  Nondeterminism in neural network optimization produces uncertainty in
performance, making small improvements difficult to discern from run-to-run
variability. While uncertainty can be reduced by training multiple model
copies, doing so is time-consuming, costly, and harms reproducibility. In this
work, we establish an experimental protocol for understanding the effect of
optimization nondeterminism on model diversity, allowing us to isolate the
effects of a variety of sources of nondeterminism. Surprisingly, we find that
all sources of nondeterminism have similar effects on measures of model
diversity. To explain this intriguing fact, we identify the instability of
model training, taken as an end-to-end procedure, as the key determinant. We
show that even one-bit changes in initial parameters result in models
converging to vastly different values. Last, we propose two approaches for
reducing the effects of instability on run-to-run variability.
