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gpu [2016/04/27 17:09] bojar |
gpu [2017/10/11 11:46] popel [Basic commands] |
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====== GPU at ÚFAL ====== | ====== GPU at ÚFAL ====== | ||
- | This page summarizes which UFAL servers have some GPU card, and suggest | + | This page summarizes which UFAL servers have some GPU card, and suggests |
===== Servers with GPU units ===== | ===== Servers with GPU units ===== | ||
+ | GPU cluster '' | ||
- | | machine | GPU | + | | machine |
- | | titan-gpu | GeForce GTX Titan Z | + | | iridium |
- | | twister1 | + | | titan-gpu |
- | | twister2 | + | | twister1; twister2; kronos |
- | | kronos | + | | dll1; dll2 | GeForce GTX 1080 |
+ | | titan | GeForce GTX 1080 | ||
+ | | dll3; dll4; dll5 | GeForce GTX 1080 Ti | | ||
+ | | dll6 | GeForce GTX 1080 Ti | cc6.1 | 3| 11 GB | | | ||
- | ===== Installed toolkits ===== | + | Desktop machines: |
+ | | machine | ||
+ | | victoria; arc | GeForce GT 630 | cc3.0 | 1 | 2 GB | desktop machine | | ||
+ | | athena | ||
- | This should mention where each interesting toolkit lives (on a particular machine). | + | Not used at the moment: GeForce GTX 570 (from twister2) |
+ | All machines have CUDA8.0 and should support both Theano and TensorFlow. | ||
- | ===== Basic commands | + | === Disk space === |
+ | All the GPU machines are at Malá Strana (not at Troja), so you should not use ''/ | ||
+ | - ''/ | ||
+ | - ''/ | ||
+ | - ''/ | ||
+ | - ''/ | ||
+ | |||
+ | === 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) | ||
+ | |||
+ | |||
+ | |||
+ | |||
+ | ===== How to use cluster ===== | ||
+ | |||
+ | 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 ==== | ||
+ | |||
+ | 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 | ||
+ | |||
+ | export PATH=/ | ||
+ | |||
+ | And then you can activate your environment: | ||
+ | |||
+ | source activate tf1 | ||
+ | source activate tf1cpu | ||
+ | |||
+ | This environment have TensorFlow 1.0 and all necessary requirements for NeuralMonkey. | ||
+ | |||
+ | ==== Using cluster ==== | ||
+ | |||
+ | Rule number one, always use the GPU queue (never log in machine by ssh). Always use qsub or qsubmit with proper arguments. | ||
+ | |||
+ | 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: | ||
+ | |||
+ | qsub -q gpu.q -l gpu=1, | ||
+ | |||
+ | Cleaner way to use cluster is with / | ||
+ | |||
+ | 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. | ||
+ | ==== Basic commands ==== | ||
lspci | lspci | ||
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nvidia-smi | nvidia-smi | ||
# more details, incl. running processes on the GPU | # more details, incl. running processes on the GPU | ||
+ | # nvidia-* are typically located in /usr/bin | ||
watch nvidia-smi | watch nvidia-smi | ||
# For monitoring GPU activity in a separate terminal (thanks to Jindrich Libovicky for this!) | # For monitoring GPU activity in a separate terminal (thanks to Jindrich Libovicky for this!) | ||
- | # See nvcc path in the table above for nvidia-smi location. | ||
nvcc --version | nvcc --version | ||
# this should tell CUDA version | # this should tell CUDA version | ||
# nvcc is typically installed in / | # nvcc is typically installed in / | ||
- | | + | theano-test |
# dela to vubec neco uzitecneho? :-) | # 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 === | ||
+ | |||
+ | Use variable CUDA_VISIBLE_DEVICES to constrain tensorflow to compute only on the selected one. For the use of first GPU use (GPU queue do this for you): | ||
+ | export CUDA_VISIBLE_DEVICES=0 | ||
+ | |||
+ | 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 ===== | ||
+ | |||
+ | * [[https:// | ||
+ | |||
+ | |||
+ | GPU specs for those GPUs we have: | ||
+ | * [[http:// |