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gpu [2017/01/03 09:39] kocmanek |
gpu [2018/04/12 15:17] kocmanek [Performance tests] |
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===== Servers with GPU units ===== | ===== Servers with GPU units ===== | ||
+ | GPU cluster '' | ||
+ | | machine | GPU type | GPU driver version | [[https:// | ||
+ | | dll1 | GeForce GTX 1080 | 384.69 | 6.1 | 8 | 8 | 250 | | ||
+ | | dll2 | GeForce GTX 1080 | 387.34 | 6.1 | 8 | 8 | 250 | | ||
+ | | dll3 | GeForce GTX 1080 Ti | 375.66 | 6.1 | 9 | 11 | 250 | | ||
+ | | dll4 | GeForce GTX 1080 Ti | 375.66 | 6.1 | 10 | 11 | 250 | | ||
+ | | dll5 | GeForce GTX 1080 Ti | 384.69 | 6.1 | 10 | 11 | 250 | | ||
+ | | dll6 | GeForce GTX 1080 Ti | 384.69 | 6.1 | 9 | 11 | 122 | | ||
+ | | gpu | GeForce GTX TITAN Z | 381.22 | 3.5 | 2 | 6 | 31 | | ||
+ | | iridium | Quadro K2000 | 367.48 | 3.0 | 1 | 2 | 504 | | ||
+ | | kronos | GeForce GTX 1080 Ti | 384.81 | 6.1 | 1 | 11 | 125 | | ||
+ | | titan | GeForce GTX 1080 | 381.22 | 6.1 | 1 | 8 | 31 | | ||
+ | | twister1 | Tesla K40c | 367.48 | 3.5 | 1 | 11 | 47 | | ||
+ | | twister2 | Quadro P5000 | 367.48 | 6.1 | 1 | 17 | 47 | | ||
- | | machine | GPU; [[https:// | + | Desktop machines: |
- | | titan-gpu | + | | machine |
- | | twister1 | + | | victoria; arc | GeForce GT 630 | cc3.0 | 1 | 2 GB | desktop machine | |
- | | twister2 | + | | athena |
- | | kronos-dev | Tesla K40c; cc3.5 | 1 | 12 GB | | | + | |
- | | iridium | + | |
- | | victoria | + | |
- | | arc | GeForce GT 630; cc3.0 | 1 | 2 GB | + | |
- | | athena | + | |
- | not used at the moment: GeForce GTX 570 (from twister2) | + | 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: | + | [[https://ufaladm2.ufal.hide.ms.mff.cuni.cz/munin/ufal.hide.ms.mff.cuni.cz/lrc-headnode.ufal.hide.ms.mff.cuni.cz/index.html# |
- | * Current Troja servers won't get any GPUs (the only option would be [[http://www.czc.cz/hp-quadro-k1200-4gb/171662/produkt? | + | |
- | * The old Quadro K2000 we have is a much more low end piece, so we can't test is in Troja. | + | |
- | * There is MetaCentrum which also has GPUs, so testing can be done there. | + | |
- | * 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. | + | |
- | * 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. | + | |
- | === Individual acquisitions: 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 '' | ||
+ | * Always specify the number of GPU cards (e.g. '' | ||
+ | * If you need more than one GPU card (on a single machine), always require as many CPU cores ('' | ||
+ | * For interactive jobs, you can use '' | ||
+ | * Note that the dll machines have typically 10 cards, but " | ||
- | There is an easy way to get one high-end GPU: [[https:// | + | ===== How to use cluster ===== |
- | 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 " | + | ==== Set-up CUDA and CUDNN ==== |
- | Known NVIDIA Academic Hardware Grants: | + | You should add the following commands into your ~/.bashrc |
- | | + | |
- | | + | |
- | | + | 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 | ||
+ | When not using Theano, just Tensorflow this can be simplified to '' | ||
+ | TensorFlow 1.5 precompiled binaries need CUDA 9.0, for this you need to | ||
+ | |||
+ | export LD_LIBRARY_PATH=/ | ||
+ | |||
