Differences
This shows you the differences between two versions of the page.
Both sides previous revision Previous revision Next revision | Previous revision Next revision Both sides next revision | ||
gpu [2016/12/22 11:18] kocmanek |
gpu [2021/12/15 17:53] kasner Update CUDA version |
||
---|---|---|---|
Line 4: | Line 4: | ||
===== Servers with GPU units ===== | ===== Servers with GPU units ===== | ||
+ | GPU cluster '' | ||
- | | machine | GPU; [[https:// | + | | machine | GPU type | GPU driver version | [[https:// |
- | | titan-gpu | + | | dll1 | Quadro RTX 5000 | 455.23.05 | 7.5 | |
- | | twister1 | + | | dll3 | |
- | | twister2 | + | | dll4 | |
- | | kronos-dev | + | | dll5 | |
- | | iridium | + | | dll6 | NVIDIA RTX A4000 |
- | | victoria | + | | dll7 | GeForce |
- | | arc | + | | dll8 | |
- | | athena | + | | dll9 | GeForce |
+ | | dll10 | GeForce RTX 3090 | | ||
- | not used at the moment: GeForce GTX 570 (from twister2) | + | GPU cluster '' |
- | All machines have CUDA8.0 and should support both Theano and TensorFlow. | + | |
- | Summary of future plans: | + | | machine | GPU type | GPU driver version | [[https://en.wikipedia.org/wiki/CUDA# |
- | * Current Troja servers won't get any GPUs (the only option would be [[http://www.czc.cz/hp-quadro-k1200-4gb/171662/ | + | | tdll1 | |
- | | + | | tdll2 | Quadro P5000 | 455.23.05 | 6.1 | 8 | 17 | 245.0 | |
- | | + | | tdll3 | Quadro P5000 | 455.23.05 | 6.1 | 8 | 17 | 245.0 | |
- | | + | | tdll4 | Quadro P5000 | 455.23.05 | 6.1 | 8 | 17 | 245.0 | |
- | | + | | tdll5 | Quadro P5000 | |
+ | Desktop machines: | ||
+ | | machine | ||
+ | | victoria; arc | GeForce GT 630 | cc3.0 | 1 | 2 GB | desktop machine | | ||
+ | | athena | ||
- | === Individual acquisitions: | + | Multiple versions of CUDA library are accessible on each machine together with cudnn. Theano and TensorFlow is supported. |
- | There is an easy way to get one high-end GPU: [[https://developer.nvidia.com/academic_gpu_seeding|ask NVIDIA for an Academic Hardware Grant]]. All it takes is writing a short grant application (at most ~2 hrs of work from scratch; if you have a GAUK, ~15 minutes of copy-pasting). Due to the GPU housing issues (mainly rack space and cooling), Milan F. said we should request the Tesla-line cards. If you want to have a look at an application, | + | [[http://ufaladm2.ufal.hide.ms.mff.cuni.cz/munin/ufal.hide.ms.mff.cuni.cz/ |
- | 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: | + | ===== Rules ===== |
+ | * First, read [[internal:Linux network]] and [[: | ||
+ | * 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 at least as many CPU cores ('' | ||
+ | * For interactive jobs, you can use '' | ||
+ | * Note that the dll machines have typically 10 cards, but " | ||
+ | * If you know an approximate runtime of your job, please specify it with '' | ||
- | * Ondřej Plátek - granted (2015) | ||
- | * Jan Hajič jr. - granted (early 2016) | ||
- | * Jindra Helcl - planning to apply (fall 2016) | ||
+ | ===== How to use cluster ===== | ||
- | | + | ==== Set-up CUDA and CUDNN ==== |
- | ===== Performance tests ===== | + | Multiple versions of '' |
- | * [[http://www.trustedreviews.com/ | + | You need to set library path from your '' |
- | In the following table is the experiment conducted by Tom Kocmi. You can replicate experiment: /a/merkur3/kocmanek/GPUBenchmark (you will need to prepare environment of TensorFlow11 or use my ANACONDA) | + | CUDA_version=11.1 |
+ | CUDNN_version=8.0 | ||
+ | CUDA_DIR_OPT=/ | ||
+ | if [ -d " | ||
+ | CUDA_DIR=$CUDA_DIR_OPT | ||
+ | export CUDA_HOME=$CUDA_DIR | ||
+ | export THEANO_FLAGS=" | ||
+ | export PATH=$PATH:$CUDA_DIR/bin | ||
+ | export LD_LIBRARY_PATH=$LD_LIBRARY_PATH: | ||
+ | export CPATH=$CUDA_DIR/ | ||
+ | fi | ||
- | I am preparing department-wide benchmark, but meanwhile the results for different experiment: | + | * When not using Theano, just Tensorflow this can be simplified to '' |
- | | + | * Note that the '' |
- | * Twister (Tesla K40c) - 6 hodin 46 minut | + | |
- | | machine | Setup; CPU/GPU; [[https:// | ||
- | | athena | ||
- | | titan-gpu | ||
- | | twister1 | ||
- | | twister2 | ||
- | | kronos-dev | Tesla K40c; cc3.5 | 22:41:01 | | ||
- | | iridium | ||
- | | victoria | ||
- | | arc | GeForce GT 630; cc3.0 | | | ||
- | | lucifer4 | ||
- | | belzebub | ||
+ | ==== TensorFlow Environment ==== | ||
+ | Many people at UFAL use TensorFlow. To start using it it is recommended to create a [[python|Python virtual environment]] (or use Anaconda for it). Into the environment you must place TensorFlow. The TF is either in CPU or GPU version. | ||
- | ===== Installed toolkits ===== | + | pip install tensorflow |
+ | pip install tensorflow-gpu | ||
+ | |||
+ | You can use prepared environment by adding into your ~/.bashrc | ||
- | //This should mention where each interesting toolkit lives (on a particular machine).// | + | export PATH=/a/merkur3/ |
- | ==== TensorFlow ==== | + | And then you can activate your environment: |
- | [[https:// | + | |
+ | source activate tf18cpu | ||
- | OP: I created [[https:// | + | This environment have TensorFlow 1.8.0 and all necessary requirements |
- | === Select GPU device | + | ==== Pytorch Environment ==== |
- | Use variable CUDA_VISIBLE_DEVICES | + | If you want to use pytorch, there is a ready-made environment in |
- | < | + | |
- | To list available devices, use: | + | |
- | < | + | |
+ | It does rely on the CUDA and CuDNN setup above. | ||
- | ===== Basic commands | + | ==== Using cluster ==== |
+ | |||
+ | As an alternative to '' | ||
+ | |||
+ | qsubmit --gpumem=2G --queue=" | ||
+ | |||
+ | 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 ==== | ||
lspci | lspci | ||
Line 92: | Line 113: | ||
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!) | ||
+ | # You can also use nvidia-smi -l TIME | ||
nvcc --version | nvcc --version | ||
# this should tell CUDA version | # this should tell CUDA version | ||
Line 100: | Line 122: | ||
/ | / | ||
# 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 | | ||
+ | | twister2 | ||
+ | | 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 | ||
+ | | 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 | ||
===== Links ===== | ===== Links ===== | ||
Line 108: | Line 175: | ||
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) | ||
+ | |||
+ | |||
+ |