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Both sides previous revision Previous revision 2018/12/19 12:21 vodrazka GPU machine table update2018/12/16 16:26 straka [Rules] 2018/12/01 21:09 popel [Rules] -pe smp now works with gpu2018/11/28 11:21 laitoch [Rules] 2018/11/28 11:11 laitoch [Rules] 2018/11/14 13:46 kocmi [Using cluster] 2018/11/12 10:36 vodrazka [Servers with GPU units] 2018/11/01 14:41 vodrazka [Servers with GPU units] 2018/10/22 16:02 vodrazka [Servers with GPU units] 2018/10/10 16:48 fucik 2018/10/02 16:41 kocmi [Set-up CUDA and CUDNN] 2018/10/01 10:07 vodrazka [Servers with GPU units] 2018/09/05 10:17 vodrazka [Servers with GPU units] 2018/08/10 13:49 rosa [Set-up CUDA and CUDNN] 2018/06/21 10:04 vodrazka [Servers with GPU units] 2018/06/21 10:04 vodrazka [Servers with GPU units] 2018/06/12 14:17 popel 2018/06/12 13:50 machacek.dominik [Servers with GPU units] 2018/06/12 12:42 vodrazka [Servers with GPU units] 2018/06/12 12:42 vodrazka [Servers with GPU units] 2018/06/12 12:08 ufal [TensorFlow Environment] 2018/05/31 10:30 popel [Set-up CUDA and CUDNN] 2018/05/30 09:57 vodrazka [Set-up CUDA and CUDNN] 2018/05/30 09:54 vodrazka [Set-up CUDA and CUDNN] 2018/04/27 18:22 popel [Servers with GPU units] 2018/04/24 10:01 vodrazka [Set-up CUDA and CUDNN] 2018/04/23 12:53 vodrazka [Set-up CUDA and CUDNN] 2018/04/16 11:35 kocmanek [Performance tests] 2018/04/16 11:29 kocmanek [Performance tests] 2018/04/16 11:18 kocmanek [Performance tests] 2018/04/13 12:50 kocmanek [Performance tests] 2018/04/12 17:11 kocmanek [Performance tests] 2018/04/12 16:02 kocmanek [Performance tests] 2018/04/12 15:17 kocmanek [Performance tests] 2018/04/09 12:45 vodrazka documenting default settings for cuda and cudnn libraries2018/04/05 14:41 vodrazka table update - Quadro P5000 for testing in twister22018/03/22 11:14 vodrazka Updated cluster GPU table2018/03/21 15:29 ufal [Servers with GPU units] 2018/02/20 17:54 kruza [Rules] typo2018/02/09 15:15 popel [Set-up CUDA and CUDNN] 2018/02/08 15:26 popel [Rules] 2018/02/08 15:21 kocmanek [Servers with GPU units] 2018/02/08 15:20 kocmanek [Rules] 2018/02/08 15:16 kocmanek [Servers with GPU units] 2018/02/08 15:10 kocmanek [Servers with GPU units] 2018/02/08 14:56 kocmanek [Servers with GPU units] 2018/01/25 17:27 popel [Using cluster] 2018/01/23 23:16 popel [Servers with GPU units] 2018/01/15 21:51 popel [Rules] 2018/01/15 10:39 popel [Set-up CUDA and CUDNN] 2018/01/08 13:25 popel dll6 now has 9 cards, so it is the same config as dll3 Go Next revision		Previous revision 2019/07/02 15:18 vodrazka [Servers with GPU units] 2019/04/17 15:13 ufal [Servers with GPU units] 2019/04/17 14:11 ufal [Servers with GPU units] 2019/03/05 13:15 vodrazka [Servers with GPU units] 2019/02/11 13:03 popel [Rules] at se pouziva i gpu-troja2019/02/11 10:50 naplava [Servers with GPU units] 2019/02/07 10:44 vodrazka GPU troja machines added2019/01/08 09:36 vodrazka [Servers with GPU units] 2018/12/19 12:21 vodrazka GPU machine table update2018/12/16 16:26 straka [Rules] 2018/12/01 21:09 popel [Rules] -pe smp now works with gpu2018/11/28 11:21 laitoch [Rules] 2018/11/28 11:11 laitoch [Rules] 2018/11/14 13:46 kocmi [Using cluster] 2018/11/12 10:36 vodrazka [Servers with GPU units] 2018/11/01 14:41 vodrazka [Servers with GPU units] 2018/10/22 16:02 vodrazka [Servers with GPU units] 2018/10/10 16:48 fucik 