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gpu [2017/08/28 14:40] kocmanek [Set-up CUDA and CUDNN] |
gpu [2017/11/13 09:43] bojar [Rules] |
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| twister1; twister2; kronos | Tesla K40c | cc3.5 | | | twister1; twister2; kronos | Tesla K40c | cc3.5 | | ||
| dll1; dll2 | GeForce GTX 1080 | cc6.1 | | | dll1; dll2 | GeForce GTX 1080 | cc6.1 | | ||
- | | titan | GeForce GTX 1080 Ti | cc6.1 | | + | | titan | GeForce GTX 1080 | cc6.1 | |
| dll3; dll4; dll5 | GeForce GTX 1080 Ti | cc6.1 | 10| 11 GB | dll3 has only 9 GPUs since 2017/07 | | | dll3; dll4; dll5 | GeForce GTX 1080 Ti | cc6.1 | 10| 11 GB | dll3 has only 9 GPUs since 2017/07 | | ||
+ | | dll6 | GeForce GTX 1080 Ti | cc6.1 | | ||
Desktop machines: | Desktop machines: | ||
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All machines have CUDA8.0 and should support both Theano and TensorFlow. | All machines have CUDA8.0 and should support both Theano and TensorFlow. | ||
- | === Disk space === | + | ===== Rules ===== |
- | All the GPU machines | + | * 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 |
- | - '' | + | * 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 ('' |
- | - ''/COMP.TMP'' | + | * For interactive jobs, you can use '' |
- | + | ||
- | === Individual acquisitions: | + | |
- | + | ||
- | There is an easy way to get one high-end | + | |
- | + | ||
- | Take care, however, to coordinate | + | |
- | + | ||
- | Known NVIDIA Academic Hardware Grants: | + | |
- | + | ||
- | * Ondřej | + | |
- | * Jan Hajič jr. - granted (early 2016) | + | |
- | + | ||
- | + | ||
- | | + | |
===== How to use cluster ===== | ===== How to use cluster ===== | ||
- | |||
- | In this section will be explained how to use cluster properly. | ||
==== Set-up CUDA and CUDNN ==== | ==== Set-up CUDA and CUDNN ==== | ||
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export THEANO_FLAGS=" | export THEANO_FLAGS=" | ||
export PATH=$PATH: | export PATH=$PATH: | ||
- | export LD_LIBRARY_PATH=$LD_LIBRARY_PATH: | + | export LD_LIBRARY_PATH=$LD_LIBRARY_PATH: |
export CPATH=$CUDA_DIR/ | export CPATH=$CUDA_DIR/ | ||
fi | fi | ||
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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. | ||
- | ==== Using cluster | + | ==== Pytorch Environment |
- | Rule number one, always use the GPU queue (never log in machine by ssh). Always use qsub or qsubmit with proper arguments. | + | If you want to use pytorch, there is a ready-made environment |
- | 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. | + | / |
- | + | ||
- | qrsh -q gpu.q -l gpu=1, | + | |
| | ||
- | For running experiments you must use qsub command: | + | It does rely on the CUDA and CuDNN setup above. |
- | qsub -q gpu.q -l gpu=1, | + | ==== Using cluster ==== |
- | + | ||
- | Cleaner way to use cluster is with / | + | As an alternative |
qsubmit --gpumem=2G --queue=" | qsubmit --gpumem=2G --queue=" | ||
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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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GPU specs for those GPUs we have: | GPU specs for those GPUs we have: | ||
* [[http:// | * [[http:// | ||
+ | |||
+ | ==== Individual acquisitions: | ||
+ | |||
+ | There is an easy way to get one high-end GPU: [[https:// | ||
+ | |||
+ | 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 " | ||
+ | |||
+ | Known NVIDIA Academic Hardware Grants: | ||
+ | |||
+ | * Ondřej Plátek - granted (2015) | ||
+ | * Jan Hajič jr. - granted (early 2016) | ||
+ | |||
+ | |||
+ |