+ | You also need to use '' | ||
+ | |||
+ | **Testing configuration (so far on twister2 only)** | ||
+ | |||
+ | Multiple versions of '' | ||
+ | System default version for both libraries is configured in ''/ | ||
+ | |||
+ | / | ||
+ | / | ||
+ | / | ||
+ | |||
+ | Actual version used depends on the link in ''/ | ||
+ | |||
+ | ls -l /opt | ||
+ | ... | ||
+ | lrwxrwxrwx 1 root root 8 dub 9 12:30 cuda -> cuda-9.0 | ||
+ | lrwxrwxrwx 1 root root 9 dub 9 12:32 cudnn -> cudnn-7.1 | ||
+ | ... | ||
| | ||
+ | This means that the system is using '' | ||
- | ===== Performance tests ===== | + | If system default version does not work for you, you can set library path from your '' |
- | * [[http:// | ||
- | In the following table is the experiment conducted by Tom Kocmi. You can replicate experiment: / | ||
- | I am preparing department-wide benchmark, but meanwhile the results for different experiment: | ||
- | * Athena (GTX 1080) - 2 hodiny 32 minut | ||
- | * Twister (Tesla K40c) - 6 hodin 46 minut | ||
- | | machine | Setup; CPU/GPU; [[https:// | ||
- | | athena | ||
- | | dll1 | (2 GPU) GeForce GTX 1080; cc6.1 | 12:34:34 | Probably only one GPU was used due to the size of model | | ||
- | | titan-gpu | ||
- | | kronos-dev | Tesla K40c; cc3.5 | 22:41:01 | | | ||
- | | twister2 | ||
- | | twister1 | ||
- | | belzebub | ||
- | | iridium | ||
- | | arc | GeForce GT 630; cc3.0 | 103:42:30 | (approximated after 66 hours) | | ||
- | | lucifer4 | ||
- | | dll2 | (2 GPU) GeForce GTX 1080; cc6.1 | | | | ||
- | | helena | ||
- | | helena | ||
- | | victoria | ||
- | A comparison with Ondrej' | ||
- | * dll2 (2xGPU) takes 13m for one reporting period | ||
- | * achilles2 (4xCPU with 8 CPUs reserved) takes 24m for one reporting period | ||
- | ===== Installed toolkits ===== | ||
- | //This should mention where each interesting toolkit lives (on a particular machine).// | + | ==== TensorFlow Environment ==== |
- | ==== TensorFlow | + | 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. |
- | [[https:// | + | pip install tensorflow |
+ | pip install tensorflow-gpu | ||
+ | |||
+ | You can use prepared | ||
- | OP: I created [[https://gist.github.com/oplatek/323b63b8f116cd3d78c0f492f78cc289|script]] which install Tensorflow 0.8 and test it if it uses GPU. TF is installed into `user` or `global` installation either for `python3.4` or `python2.7` | + | export PATH=/a/merkur3/kocmanek/ANACONDA/ |
- | === Select GPU device === | + | And then you can activate your environment: |
- | Use variable CUDA_VISIBLE_DEVICES to constrain tensorflow to compute only on the selected one. For the use of first GPU use: | + | source activate tf1 |
- | < | + | |
- | To list available devices, use: | + | This environment have TensorFlow 1.0 and all necessary requirements for NeuralMonkey. |
- | < | + | |
- | ===== Basic commands | + | ==== Pytorch Environment ==== |
+ | |||
+ | If you want to use pytorch, there is a ready-made environment in | ||
+ | |||
+ | / | ||
+ | |||
+ | It does rely on the CUDA and CuDNN setup above. | ||
+ | |||
+ | ==== 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. | ||
+ | ==== 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 | ||
+ | | 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) | ||
+ | |||
+ | |||
+ |