[ Skip to the content ]

Institute of Formal and Applied Linguistics Wiki

[ Back to the navigation ]

You are here: start » gpu

Differences

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

Link to this comparison view

Both sides previous revision Previous revision
Next revision 		Previous revision
Next revision Both sides next revision
gpu [2017/03/16 17:09]
kocmanek [How to use cluster] 		gpu [2017/03/16 17:10]
kocmanek [TensorFlow]
Line 81: Line 81:
      
 ==== Basic commands ==== ==== 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.
 ===== Performance tests ===== ===== Performance tests =====
  
Line 109: Line 125:
 //This should mention where each interesting toolkit lives (on a particular machine).// //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 ===== ===== Links =====

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