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--- gpu_resources [2017/05/05 15:25] – [nvidia-smi flags used] csteel
+++ gpu_resources [2024/03/26 13:52] (current) – external edit 127.0.0.1
@@ Line 28: / Line 28: @@
 ===== Preventing Job Clobbering =====
-Today I was training a model and inadvertently kicked Konrad's job off the GPU. I discovered how to configure TensorFlow so that it doesn't do this:
+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:
 <code>
@@ Line 38: / Line 53: @@
 </code>
-We should develop some kind of policy to run jobs on ace-gpu-1 so that we don't inadvertently ruin other peoples' processes.
+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 =====
@@ Line 70: / Line 84: @@
 nsight
 </code>
+Nvidia Visual Profiler (https://developer.nvidia.com/nvidia-visual-profiler) would be useful for GPU monitoring if we had X visualization, but we do not:
+<code>
+/usr/local/cuda/bin/nvvp
+</code>
 ===== GPU Accounting =====