January 2018
Intermediate to advanced
474 pages
10h 23m
English
Content preview from Mastering TensorFlow 1.x
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GPU memory handling
When you start running the TensorFlow session, by default it grabs all of the GPU memory, even if you place the operations and variables only on one GPU in a multi-GPU system. If you try to run another session at the same time, you will get out of memory error. This can be solved in multiple ways:
- For multi-GPU systems, set the environment variable CUDA_VISIBLE_DEVICES=<list of device idx>
os.environ['CUDA_VISIBLE_DEVICES']='0'
The code executed after this setting will be able to grab all of the memory of only the visible GPU.
- When you do not want the session to grab all of the memory of the GPU, then you can use the config option per_process_gpu_memory_fraction to allocate a percentage of memory:
config.gpu_options.per_process_gpu_memory_fraction ...
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