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--- gpu [2017/03/16 17:09]
kocmanek [Using cluster]
+++ gpu [2017/10/11 13:07]
bojar people should not set CUDA_VISIBLE_DEVICES
@@ Line 4: / Line 4: @@
 ===== Servers with GPU units =====
+GPU cluster ''gpu.q'' at Malá Strana:
-| machine | GPU; [[https://en.wikipedia.org/wiki/CUDA#Supported_GPUs|Capability]] [cc]  | cores | GPU RAM | Comment |
+| machine                    | GPU type | [[https://en.wikipedia.org/wiki/CUDA#GPUs_supported|cc]] | GPUs | GPU RAM | Comment |
-| titan-gpu  | GeForce GTX Titan Z; cc3.5 | 2 | 6 GB each core |        |
+| iridium                    | Quadro K2000        |  cc3.0 |   1|   2 GB |  |
-| twister1   | Tesla K40c; cc3.5          | 1 | 12 GB          |        |
+| titan-gpu                  | GeForce GTX Titan Z |  cc3.5 |   2|   6 GB |  |
-| twister2   | Tesla K40c; cc3.5          | 1 | 12 GB          |        |
+| twister1; twister2; kronos | Tesla K40c          |  cc3.5 |   1|  12 GB |  |
-| kronos-dev | Tesla K40c; cc3.5          | 1 | 12 GB          |        |
+| dll1; dll2                 | GeForce GTX 1080    |  cc6.1 |   8|   8 GB |  |
-| iridium    | Quadro K2000; cc3.0        | 1 | 2 GB           |        |
+| titan                      | GeForce GTX 1080    |  cc6.1 |   1|   8 GB |  |
-| victoria   | GeForce GT 630; cc3.0      | 1 | 2 GB           | Ondrej Bojar's desktop machine |
+| dll3; dll4; dll5           | GeForce GTX 1080 Ti |  cc6.1 |  10|  11 GB | dll3 has only 9 GPUs since 2017/07 |
-| arc        | GeForce GT 630; cc3.0      | 1 | 2 GB           | Ales's desktop machine |
+| dll6                       | GeForce GTX 1080 Ti |  cc6.1 |   3|  11 GB |  |
-| athena     | GeForce GTX 1080; cc6.1    | 1 | 8 GB           | Tom's desktop machine |
-| 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:
-All machines have CUDA8.0 and should support both Theano and TensorFlow.
+| machine                    | GPU type | [[https://en.wikipedia.org/wiki/CUDA#GPUs_supported|cc]] | GPUs | GPU RAM | Comment |
+| victoria; arc              | GeForce GT 630   | cc3.0 |  1 |  2 GB | desktop machine |
+| athena                     | GeForce GTX 1080 | cc6.1 |  1 |  8 GB | Tom's desktop machine |
-Summary of future plans:
+Not used at the moment: GeForce GTX 570 (from twister2)
-  * Current Troja servers won't get any GPUs (the only option would be [[http://www.czc.cz/hp-quadro-k1200-4gb/171662/produkt?ppcbee-adtext-variant=Produkt%3B+kategorie+%2B+cena%3B+Pobo%C4%8Dky&gclid=CKbKkbrWrswCFQUq0wodHDELCw|Quadro K1200 4GB]], horribly cost-inefficient)
+All machines have CUDA8.0 and should support both Theano and TensorFlow.
-  * 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.
+=== Disk space ===
+All the GPU machines are at Malá Strana (not at Troja), so you should not use ''/lnet/tspec/work/'', but you should use:
+- ''/lnet/spec/work/'' (alias ''/net/work/'') - Lustre disk space at Malá Strana
+- ''/net/cluster/TMP'' - NFS hard disk for temporary files, so slower than Lustre for most tasks
+- ''/net/cluster/SSD'' - also NFS, but faster then TMP because of SSD
+- ''/COMP.TMP'' - local (for each machine) space for temporary files (use it instead of ''/tmp''; over-filling ''/COMP.TMP'' should not halt the system).
 === Individual acquisitions: NVIDIA Academic Hardware Grants ==
@@ Line 38: / Line 40: @@
   * Ondřej Plátek - granted (2015)
   * Jan Hajič jr. - granted (early 2016)
-  * Jindra Helcl - planning to apply (fall 2016)
@@ Line 45: / Line 46: @@
 ===== How to use cluster =====
-In this section will be explained how to use cluster properly. Rule number one, always use the GPU queue (never log in machine by ssh).
+In this section will be explained how to use cluster properly.
+==== Set-up CUDA and CUDNN ====
+You can add following command into your ~/.bashrc
+  CUDNN_version=6.0
+  CUDA_version=8.0
+  CUDA_DIR_OPT=/opt/cuda-$CUDA_version
+  if [ -d "$CUDA_DIR_OPT" ] ; then
+    CUDA_DIR=$CUDA_DIR_OPT
+    export CUDA_HOME=$CUDA_DIR
+    export THEANO_FLAGS="cuda.root=$CUDA_HOME,device=gpu,floatX=float32"
+    export PATH=$PATH:$CUDA_DIR/bin
+    export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CUDA_DIR/cudnn/$CUDNN_version/lib64:$CUDA_DIR/lib64
+    export CPATH=$CUDA_DIR/cudnn/$CUDNN_version/include:$CPATH
+  fi
 ==== TensorFlow Environment ====
@@ Line 71: / Line 88: @@
 For testing and using the cluster interactively you can use qrsh (this should not be used for long running experiments since the console is not closed on the end of the experiment). Following command will assign you a GPU and creates interactive console.
