====== Differences ====== This shows you the differences between two versions of the page.
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gpu_resources [2017/06/08 17:20] 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 ~/.bashrc file on ace-gpu-1, where X is either 0, 1 or 2: | + | 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> | <code> | ||
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</code> | </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 ===== | ||