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--- gpu [2017/01/09 15:19]
kocmanek
+++ gpu [2017/03/16 17:09]
kocmanek [How to use cluster]
@@ Line 14: / Line 14: @@
 | arc        | GeForce GT 630; cc3.0      | 1 | 2 GB           | Ales's desktop machine |
 | athena     | GeForce GTX 1080; cc6.1    | 1 | 8 GB           | Tom's desktop machine |
-| dll1     | GeForce GTX 1080; cc6.1    | 2 | 8 GB each core |  |
+| dll1     | GeForce GTX 1080; cc6.1    | 8 | 8 GB each core |  |
-| dll2     | GeForce GTX 1080; cc6.1    | 2 | 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 30: / Line 30: @@
 === Individual acquisitions: NVIDIA Academic Hardware Grants ==
-There is an easy way to get one high-end GPU: [[https://developer.nvidia.com/academic_gpu_seeding|ask NVIDIA for an Academic Hardware Grant]]. All it takes is writing a short grant application (at most ~2 hrs of work from scratch; if you have a GAUK, ~15 minutes of copy-pasting). Due to the GPU housing issues (mainly rack space and cooling), Milan F. said we should request the Tesla-line cards. If you want to have a look at an application, feel free to ask at hajicj@ufal.mff.cuni.cz :)
+There is an easy way to get one high-end GPU: [[https://developer.nvidia.com/academic_gpu_seeding|ask NVIDIA for an Academic Hardware Grant]]. All it takes is writing a short grant application (at most ~2 hrs of work from scratch; if you have a GAUK, ~15 minutes of copy-pasting). Due to the GPU housing issues (mainly rack space and cooling), Milan F. said we should request the Tesla-line cards (2017 check with Milan about this issue). If you want to have a look at an application, feel free to ask at hajicj@ufal.mff.cuni.cz :)
 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 "seeding" grants meant for researchers to try out GPUs (and fall in love with them, and buy a cluster later). If you are planning to submit the hardware grant, have submitted one, or have already been awarded one, please add yourself here.
@@ Line 43: / Line 43: @@
+===== How to use cluster =====
+In this section will be explained how to use cluster properly.
+==== TensorFlow Environment ====
+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.
+  pip install tensorflow
+  pip install tensorflow-gpu
+You can use prepared environment by adding into your ~/.bashrc
+  export PATH=/a/merkur3/kocmanek/ANACONDA/bin:$PATH
+And then you can activate your environment:
+  source activate tf1
+  source activate tf1cpu
+This environment have TensorFlow 1.0 and all necessary requirements for NeuralMonkey.
+==== Using cluster ====
+Rule number one, always use the GPU queue (never log in machine by ssh). Always use qsub or qsubmit with proper arguments.
+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
+For running experiments you must use qsub command:
+  qsub -q gpu.q -l gpu=1,gpu_cc_min3.5=1,gpu_ram=2G WHAT_SHOULD_BE_RUN
+Cleaner way to use cluster is with /home/bojar/tools/shell/qsubmit
+  qsubmit --gpumem=2G --queue="gpu.q" WHAT_SHOULD_BE_RUN
+==== Basic commands ====
 ===== 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/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)
-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://en.wikipedia.org/wiki/CUDA#Supported_GPUs|Capability]] [cc] | Walltime | Note |
-| athena     | GeForce GTX 1080; cc6.1            |  9:55:58 | Tom's desktop  |
+| 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       | (2 GPU) GeForce GTX 1080; cc6.1    |   10:19:40 | with CUDA_VISIBLE_DEVICES=0 |
-| dll1       | (2 GPU) GeForce GTX 1080; cc6.1    |  12:34:34 | Probably only one GPU was used |
+| 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       | (2 GPU) 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 |
+| 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 |  |
+| kronos-dev | Tesla K40c; cc3.5                  |   22:41:01 |  |
-| twister2   | Tesla K40c; cc3.5                  |  22:43:10 |  |
+| twister2   | Tesla K40c; cc3.5                  |   22:43:10 |  |
-| twister1   | Tesla K40c; cc3.5                  |  24:19:45 |  |
+| twister1   | Tesla K40c; cc3.5                  |   24:19:45 |  |
-| helena1    | 16x CPU                            |  46:33:14 |  |
+| helena1    | 16x cores CPU                      |   46:33:14 |  |
-| belzebub   | 16x CPU                            |  52:36:56 |  |
+| belzebub   | 16x cores CPU                      |   52:36:56 |  |
-| iridium    | Quadro K2000; cc3.0                |  59:47:58 |  |
+| iridium    | Quadro K2000; cc3.0                |   59:47:58 |  |
-| helena7     | 8x CPU                            |  60:39:17 | approximated (still running) |
+| helena7    | 8x cores CPU                       |   60:39:17 |  |
 | arc        | GeForce GT 630; cc3.0              |  103:42:30 | (approximated after 66 hours) |
-| lucifer4   | 8x CPU                             |  134:41:22 |  |
+| lucifer4   | 8x cores CPU                       |  134:41:22 |  |
-| victoria   | GeForce GT 630; cc3.0              |  --- | never run, same GPU as Arc |
+| victoria   | GeForce GT 630; cc3.0              |        --- | never run, same GPU as Arc |
-A comparison with Ondrej's small data set:
-  * dll2 (2xGPU) takes 13m for one reporting period
-  * achilles2 (4xCPU with 8 CPUs reserved) takes 24m for one reporting period

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