-  qrsh -q gpu.q -l gpu=1 -pty yes bash
+  qrsh -q gpu.q -l gpu=1,gpu_ram=2G -pty yes bash
 For running experiments you must use qsub command:
@@ Line 81: / Line 98: @@
   qsubmit --gpumem=2G --queue="gpu.q" WHAT_SHOULD_BE_RUN
+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 ====
+  lspci
+    # is any such hardware there?
+  nvidia-smi
+    # more details, incl. running processes on the GPU
+    # nvidia-* are typically located in /usr/bin
+  watch nvidia-smi
+    # For monitoring GPU activity in a separate terminal (thanks to Jindrich Libovicky for this!)
+  nvcc --version
+    # this should tell CUDA version
+    # nvcc is typically installed in /usr/local/cuda/bin/
+  theano-test
+    # dela to vubec neco uzitecneho? :-)
+    # theano-* are typically located in /usr/local/bin/
+  /usr/local/cuda/samples/1_Utilities/deviceQuery/deviceQuery
+    # shows CUDA capability etc.
+  ssh dll1; ~popel/bin/gpu_allocations
+    # 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:
+  /opt/cuda/samples/1_Utilities/deviceQuery/deviceQuery | grep ^Device
 ===== Performance tests =====
 * [[http://www.trustedreviews.com/nvidia-geforce-gtx-1080-review-performance-benchmarks-and-conclusion-page-2| 980 vs 1080 vs Titan X (not the Titan Z we have)]]
-In the following table is the experiment conducted by Tom Kocmi. You can replicate experiment: /a/merkur3/kocmanek/Projects/GPUBenchmark (you will need to prepare environment of TensorFlow11 or use my ANACONDA)
+In the following table is the experiment conducted by Tom Kocmi. You can replicate experiment: /a/merkur3/kocmanek/Projects/GPUBenchmark (you will need to prepare environment of TensorFlow11 or use my ANACONDA). The benchmark uses 2GB model of seq2seq machine translation in Neural Monkey (De > EN). If not specified, the benchmark had an access only to one GPU.
 | machine | Setup; CPU/GPU; [[https://en.wikipedia.org/wiki/CUDA#Supported_GPUs|Capability]] [cc] | Walltime | Note |
 | athena     | GeForce GTX 1080; cc6.1            |    9:55:58 | Tom's desktop  |
-| dll2       | (2 GPU) GeForce GTX 1080; cc6.1    |   10:19:40 | with CUDA_VISIBLE_DEVICES=0 |
+| dll2       | GeForce GTX 1080; cc6.1            |   10:19:40 |  |
+| titan      | GeForce GTX 1080 Ti                |   10:45:11 | (new result with correct CUDA version) |
 | dll1       | (2 GPU) GeForce GTX 1080; cc6.1    |   12:34:34 | Probably only one GPU was used |
-| dll2       | (2 GPU) GeForce GTX 1080; cc6.1    |   13:01:05 | Only one GPU was used |
+| dll2       | GeForce GTX 1080; cc6.1            |   13:01:05 | Only one GPU was used |
 | titan-gpu  | (2 GPU) GeForce GTX Titan Z; cc3.5 |   16:05:24 | Probably only one GPU was used |
 | kronos-dev | Tesla K40c; cc3.5                  |   22:41:01 |  |
@@ Line 103: / Line 148: @@
 | arc        | GeForce GT 630; cc3.0              |  103:42:30 | (approximated after 66 hours) |
 | lucifer4   | 8x cores CPU                       |  134:41:22 |  |
-| victoria   | GeForce GT 630; cc3.0              |        --- | never run, same GPU as Arc |
-===== Installed toolkits =====
+=== Second Benchmark ===
-//This should mention where each interesting toolkit lives (on a particular machine).//
+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.
-==== TensorFlow ====
+| GPU; Cuda capability       | GPU RAM |  Walltime | Batch size | Machine |
+| Tesla K40c; cc3.5          |   12 GB |           |        |  |
-[[https://redmine.ms.mff.cuni.cz/projects/mmmt/repository/revisions/6a064187fc6959db9b77cf2d5350c5f4918a8067/entry/prepare_env.sh|This script]] installs TensorFlow 0.7.1 (and all other dependencies we need for Multimodal Translation) into `tf' and `tf-gpu' virtual environments. The GPU environment can be loaded by calling <code>source tf-gpu/bin/activate-gpu</code>
+| 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 |
-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`
+| 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 |
-=== Select GPU device ===
-Use variable CUDA_VISIBLE_DEVICES to constrain tensorflow to compute only on the selected one. For the use of first GPU use:
-<code>export CUDA_VISIBLE_DEVICES=0</code>
-To list available devices, use:
-<code>/opt/cuda/samples/1_Utilities/deviceQuery/deviceQuery | grep ^Device</code>
-===== Basic commands =====
-  lspci
-    # is any such hardware there?
-  nvidia-smi
-    # more details, incl. running processes on the GPU
-    # nvidia-* are typically located in /usr/bin
-  watch nvidia-smi
-    # For monitoring GPU activity in a separate terminal (thanks to Jindrich Libovicky for this!)
-  nvcc --version
-    # this should tell CUDA version
-    # nvcc is typically installed in /usr/local/cuda/bin/
-  theano-test
-    # dela to vubec neco uzitecneho? :-)
-    # theano-* are typically located in /usr/local/bin/
-  /usr/local/cuda/samples/1_Utilities/deviceQuery/deviceQuery
-    # shows CUDA capability etc.
 ===== Links =====

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