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gpu [2017/03/16 17:09] kocmanek [How to use cluster] |
gpu [2019/01/08 09:36] vodrazka [Servers with GPU units] |
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===== Servers with GPU units ===== | ===== Servers with GPU units ===== | ||
+ | GPU cluster '' | ||
- | | machine | GPU; [[https:// | + | | machine | GPU type | GPU driver version | [[https:// |
- | | titan-gpu | + | | dll1 | GeForce GTX 1080 | 396.24 | 6.1 | 8 | 8 | 249 | |
- | | twister1 | + | | dll2 | |
- | | twister2 | + | | dll3 | |
- | | kronos-dev | + | | dll4 | |
- | | iridium | + | | dll5 | |
- | | victoria | + | | dll6 | GeForce |
- | | arc | + | | dll7 | GeForce |
- | | athena | + | | kronos |
- | | dll1 | GeForce GTX 1080; cc6.1 | 8 | 8 GB each core | | | + | | titan1 |
- | | dll2 | GeForce GTX 1080; cc6.1 | + | | titan2 |
- | 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 | + | Multiple versions |
- | * Current Troja servers won't get any GPUs (the only option would be [[http:// | + | |
- | * 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 GeForce GTX 1080 (~20000CZK, out of stock now), for a non-rack | + | |
- | * Yes, there are grant applications under review which include rack machines | + | |
+ | [[http:// | ||
- | === Individual acquisitions: | ||
- | There is an easy way to get one high-end GPU: [[https://developer.nvidia.com/ | + | ===== Rules ===== |
+ | * First, read [[internal:Linux network]] and [[:Grid]]. | ||
+ | * 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 '' | ||
+ | * **Note that you need to use '' | ||
+ | * 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 ('' | ||
+ | * For interactive jobs, you can use '' | ||
+ | * Note that the dll machines have typically 10 cards, but " | ||
- | Take care, however, | + | ===== How to use cluster |
- | Known NVIDIA Academic Hardware Grants: | + | ==== Set-up CUDA and CUDNN ==== |
- | * Ondřej Plátek - granted (2015) | + | Multiple versions of '' |
- | * Jan Hajič jr. - granted (early 2016) | + | |
- | * Jindra Helcl - planning to apply (fall 2016) | + | |
+ | You need to set library path from your '' | ||
- | | + | |
+ | 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 '' | ||
+ | * Note that the '' | ||
- | ===== How to use cluster ===== | ||
- | In this section will be explained how to use cluster properly. | ||
==== TensorFlow Environment ==== | ==== TensorFlow Environment ==== | ||
- | Majority | + | Many people at UFAL use TensorFlow. To start using it it is recommended |
pip install tensorflow | pip install tensorflow | ||
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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. |
- | ==== 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 -pty yes bash | + | |
| | ||
- | 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 | + | |
- | | + | As an alternative to '' |
+ | |||
+ | | ||
| | ||
+ | It is recommended to use priority lower than the default -100 if you are not rushing for the results and don't need to leap over your colleagues jobs. Please, do not use priority between -99 to 0 for jobs taking longer than a few hours, unless it is absolutely necessary for your work. | ||
==== Basic commands ==== | ==== Basic commands ==== | ||
+ | |||
+ | lspci | ||
+ | # is any such hardware there? | ||
+ | nvidia-smi | ||
+ | # more details, incl. running processes on the GPU | ||
+ | # nvidia-* are typically located in /usr/bin | ||
+ | watch nvidia-smi | ||
+ | # For monitoring GPU activity in a separate terminal (thanks to Jindrich Libovicky for this!) | ||
+ | nvcc --version | ||
+ | # this should tell CUDA version | ||
+ | # nvcc is typically installed in / | ||
+ | theano-test | ||
+ | # dela to vubec neco uzitecneho? :-) | ||
+ | # theano-* are typically located in / | ||
+ | / | ||
+ | # shows CUDA capability etc. | ||
+ | ssh dll1; ~popel/ | ||
+ | # who occupies which card on a given machine | ||
+ | | ||
+ | === Select GPU device === | ||
+ | |||
+ | The variable CUDA_VISIBLE_DEVICES constrains tensorflow and other toolkits to compute only on the selected GPUs. **Do not set this variable yourself** (unless debugging SGE), it is set for you automatically by SGE if you ask for some GPUs (see above). | ||
+ | |||
+ | To list available devices, use: | ||
+ | / | ||
+ | |||
===== Performance tests ===== | ===== Performance tests ===== | ||
* [[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 | | ||
| dll1 | (2 GPU) GeForce GTX 1080; cc6.1 | | | dll1 | (2 GPU) GeForce GTX 1080; cc6.1 | | ||
- | | dll2 | + | | twister2 |
+ | | dll2 | GeForce GTX 1080; cc6.1 | | ||
| titan-gpu | | titan-gpu | ||
| kronos-dev | Tesla K40c; cc3.5 | | | kronos-dev | Tesla K40c; cc3.5 | | ||
Line 102: | Line 144: | ||
| arc | GeForce GT 630; cc3.0 | 103:42:30 | (approximated after 66 hours) | | | arc | GeForce GT 630; cc3.0 | 103:42:30 | (approximated after 66 hours) | | ||
| lucifer4 | | lucifer4 | ||
- | | victoria | ||
- | ===== Installed toolkits ===== | + | === Second Benchmark |
- | //This should mention where each interesting toolkit lives (on a particular machine).// | + | 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. |
- | ==== TensorFlow ==== | + | | GPU; Cuda capability |
+ | | 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 | | ||
+ | | Quadro P5000 | ||
+ | | 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 | ||
- | [[https:// | + | ===== Links ===== |
- | OP: I created | + | * [[https://en.wikipedia.org/wiki/CUDA# |
- | === 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 specs for those GPUs we have: |
- | < | + | * [[http:// |
- | To list available devices, use: | + | ==== Individual acquisitions: NVIDIA Academic Hardware Grants ==== |
- | < | + | |
- | ===== Basic commands ===== | + | There is an easy way to get one high-end GPU: [[https:// |
- | lspci | + | Take care, however, to coordinate |
- | # is any such hardware there? | + | |
- | nvidia-smi | + | |
- | # more details, incl. running processes on the GPU | + | |
- | # nvidia-* | + | |
- | watch nvidia-smi | + | |
- | # For monitoring | + | |
- | nvcc --version | + | |
- | # this should tell CUDA version | + | |
- | # nvcc is typically installed in / | + | |
- | theano-test | + | |
- | # dela to vubec neco uzitecneho? :-) | + | |
- | # theano-* are typically located in / | + | |
- | / | + | |
- | # shows CUDA capability etc. | + | |
- | ===== Links ===== | + | Known NVIDIA Academic Hardware Grants: |
+ | |||
+ | * Ondřej Plátek - granted (2015) | ||
+ | * Jan Hajič jr. - granted (early 2016) | ||
- | * [[https:// | ||
- | GPU specs for those GPUs we have: | ||
- | * [[http:// |