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gpu [2016/12/26 18:40] kocmanek |
gpu [2017/11/13 09:43] bojar [Rules] |
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===== Servers with GPU units ===== | ===== Servers with GPU units ===== | ||
+ | GPU cluster '' | ||
- | | machine | GPU; [[https:// | + | | machine |
- | | titan-gpu | + | | iridium |
- | | twister1 | + | | titan-gpu |
- | | twister2 | + | | twister1; twister2; kronos |
- | | kronos-dev | Tesla K40c; cc3.5 | + | | dll1; dll2 | GeForce GTX 1080 |
- | | iridium | + | | titan |
- | | victoria | + | | dll3; dll4; dll5 | GeForce |
- | | arc | + | | dll6 | GeForce GTX 1080 Ti | |
- | | athena | + | |
- | not used at the moment: GeForce GTX 570 (from twister2) | + | Desktop machines: |
+ | | machine | ||
+ | | victoria; arc | GeForce GT 630 | cc3.0 | 1 | 2 GB | desktop machine | | ||
+ | | athena | ||
+ | |||
+ | Not used at the moment: GeForce GTX 570 (from twister2) | ||
All machines have CUDA8.0 and should support both Theano and TensorFlow. | All machines have CUDA8.0 and should support both Theano and TensorFlow. | ||
- | Summary of future plans: | + | ===== Rules ===== |
- | * Current Troja servers won't get any GPUs (the only option would be [[http://www.czc.cz/hp-quadro-k1200-4gb/171662/ | + | * First, read [[internal:Linux network]] and [[:Grid]]. |
- | * The old Quadro K2000 we have is a much more low end piece, so we can' | + | * 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. '' |
- | * 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' | + | * If you need more than one GPU card (on a single machine), always require as many CPU cores ('' |
- | * Yes, there are grant applications under review which include rack machines with GPUs, e.g. 5x2 or something like that; more will be known in 2017. | + | * For interactive jobs, you can use '' |
+ | ===== How to use cluster ===== | ||
- | === Individual acquisitions: | + | ==== Set-up CUDA and CUDNN ==== |
- | There is an easy way to get one high-end GPU: [[https:// | + | You can add following command into your ~/.bashrc |
- | 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 | + | CUDNN_version=6.0 |
+ | CUDA_version=8.0 | ||
+ | CUDA_DIR_OPT=/opt/cuda-$CUDA_version | ||
+ | if [ -d "$CUDA_DIR_OPT" | ||
+ | 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 | ||
- | Known NVIDIA Academic Hardware Grants: | + | ==== TensorFlow Environment ==== |
- | + | ||
- | * Ondřej Plátek - granted (2015) | + | |
- | * Jan Hajič jr. - granted (early 2016) | + | |
- | * Jindra Helcl - planning to apply (fall 2016) | + | |
+ | 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. | ||
+ | pip install tensorflow | ||
+ | pip install tensorflow-gpu | ||
| | ||
+ | You can use prepared environment by adding into your ~/.bashrc | ||
- | ===== Performance tests ===== | + | export PATH=/ |
- | * [[http:// | + | And then you can activate your environment: |
- | In the following table is the experiment conducted by Tom Kocmi. You can replicate experiment: / | + | source activate tf1 |
+ | source activate tf1cpu | ||
- | I am preparing department-wide benchmark, but meanwhile the results | + | This environment have TensorFlow 1.0 and all necessary requirements |
- | * Athena (GTX 1080) - 2 hodiny 32 minut | + | |
- | * Twister (Tesla K40c) - 6 hodin 46 minut | + | |
- | | machine | Setup; CPU/GPU; [[https:// | + | ==== Pytorch Environment ==== |
- | | athena | + | |
- | | titan-gpu | + | |
- | | kronos-dev | Tesla K40c; cc3.5 | 22:41:01 | | | + | |
- | | twister1 | + | |
- | | belzebub | + | |
- | | iridium | + | |
- | | arc | GeForce GT 630; cc3.0 | 103:42:30 | (approximated after 66 hours) | | + | |
- | | lucifer4 | + | |
- | | twister2 | + | |
- | | dll1 | (2 GPU) GeForce GTX 1080; cc6.1 | | | | + | |
- | | dll2 | (2 GPU) GeForce GTX 1080; cc6.1 | | | | + | |
- | | victoria | + | |
+ | If you want to use pytorch, there is a ready-made environment in | ||
+ | / | ||
+ | | ||
+ | It does rely on the CUDA and CuDNN setup above. | ||
- | ===== Installed toolkits ===== | + | ==== Using cluster |
- | //This should mention where each interesting toolkit lives (on a particular machine).// | + | As an alternative to '' |
- | ==== TensorFlow ==== | + | qsubmit --gpumem=2G --queue="gpu.q" WHAT_SHOULD_BE_RUN |
- | + | ||
- | [[https:// | + | It is recommended to use priority -100 if you are not rushing |
- | + | ==== Basic commands ==== | |
- | OP: I created [[https:// | + | |
- | + | ||
- | === Select GPU device === | + | |
- | + | ||
- | Use variable CUDA_VISIBLE_DEVICES | + | |
- | < | + | |
- | + | ||
- | To list available devices, use: | + | |
- | < | + | |
- | + | ||
- | ===== Basic commands | + | |
lspci | lspci | ||
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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 === | ||
+ | |||
+ | 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 ===== | ||
+ | |||
+ | * [[http:// | ||
+ | |||
+ | In the following table is the experiment conducted by Tom Kocmi. You can replicate experiment: / | ||
+ | |||
+ | | machine | Setup; CPU/GPU; [[https:// | ||
+ | | athena | ||
+ | | dll2 | GeForce GTX 1080; cc6.1 | | ||
+ | | titan | GeForce GTX 1080 Ti | | ||
+ | | dll1 | (2 GPU) GeForce GTX 1080; cc6.1 | | ||
+ | | dll2 | 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 | ||
+ | |||
+ | |||
+ | === Second 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 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 | ||
+ | | Tesla K40c; cc3.5 | 12 GB | | ||
+ | | 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 Titan Z; cc3.5 | 6 GB | 02:20:47 | 1100 | titan-gpu | | ||
+ | | Quadro K2000; cc3.0 | 2 GB | 28:15:26 | 50 | iridium | | ||
===== Links ===== | ===== Links ===== | ||
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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) | ||
+ | |||
+ | |||
+ |