How do TensorFlow systems handle partial failures in distributed training clusters?

They use checkpoint recovery, worker replacement, and fault-tolerant strategies.

In distributed TensorFlow training, node failures are expected. Systems mitigate this by periodically saving checkpoints, allowing failed workers to be replaced and training to resume. However, synchronization state must be carefully managed to avoid corruption or divergence across workers.