Differences

This shows you the differences between two versions of the page.

--- gpu [2017/03/16 17:09]
kocmanek [How to use cluster]
+++ gpu [2017/07/11 13:10]
ufal [Servers with GPU units]
@@ Line 5: / Line 5: @@
 ===== Servers with GPU units =====
-| machine | GPU; [[https://en.wikipedia.org/wiki/CUDA#Supported_GPUs|Capability]] [cc]  | cores | GPU RAM | Comment |
+| machine                    | GPU; [[https://en.wikipedia.org/wiki/CUDA#Supported_GPUs|Capability]] [cc]  | cores | GPU RAM | Comment |
-| titan-gpu  | GeForce GTX Titan Z; cc3.5 | 2 | 6 GB each core |        |
+| titan                      | GeForce GTX 1080 Ti; cc6.1 | 1  | 11 GB           |  |
-| twister1   | Tesla K40c; cc3.5          | 1 | 12 GB          |        |
+| titan-gpu                  | GeForce GTX Titan Z; cc3.5 | 2  | 6 GB each core  |  |
-| 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          |        |
+| iridium                    | Quadro K2000; cc3.0        | 1  | 2 GB            |  |
-| iridium    | Quadro K2000; cc3.0        | 1 | 2 GB           |        |
+| victoria; arc              | GeForce GT 630; cc3.0      | 1  | 2 GB            | desktop machine |
-| victoria   | GeForce GT 630; cc3.0      | 1 | 2 GB           | Ondrej Bojar's desktop machine |
+| athena                     | GeForce GTX 1080; cc6.1    | 1  | 8 GB            | Tom's desktop machine |
-| arc        | GeForce GT 630; cc3.0      | 1 | 2 GB           | Ales's desktop machine |
+| dll1; dll2                 | GeForce GTX 1080; cc6.1    | 8  | 8 GB each core  |  |
-| athena     | GeForce GTX 1080; cc6.1    | 1 | 8 GB           | Tom's desktop machine |
+| dll3; dll4; dll5           | GeForce GTX 1080 Ti; cc6.1 | 10 | 11 GB each core |  |
-| 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)
@@ Line 38: / Line 36: @@
   * Ondřej Plátek - granted (2015)
   * Jan Hajič jr. - granted (early 2016)
-  * Jindra Helcl - planning to apply (fall 2016)
@@ Line 70: / Line 67: @@
 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 80: / Line 77: @@
   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.
+=== 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 (GPU queue do this for you):
+  export CUDA_VISIBLE_DEVICES=0
+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.
 | 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 |
+| titan      | GeForce GTX 1080 Ti                |   11:41:08 |  |
 | 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 |
@@ Line 105: / Line 129: @@
-===== Installed toolkits =====
-//This should mention where each interesting toolkit lives (on a particular machine).//
-==== TensorFlow ====
-[[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>
-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`
-=== 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 =====

[ Back to the navigation ] [ Back to the content ]

Institute of Formal and Applied Linguistics Wiki

Differences