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--- gpu [2017/03/16 17:10]
kocmanek [Basic commands]
+++ gpu [2017/03/16 17:11]
kocmanek [Basic commands]
@@ Line 97: / Line 97: @@
   /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 =====
@@ Line 125: / Line 134: @@
 //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>

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