2018/10/02 16:41 kocmi [Set-up CUDA and CUDNN] 2018/10/01 10:07 vodrazka [Servers with GPU units] 2018/09/05 10:17 vodrazka [Servers with GPU units] 2018/08/10 13:49 rosa [Set-up CUDA and CUDNN] 2018/06/21 10:04 vodrazka [Servers with GPU units] 2018/06/21 10:04 vodrazka [Servers with GPU units] 2018/06/12 14:17 popel 2018/06/12 13:50 machacek.dominik [Servers with GPU units] 2018/06/12 12:42 vodrazka [Servers with GPU units] 2018/06/12 12:42 vodrazka [Servers with GPU units] 2018/06/12 12:08 ufal [TensorFlow Environment] 2018/05/31 10:30 popel [Set-up CUDA and CUDNN] 2018/05/30 09:57 vodrazka [Set-up CUDA and CUDNN] 2018/05/30 09:54 vodrazka [Set-up CUDA and CUDNN] 2018/04/27 18:22 popel [Servers with GPU units] 2018/04/24 10:01 vodrazka [Set-up CUDA and CUDNN] 2018/04/23 12:53 vodrazka [Set-up CUDA and CUDNN] 2018/04/16 11:35 kocmanek [Performance tests] 2018/04/16 11:29 kocmanek [Performance tests] 2018/04/16 11:18 kocmanek [Performance tests] 2018/04/13 12:50 kocmanek [Performance tests] 2018/04/12 17:11 kocmanek [Performance tests] 2018/04/12 16:02 kocmanek [Performance tests] 2018/04/12 15:17 kocmanek [Performance tests] 2018/04/09 12:45 vodrazka documenting default settings for cuda and cudnn libraries2018/04/05 14:41 vodrazka table update - Quadro P5000 for testing in twister22018/03/22 11:14 vodrazka Updated cluster GPU table2018/03/21 15:29 ufal [Servers with GPU units] 2018/02/20 17:54 kruza [Rules] typo2018/02/09 15:15 popel [Set-up CUDA and CUDNN] 2018/02/08 15:26 popel [Rules] 2018/02/08 15:21 kocmanek [Servers with GPU units] 2018/02/08 15:20 kocmanek [Rules] Go Next revision Both sides next revision
gpu [2018/04/24 10:01] vodrazka [Set-up CUDA and CUDNN]		gpu [2018/06/12 13:50] machacek.dominik [Servers with GPU units]
Line 4:		Line 4:
	===== Servers with GPU units =====		===== Servers with GPU units =====
-	GPU cluster ''gpu.q'' at Malá Strana:	+	GPU cluster ''gpu-ms.q'' at Malá Strana:
	| machine | GPU type | GPU driver version | [[https://en.wikipedia.org/wiki/CUDA#GPUs_supported|cc]] | GPU cnt | GPU RAM (GB) | machine RAM (GB)|		| machine | GPU type | GPU driver version | [[https://en.wikipedia.org/wiki/CUDA#GPUs_supported|cc]] | GPU cnt | GPU RAM (GB) | machine RAM (GB)|
-	| dll1 |  GeForce GTX 1080 |  384.69 |  6.1 |  8 |  8 |  250 |	+	| dll1 |  GeForce GTX 1080 |  396.24 |  6.1 |  8 |  8 |  249 |
-	| dll2 |  GeForce GTX 1080 |  387.34 |  6.1 |  8 |  8 |  250 |	+	| dll2 |  GeForce GTX 1080 |  396.24 |  6.1 |  8 |  8 |  249 |
-	| dll3 |  GeForce GTX 1080 Ti |  375.66 |  6.1 |  9 |  11 |  250 |	+	| dll3 |  GeForce GTX 1080 Ti |  396.24 |  6.1 |  9 |  11 |  249 |
-	| dll4 |  GeForce GTX 1080 Ti |  375.66 |  6.1 |  10 |  11 |  250 |	+	| dll4 |  GeForce GTX 1080 Ti |  396.24 |  6.1 |  10 |  11 |  249 |
-	| dll5 |  GeForce GTX 1080 Ti |  384.69 |  6.1 |  10 |  11 |  250 |	+	| dll5 |  GeForce GTX 1080 Ti |  396.24 |  6.1 |  10 |  11 |  249 |
-	| dll6 |  GeForce GTX 1080 Ti |  384.69 |  6.1 |  9 |  11 |  122 |	+	| dll6 |  GeForce GTX 1080 Ti |  396.24 |  6.1 |  9 |  11 |  123 |
-	| gpu |  GeForce GTX TITAN Z |  381.22 |  3.5 |  2 |  6 |  31 |	+
-	| iridium |  Quadro K2000 |  367.48 |  3.0 |  1 |  2 |  504 |	+	To be migrated to new cluster:
		+
		+	| titan-gpu |  GeForce GTX TITAN Z |  381.22 |  3.5 |  2 |  6 |  31 |
	| kronos |  GeForce GTX 1080 Ti |  384.81 |  6.1 |  1 |  11 |  125 |		| kronos |  GeForce GTX 1080 Ti |  384.81 |  6.1 |  1 |  11 |  125 |
