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gpu [2017/07/16 01:04] kocmanek [Performance tests] |
gpu [2017/10/12 13:42] ufal [How to use cluster] |
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===== Servers with GPU units ===== | ===== Servers with GPU units ===== | ||
+ | GPU cluster '' | ||
- | | machine | + | | machine |
- | | titan | + | | iridium |
- | | titan-gpu | + | | titan-gpu |
- | | twister1; twister2; kronos | Tesla K40c; cc3.5 | 1 | 12 GB | + | | twister1; twister2; kronos | Tesla K40c |
- | | iridium | + | | dll1; dll2 | GeForce GTX 1080 |
- | | victoria; arc | GeForce GT 630; cc3.0 | 1 | 2 GB | desktop machine | + | | titan |
- | | athena | + | | dll3; dll4; dll5 | GeForce GTX 1080 Ti | |
- | | dll1; dll2 | GeForce GTX 1080; cc6.1 | 8 | + | | dll6 | GeForce GTX 1080 Ti | |
- | | dll3; dll4; dll5 | GeForce GTX 1080 Ti; cc6.1 | 10 | 11 GB each core | | | + | |
- | not used at the moment: GeForce GTX 570 (from twister2) | + | Desktop machines: |
- | All machines have CUDA8.0 and should support both Theano and TensorFlow. | + | | machine |
+ | | victoria; arc | GeForce GT 630 | cc3.0 | 1 | 2 GB | desktop machine | | ||
+ | | athena | ||
- | Summary of future plans: | + | Not used at the moment: GeForce GTX 570 (from twister2) |
- | * Current Troja servers won't get any GPUs (the only option would be [[http:// | + | All machines |
- | * The old Quadro K2000 we have is a much more low end piece, so we can't test is in Troja. | + | |
- | * There is MetaCentrum which also has GPUs, so testing can be done there. | + | |
- | * It is impossible (wasteful in terms of space and forbidden by a dean regulation) to put non-rack machines to our servers rooms. So we won't be buying | + | |
- | * Yes, there are grant applications under review which include rack machines | + | |
+ | === Disk space === | ||
+ | All the GPU machines are at Malá Strana (not at Troja), so you should not use ''/ | ||
+ | - ''/ | ||
+ | - ''/ | ||
+ | - ''/ | ||
+ | - ''/ | ||
=== Individual acquisitions: | === Individual acquisitions: | ||
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In this section will be explained how to use cluster properly. | In this section will be explained how to use cluster properly. | ||
+ | |||
+ | ==== Set-up CUDA and CUDNN ==== | ||
+ | |||
+ | You can add following command into your ~/.bashrc | ||
+ | |||
+ | CUDNN_version=6.0 | ||
+ | CUDA_version=8.0 | ||
+ | CUDA_DIR_OPT=/ | ||
+ | if [ -d " | ||
+ | CUDA_DIR=$CUDA_DIR_OPT | ||
+ | export CUDA_HOME=$CUDA_DIR | ||
+ | export THEANO_FLAGS=" | ||
+ | export PATH=$PATH: | ||
+ | export LD_LIBRARY_PATH=$LD_LIBRARY_PATH: | ||
+ | export CPATH=$CUDA_DIR/ | ||
+ | fi | ||
+ | |||
==== TensorFlow Environment ==== | ==== TensorFlow Environment ==== | ||
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This environment have TensorFlow 1.0 and all necessary requirements for NeuralMonkey. | This environment have TensorFlow 1.0 and all necessary requirements for NeuralMonkey. | ||
+ | |||
+ | ==== Pytorch Environment ==== | ||
+ | |||
+ | If you want to use pytorch, there is a ready-made environment in | ||
+ | |||
+ | / | ||
+ | | ||
+ | It does rely on the CUDA and CuDNN setup above. | ||
==== Using cluster ==== | ==== Using cluster ==== | ||
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/ | / | ||
# shows CUDA capability etc. | # shows CUDA capability etc. | ||
+ | ssh dll1; ~popel/ | ||
+ | # who occupies which card on a given machine | ||
| | ||
=== Select GPU device === | === Select GPU device === | ||
- | Use variable CUDA_VISIBLE_DEVICES | + | The variable CUDA_VISIBLE_DEVICES |
- | export CUDA_VISIBLE_DEVICES=0 | + | |
To list available devices, use: | To list available devices, use: | ||
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* [[http:// | * [[http:// | ||
- | In the following table is the experiment conducted by Tom Kocmi. You can replicate experiment: / | + | In the following table is the experiment conducted by Tom Kocmi. You can replicate experiment: / |
| machine | Setup; CPU/GPU; [[https:// | | machine | Setup; CPU/GPU; [[https:// | ||
| athena | | athena | ||
- | | dll2 | + | | dll2 | GeForce GTX 1080; cc6.1 | |
| titan | GeForce GTX 1080 Ti | | | titan | GeForce GTX 1080 Ti | | ||
| dll1 | (2 GPU) GeForce GTX 1080; cc6.1 | | | dll1 | (2 GPU) GeForce GTX 1080; cc6.1 | | ||
- | | dll2 | + | | dll2 | GeForce GTX 1080; cc6.1 | |
| titan-gpu | | titan-gpu | ||
| kronos-dev | Tesla K40c; cc3.5 | | | kronos-dev | Tesla K40c; cc3.5 | | ||
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- | === Better | + | === Second |
- | 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. | + | 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 I have increased the batch size as much as possible so the model still could fit into the GPU (the previous benchmark |
| GPU; Cuda capability | | GPU; Cuda capability | ||
- | | Tesla K40c; cc3.5 | 12 GB | | + | | Tesla K40c; cc3.5 | 12 GB | |
| GeForce GTX 1080 Ti; cc6.1 | 11 GB | 00:55:56 | 2300 | dll5 | | | GeForce GTX 1080 Ti; cc6.1 | 11 GB | 00:55:56 | 2300 | dll5 | | ||
| GeForce GTX 1080; cc6.1 | 8 GB | 01:10:57 | 1700 | dll1 | | | GeForce GTX 1080; cc6.1 | 8 GB | 01:10:57 | 1700 | dll1 | | ||
- | | GeForce GTX 1080; cc6.1 | 8 GB | | ||
| GeForce GTX Titan Z; cc3.5 | 6 GB | 02:20:47 | 1100 | titan-gpu | | | GeForce GTX Titan Z; cc3.5 | 6 GB | 02:20:47 | 1100 | titan-gpu | | ||
- | | Quadro K2000; cc3.0 | 2 GB | | + | | Quadro K2000; cc3.0 | 2 GB | |
===== Links ===== | ===== Links ===== |