fix: TensorFlow memory and performance fixes#26
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jfilling merged 2 commits intoPassiveLogic:mainfrom Jul 31, 2024
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Looks great, thanks!
jfilling
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When running BuildingSimulation benchmarks, I observed a very high amount of memory usage (MacBook Pro, M1 Max, 32GB RAM) during high-timestep simulations using TensorFlow. Some cases (100,000 timestep, single trial) failed to complete, with warnings thrown from TF about 'large unrolled loops'. TF documentation suggested using TensorFlow's control flow:
tf.range. To keep each trial isolated and independent, I'm keeping the trials'rangeusage as Python's.The observed effect of these two changes is that overall memory usage is reduced, lowering RAM hardware requirements, and performance is increased by decorating the
getGradientfunction with@tf.function. This decoration is recommended to avoid eager execution, and is a basic and straightforward measure to increase performance.