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gpu [2016/04/28 17:49] ufal |
gpu [2021/12/15 17:57] kasner [Pytorch Environment] |
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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 | |
- | | twister1 | + | | dll3 | |
- | | twister2 | + | | dll4 | |
- | | kronos-dev | + | | dll5 | GeForce GTX 1080 Ti | 455.23.05 | |
- | | kronos-dev | + | | dll6 | NVIDIA RTX A4000 |
+ | | dll7 | | ||
+ | | dll8 | Quadro RTX 5000 | 455.23.05 | ||
+ | | dll9 | GeForce RTX 3090 | 455.23.05 | ||
+ | | dll10 | | ||
+ | GPU cluster '' | ||
- | Milan Fucik says that Troja servers can accommodate only [[http://www.czc.cz/hp-quadro-k1200-4gb/171662/ | + | | machine | GPU type | GPU driver version | [[https://en.wikipedia.org/wiki/CUDA# |
+ | | tdll1 | Quadro P5000 | 455.23.05 | 6.1 | 8 | 17 | 245.0 | | ||
+ | | tdll2 | | ||
+ | | tdll3 | Quadro P5000 | | ||
+ | | tdll4 | Quadro P5000 | | ||
+ | | tdll5 | Quadro P5000 | | ||
- | ===== 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 | + | Multiple versions of CUDA library are accessible |
- | ==== TensorFlow ==== | + | [[http:// |
- | [[https:// | ||
- | ===== Basic commands | + | ===== Rules ===== |
+ | * First, read [[internal: | ||
+ | * 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 '' | ||
+ | |||
+ | |||
+ | ===== How to use cluster ===== | ||
+ | |||
+ | ==== Set-up CUDA and CUDNN ==== | ||
+ | |||
+ | Multiple versions of '' | ||
+ | |||
+ | You need to set library path from your '' | ||
+ | |||
+ | 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=$PATH: | ||
+ | export LD_LIBRARY_PATH=$LD_LIBRARY_PATH: | ||
+ | export CPATH=$CUDA_DIR/ | ||
+ | fi | ||
+ | |||
+ | * When not using Theano, just Tensorflow this can be simplified to '' | ||
+ | * Note that the '' | ||
+ | |||
+ | |||
+ | ==== 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. | ||
+ | |||
+ | 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 tf18 | ||
+ | source activate tf18cpu | ||
+ | |||
+ | This environment have TensorFlow 1.8.0 and all necessary requirements for NeuralMonkey. | ||
+ | |||
+ | ==== PyTorch Environment ==== | ||
+ | |||
+ | Install PyTorch following the instructions on https:// | ||
+ | |||
+ | At the time of writing, the recommended setup for CUDA 11.1 (supported by all GPU nodes) is: | ||
+ | |||
+ | pip3 install torch==1.10.0+cu111 torchvision==0.11.1+cu111 torchaudio==0.10.0+cu111 -f https:// | ||
+ | ==== 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 | ||
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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 | ||
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# dela to vubec neco uzitecneho? :-) | # dela to vubec neco uzitecneho? :-) | ||
# theano-* are typically located in / | # 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 ===== | ||
+ | |||
+ | * [[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 ===== | ||
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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) | ||
+ | |||
+ | |||
+ |