-| helena1    | 16x cores CPU                      |   46:33:14 |  | +
-| belzebub   | 16x cores CPU                      |   52:36:56 |  | +
-| iridium    | Quadro K2000; cc3.0                |   59:47:58 |  | +
-| helena7    | 8x cores CPU                       |   60:39:17 |  | +
-| arc        | GeForce GT 630; cc3.0              |  103:42:30 | (approximated after 66 hours) | +
-| lucifer4   | 8x cores CPU                       |  134:41:22 |  | +
-| victoria   | GeForce GT 630; cc3.0              |        --- | never run, same GPU as Arc |+
  
 +  source activate tf1
 +  source activate tf1cpu
  
-===== Installed toolkits =====+This environment have TensorFlow 1.0 and all necessary requirements for NeuralMonkey.
  
-//This should mention where each interesting toolkit lives (on a particular machine).//+==== Using cluster ====
  
-==== TensorFlow ====+Rule number one, always use the GPU queue (never log in machine by ssh). Always use qsub or qsubmit with proper arguments.
  
-[[https://redmine.ms.mff.cuni.cz/projects/mmmt/repository/revisions/6a064187fc6959db9b77cf2d5350c5f4918a8067/entry/prepare_env.sh|This script]] installs TensorFlow 0.7.1 (and all other dependencies we need for Multimodal Translation) into `tf' and `tf-gpu' virtual environments. The GPU environment can be loaded by calling <code>source tf-gpu/bin/activate-gpu</code>+For testing and using the cluster interactively you can use qrsh (this should not be used for long running experiments since the console is not closed on the end of the experiment). Following command will assign you a GPU and creates interactive console.
  
-OP: I created [[https://gist.github.com/oplatek/323b63b8f116cd3d78c0f492f78cc289|script]] which install Tensorflow 0.8 and test it if it uses GPU. TF is installed into `user` or `global` installation either for `python3.4` or `python2.7`+  qrsh -q gpu.q -l gpu=1 -pty yes bash 
 +   
 +For running experiments you must use qsub command:
  
-=== Select GPU device ===+  qsub -q gpu.q -l gpu=1,gpu_cc_min3.5=1,gpu_ram=2G WHAT_SHOULD_BE_RUN 
 +   
 +Cleaner way to use cluster is with /home/bojar/tools/shell/qsubmit
  
-Use variable CUDA_VISIBLE_DEVICES to constrain tensorflow to compute only on the selected one. For the use of first GPU use: +  qsubmit --gpumem=2G --queue="gpu.q" WHAT_SHOULD_BE_RUN 
-<code>export CUDA_VISIBLE_DEVICES=0</code> +   
- +It is recommended to use priority -100 if you are not rushing for the results and don't need to leap over your colleagues jobs. 
-To list available devices, use: +==== Basic commands ====
-<code>/opt/cuda/samples/1_Utilities/deviceQuery/deviceQuery | grep ^Device</code> +
- +
-===== Basic commands =====+
  
   lspci   lspci
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   /usr/local/cuda/samples/1_Utilities/deviceQuery/deviceQuery   /usr/local/cuda/samples/1_Utilities/deviceQuery/deviceQuery
     # shows CUDA capability etc.     # shows CUDA capability etc.
 +    
 +=== Select GPU device ===
 +
 +Use variable CUDA_VISIBLE_DEVICES to constrain tensorflow to compute only on the selected one. For the use of first GPU use (GPU queue do this for you):
 +  export CUDA_VISIBLE_DEVICES=0
 +
 +To list available devices, use:
 +  /opt/cuda/samples/1_Utilities/deviceQuery/deviceQuery | grep ^Device
 +
 +===== Performance tests =====
 +
 +* [[http://www.trustedreviews.com/nvidia-geforce-gtx-1080-review-performance-benchmarks-and-conclusion-page-2| 980 vs 1080 vs Titan X (not the Titan Z we have)]]
 +
 +In the following table is the experiment conducted by Tom Kocmi. You can replicate experiment: /a/merkur3/kocmanek/Projects/GPUBenchmark (you will need to prepare environment of TensorFlow11 or use my ANACONDA). The benchmark uses 2GB model.
 +
 +| machine | Setup; CPU/GPU; [[https://en.wikipedia.org/wiki/CUDA#Supported_GPUs|Capability]] [cc] | Walltime | Note |
 +| athena     | GeForce GTX 1080; cc6.1            |    9:55:58 | Tom's desktop  |
 +| dll2       | (2 GPU) GeForce GTX 1080; cc6.1    |   10:19:40 | with CUDA_VISIBLE_DEVICES=0 |
 +| titan      | GeForce GTX 1080 Ti                |   11:41:08 |  |
 +| dll1       | (2 GPU) GeForce GTX 1080; cc6.1    |   12:34:34 | Probably only one GPU was used |
 +| dll2       | (2 GPU) 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 |
 +| kronos-dev | Tesla K40c; cc3.5                  |   22:41:01 |  |
 +| twister2   | Tesla K40c; cc3.5                  |   22:43:10 |  |
 +| twister1   | Tesla K40c; cc3.5                  |   24:19:45 |  |
 +| helena1    | 16x cores CPU                      |   46:33:14 |  |
 +| belzebub   | 16x cores CPU                      |   52:36:56 |  |
 +| iridium    | Quadro K2000; cc3.0                |   59:47:58 |  |
 +| helena7    | 8x cores CPU                       |   60:39:17 |  |
 +| arc        | GeForce GT 630; cc3.0              |  103:42:30 | (approximated after 66 hours) |
 +| lucifer4   | 8x cores CPU                       |  134:41:22 |  |
 +| victoria   | GeForce GT 630; cc3.0              |        --- | never run, same GPU as Arc |
 +
 +
  
 ===== Links ===== ===== Links =====

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