gpu_resources
====== Differences ====== This shows you the differences between two versions of the page.
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gpu_resources [2017/06/08 17:17] adoyle [Preventing Job Clobbering] |
gpu_resources [2017/06/08 17:48] (current) adoyle [Preventing Job Clobbering] |
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| ===== Preventing Job Clobbering ===== | ===== Preventing Job Clobbering ===== | ||
| + | There are currently 3 GPU's in ace-gpu-1. To select one of the three (0, 1, 2), set the CUDA_VISIBLE_DEVICES environment variable. This can be accomplished by adding the following line to your ~/.bash_profile file on ace-gpu-1, where X is either 0, 1 or 2: | ||
| + | |||
| + | <code> | ||
| + | export CUDA_VISIBLE_DEVICES=X | ||
| + | </code> | ||
| + | |||
| + | This will only take effect when you log in, so log out and back in and try the following to ensure that it worked: | ||
| + | |||
| + | <code> | ||
| + | echo $CUDA_VISIBLE_DEVICES | ||
| + | </code> | ||
| + | |||
| + | If it outputs the ID that you selected then you're ready to use the GPU. | ||
| + | |||
| + | ==== Sharing a single GPU ==== | ||
| To configure TensorFlow to not pre-allocate all GPU memory you can use the following Python code: | To configure TensorFlow to not pre-allocate all GPU memory you can use the following Python code: | ||
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| </code> | </code> | ||
| + | This has been found to work only to a certain extent, and when there are several jobs that use a significant amount of the GPU resources, jobs can still be ruined even when using the above code | ||
| ===== GPU Info ===== | ===== GPU Info ===== | ||
gpu_resources.1496942220.txt.gz · Last modified: 2017/06/08 17:17 by adoyle
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