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gpu [2017/03/16 16:45] kocmanek |
gpu [2017/10/17 16:37] popel |
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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 GTX 1080 Ti | |
- | | arc | GeForce GT 630; cc3.0 | 1 | 2 GB | Ales's desktop machine | | + | | dll6 | GeForce GTX 1080 Ti | |
- | | athena | + | |
- | | dll1 | GeForce GTX 1080; cc6.1 | 8 | 8 GB each core | | | + | |
- | | dll2 | GeForce GTX 1080; cc6.1 | 8 | 8 GB each core | | | + | |
- | 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:// | + | * 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 '' |
- | * There is MetaCentrum which also has GPUs, so testing can be done there. | + | * Always specify the number |
- | * 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 machine since we most likely don't have any available. | + | * If you need more than one GPU card, always require as many CPU cores as many GPU cards you need. E.g. < |
- | * 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 | ||
- | | machine | Setup; CPU/GPU; [[https:// | + | This environment have TensorFlow |
- | | athena | + | |
- | | dll2 | (2 GPU) GeForce GTX 1080; cc6.1 | | + | |
- | | dll1 | (2 GPU) GeForce GTX 1080; cc6.1 | | + | |
- | | dll2 | (2 GPU) 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 | + | |
- | | victoria | + | |
+ | ==== Pytorch Environment ==== | ||
- | ===== Installed toolkits ===== | + | If you want to use pytorch, there is a ready-made environment in |
- | //This should mention where each interesting toolkit lives (on a particular machine).// | + | |
+ | |||
+ | It does rely on the CUDA and CuDNN setup above. | ||
- | ==== TensorFlow | + | ==== Using cluster |
- | [[https:// | + | Rule number one, always use the GPU queue (never log in machine by ssh). Always use qsub or qsubmit with proper arguments. |
- | OP: I created [[https:// | + | For testing |
- | === Select GPU device === | + | qrsh -q gpu.q -l gpu=1,gpu_ram=2G -pty yes bash |
+ | |||
+ | For running experiments you must use qsub command: | ||
- | Use variable CUDA_VISIBLE_DEVICES to constrain tensorflow to compute only on the selected one. For the use of first GPU use: | + | qsub -q gpu.q -l gpu=1, |
- | < | + | |
+ | Cleaner way to use cluster is with / | ||
- | To list available devices, use: | + | 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 | + | ==== 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) | ||
+ | |||
+ | |||
+ |