How does TensorFlow ensure reproducibility in large-scale distributed training?

Reproducibility is enforced using deterministic ops, fixed seeds, and controlled execution ordering.

Distributed training introduces randomness through initialization, data shuffling, and parallel execution order. TensorFlow can enforce partial determinism using seeded randomness, deterministic GPU kernels, and controlled data pipelines. However, full reproducibility is difficult due to hardware-level floating-point differences.