	| titan |  GeForce GTX 1080 |  381.22 |  6.1 |  1 |  8 |  31 |		| titan |  GeForce GTX 1080 |  381.22 |  6.1 |  1 |  8 |  31 |
-	| twister1 |  Tesla K40c |  367.48 |  3.5 |  1 |  11 |  47 |	+	| twister1 |  Tesla K40c |  ? |  ? |  1 | 11 |  47 |
-	| twister2 |  Quadro P5000 |  367.48 |  6.1 |  1 |  17 |  47 |	+	| twister2 |  Tesla K40c |  384.81 |  3.5 |  1 |  11 |  47 |
	Desktop machines:		Desktop machines:
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	You also need to use ''qsub -q gpu.q@dll[256]'' because only those machines have drivers which support CUDA 9.		You also need to use ''qsub -q gpu.q@dll[256]'' because only those machines have drivers which support CUDA 9.
-	**Testing configuration (so far on twister1 & 2, titan and titan-gpu only)**	+	**THE NEW CLUSTER (SGE 8.1.9)**
-	Multiple versions of ''cuda'' can be accessed in ''/opt''.	+	Multiple versions of ''cuda'' can be accessed in ''/opt/cuda''. **Compared to the old cluster there is a difference in setting the CUDA_DIR_OPT variable!!**
-	System default version for cuda library is configured in ''/etc/ld.so.conf.d/cuda.conf'' as:	+
-	/opt/cuda/lib64	+	You need to set library path from your ''~/.bashrc'':
-	/opt/cuda/extras/CUPTI/lib64	+
-	Actual version used depends on the link in ''/opt''. For example:	+	CUDNN_version=7.0
-		+	CUDA_version=9.0
-	ls -l /opt	+	CUDA_DIR_OPT=/opt/cuda/$CUDA_version
-	...	+	if [ -d "$CUDA_DIR_OPT" ] ; then
-	lrwxrwxrwx 1 root root  8 dub  9 12:30 cuda -> cuda-9.0	+	CUDA_DIR=$CUDA_DIR_OPT
-	...	+	export CUDA_HOME=$CUDA_DIR
-		+	export THEANO_FLAGS="cuda.root=$CUDA_HOME,device=gpu,floatX=float32"
-	This means that the system is using ''cuda 9.0'' by default.	+	export PATH=$PATH:$CUDA_DIR/bin
-		+	export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CUDA_DIR/cudnn/$CUDNN_version/lib64:$CUDA_DIR/lib64
-	If system default version does not work for you, you can set library path from your ''~/.bashrc''. Your LD_LIBRARY_PATH should contain:	+	export CPATH=$CUDA_DIR/cudnn/$CUDNN_version/include:$CPATH
-		+	fi
-	/opt/cuda-$CUDA_version/lib64	+
-		+
-	In order to link specific version of ''cudnn'' your LD_LIBRARY_PATH should contain:	+
-	/opt/cuda-$CUDA_version/cudnn/$CUDNN_version/lib64	+	* When not using Theano, just Tensorflow this can be simplified to ''export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/opt/cuda/9.0/lib64:/opt/cuda/9.0/cudnn/7.0/lib64''.
-	Please check if required CUDNN_version is available for given CUDA_version.	+	* There is no default and you always need to set ''LD_LIBRARY_PATH'' explicitly.
		+	* Note that ''cudnn'' library is compiled for specific version of ''cuda''. If you need specific version of ''cudnn'', you can look in ''/opt/cuda/$CUDA_version/cudnn/'' whether it is available for given ''$CUDA_version''.
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	And then you can activate your environment:		And then you can activate your environment:
-	source activate tf1	+	source activate tf18
-	source activate tf1cpu	+	source activate tf18cpu
-	This environment have TensorFlow 1.0 and all necessary requirements for NeuralMonkey.	+	This environment have TensorFlow 1.8.0 and all necessary requirements for NeuralMonkey.
	==== Pytorch Environment ====		==== Pytorch Environment ====

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