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I am using Celery workers which run a single tensorflow model in solo concurrency mode. The current workaround is to keep track of per process gpu fraction and
by aub3bhat 9y ago
I am using Celery workers which run a single tensorflow model in solo concurrency mode. The current workaround is to keep track of per process gpu fraction and then incorporate information about Total GPU memory available when assigning workers/celery process to individual GPUs.
- jacquesm 9y agoRight, so essentially you are doing per process memory management because the layers lower down aren't smart enough to handle this transparently. I'm fairly sure that the nvidia libraries do support releasing memory and from what I see on my own machine memory usage of the GPU does go up and down when running longer jobs. So maybe it isn't so much a structural problem as a problem in one particular module that you exercise a lot?