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gpu [2018/01/08 13:25] popel dll6 now has 9 cards, so it is the same config as dll3 |
gpu [2018/06/12 12:08] ufal [TensorFlow Environment] |
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===== Servers with GPU units ===== | ===== Servers with GPU units ===== | ||
GPU cluster '' | GPU cluster '' | ||
- | + | | machine | GPU type | GPU driver version | |
- | | machine | + | | dll1 | |
- | | iridium | + | | dll2 | GeForce GTX 1080 | |
- | | titan-gpu | + | | dll3 | GeForce GTX 1080 Ti | 375.66 | |
- | | twister1; twister2; kronos | + | | dll4 | |
- | | dll1; dll2 | GeForce GTX 1080 | | + | | dll5 | GeForce GTX 1080 Ti | |
- | | titan | + | | dll6 | GeForce GTX 1080 Ti | |
- | | dll4; dll5 | GeForce GTX 1080 Ti | | + | | titan-gpu | GeForce GTX TITAN Z | 381.22 | 3.5 | 2 | 6 | 31 | |
- | | dll3; dll6 | GeForce GTX 1080 Ti | | + | | iridium | Quadro K2000 | 367.48 | 3.0 | |
+ | | kronos | ||
+ | | titan | GeForce GTX 1080 | | ||
+ | | twister1 | Tesla K40c | 367.48 | 3.5 | 1 | 11 | | ||
+ | | twister2 | Quadro P5000 | 367.48 | 6.1 | 1 | 17 | 47 | | ||
Desktop machines: | Desktop machines: | ||
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* First, read [[internal: | * First, read [[internal: | ||
* 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 '' | * 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 '' | ||
- | * Always specify the number of GPU cards (e.g. '' | + | * Always specify the number of GPU cards (e.g. '' |
- | * If you need more than one GPU card (on a single machine), always require as many CPU cores ('' | + | * If you need more than one GPU card (on a single machine), always require as many CPU cores ('' |
* For interactive jobs, you can use '' | * For interactive jobs, you can use '' | ||
- | * Note that the dll machines have typically 10 cards, but " | + | * Note that the dll machines have typically 10 cards, but " |
===== How to use cluster ===== | ===== How to use cluster ===== | ||
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==== Set-up CUDA and CUDNN ==== | ==== Set-up CUDA and CUDNN ==== | ||
- | You can add following | + | You should |
CUDNN_version=6.0 | CUDNN_version=6.0 | ||
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export CPATH=$CUDA_DIR/ | export CPATH=$CUDA_DIR/ | ||
fi | fi | ||
+ | |||
+ | When not using Theano, just Tensorflow this can be simplified to '' | ||
+ | |||
+ | TensorFlow 1.5 precompiled binaries need CUDA 9.0, for this you need to | ||
+ | |||
+ | export LD_LIBRARY_PATH=/ | ||
+ | |||
+ | You also need to use '' | ||
+ | |||
+ | **THE NEW CLUSTER (SGE 8.1.9)** | ||
+ | |||
+ | Multiple versions of '' | ||
+ | |||
+ | You need to set library path from your '' | ||
+ | |||
+ | CUDNN_version=7.0 | ||
+ | CUDA_version=9.0 | ||
+ | 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 | ||
+ | |||
+ | * When not using Theano, just Tensorflow this can be simplified to '' | ||
+ | |||
+ | * There is no default and you always need to set '' | ||
+ | |||
+ | * Note that '' | ||
+ | |||
+ | |||
+ | |||
+ | |||
==== TensorFlow Environment ==== | ==== TensorFlow Environment ==== | ||
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And then you can activate your environment: | And then you can activate your environment: | ||
- | source activate | + | source activate |
- | source activate | + | source activate |
- | This environment have TensorFlow 1.0 and all necessary requirements for NeuralMonkey. | + | This environment have TensorFlow 1.8.0 and all necessary requirements for NeuralMonkey. |
==== Pytorch Environment ==== | ==== Pytorch Environment ==== | ||
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qsubmit --gpumem=2G --queue=" | 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. | + | It is recommended to use priority |
==== Basic commands ==== | ==== Basic commands ==== | ||
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| 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 | | ||
+ | | twister2 | ||
| dll2 | GeForce GTX 1080; cc6.1 | | | dll2 | GeForce GTX 1080; cc6.1 | | ||
| titan-gpu | | titan-gpu | ||
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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 model 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 model had batch size 20). This way the results should be more representative of the power for each GPU. | ||
- | | GPU; Cuda capability | + | | GPU; Cuda capability |
- | | Tesla K40c; cc3.5 | | + | | 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 | | + | | Quadro P5000 |
| 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 | 28:15:26 | 50 | iridium | | + | | Quadro K2000; cc3.0 | 2 GB | 28:15:26 | 50 | iridium |
===== Links ===== | ===== Links ===== |