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gpu [2017/03/16 16:46] kocmanek [Performance tests] |
gpu [2017/07/15 18:35] kocmanek [Performance tests] |
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===== Servers with GPU units ===== | ===== Servers with GPU units ===== | ||
- | | machine | GPU; [[https:// | + | | machine |
- | | titan-gpu | + | | titan | GeForce GTX 1080 Ti; cc6.1 | 1 | 11 GB |
- | | twister1 | + | | titan-gpu |
- | | twister2 | + | | twister1; twister2; kronos | Tesla K40c; cc3.5 | 1 | 12 GB |
- | | kronos-dev | Tesla K40c; cc3.5 | 1 | 12 GB | | | + | | iridium |
- | | iridium | + | | victoria; arc | GeForce GT 630; cc3.0 | 1 | 2 GB | desktop machine | |
- | | victoria | + | | athena |
- | | arc | GeForce GT 630; cc3.0 | 1 | 2 GB | + | | dll1; dll2 | GeForce GTX 1080; cc6.1 | 8 | 8 GB each core | | |
- | | athena | + | | dll3; dll4; dll5 | GeForce GTX 1080 Ti; cc6.1 | 10 | 11 GB each core | | |
- | | dll1 | + | |
- | | 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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* Ondřej Plátek - granted (2015) | * Ondřej Plátek - granted (2015) | ||
* Jan Hajič jr. - granted (early 2016) | * Jan Hajič jr. - granted (early 2016) | ||
- | * Jindra Helcl - planning to apply (fall 2016) | ||
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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/ | + | 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: | + | export PATH=/ |
- | | machine | Setup; CPU/GPU; [[https:// | + | And then you can activate your environment: |
- | | athena | + | |
- | | dll2 | (2 GPU) GeForce GTX 1080; cc6.1 | | + | |
- | | dll1 | (2 GPU) GeForce GTX 1080; cc6.1 | | + | |
- | | dll2 | (2 GPU) GeForce GTX 1080; cc6.1 | | + | |
- | | titan-gpu | + | |
- | | kronos-dev | Tesla K40c; cc3.5 | | + | |
- | | twister2 | + | |
- | | twister1 | + | |
- | | helena1 | + | |
- | | belzebub | + | |
- | | iridium | + | |
- | | helena7 | + | |
- | | arc | GeForce GT 630; cc3.0 | 103:42:30 | (approximated after 66 hours) | | + | |
- | | lucifer4 | + | |
- | | victoria | + | |
+ | 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:// | + | 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:// | + | qrsh -q gpu.q -l gpu=1, |
+ | |||
+ | For running experiments you must use qsub command: | ||
- | === Select GPU device === | + | qsub -q gpu.q -l gpu=1, |
+ | |||
+ | Cleaner way to use cluster is with / | ||
- | 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=" |
- | < | + | |
- | + | 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, | + | ==== Basic commands ==== |
- | < | + | |
- | + | ||
- | ===== Basic commands | + | |
lspci | lspci | ||
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/ | / | ||
# 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: | ||
+ | / | ||
+ | |||
+ | ===== Performance tests ===== | ||
+ | |||
+ | * [[http:// | ||
+ | |||
+ | In the following table is the experiment conducted by Tom Kocmi. You can replicate experiment: / | ||
+ | |||
+ | | machine | Setup; CPU/GPU; [[https:// | ||
+ | | athena | ||
+ | | dll2 | (2 GPU) GeForce GTX 1080; cc6.1 | | ||
+ | | titan | GeForce GTX 1080 Ti | | ||
+ | | dll1 | (2 GPU) GeForce GTX 1080; cc6.1 | | ||
+ | | dll2 | (2 GPU) GeForce GTX 1080; cc6.1 | | ||
+ | | titan-gpu | ||
+ | | kronos-dev | Tesla K40c; cc3.5 | | ||
+ | | twister2 | ||
+ | | twister1 | ||
+ | | helena1 | ||
+ | | belzebub | ||
+ | | iridium | ||
+ | | helena7 | ||
+ | | arc | GeForce GT 630; cc3.0 | 103:42:30 | (approximated after 66 hours) | | ||
+ | | lucifer4 | ||
+ | |||
+ | |||
+ | === Better Benchmark === | ||
+ | |||
+ | 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 increased the batch size as much as possible so the model still could fit into the GPU (the previous benchmark had batch size 20). This way the results should be more representative of the power for each GPU. | ||
+ | |||
+ | | GPU; Cuda capability | ||
+ | | Tesla K40c; cc3.5 | 12 GB | | ||
+ | | GeForce GTX 1080 Ti; cc6.1 | 11 GB | | ||
+ | | GeForce GTX 1080; cc6.1 | 8 GB | | ||
+ | | GeForce GTX 1080; cc6.1 | 8 GB | | ||
+ | | GeForce GTX Titan Z; cc3.5 | 6 GB | | ||
+ | | Quadro K2000; cc3.0 | 2 GB | | ||
===== Links ===== | ===== Links ===== |