Why do identical Keras models sometimes produce different results across runs even with fixed seeds?

Non-determinism arises from GPU operations, multithreading, and non-deterministic kernels.

Even with fixed seeds, TensorFlow operations on GPUs may use atomic operations or parallel reductions that are not strictly deterministic. Data loading order, floating-point precision differences, and library-level optimizations also introduce variability.