Distributed training has a silent killer: straggler nodes.
In synchronous setups, every worker waits for the slowest one. One lagging GPU blocks the entire cluster.
In async setups, you get stale gradients that corrupt model convergence. The slow node's updates are outdated by the time they arrive.
Worse: most frameworks only detect stragglers AFTER wasting compute cycles. By the time you notice, you've burned hours on a broken training run.
The real problem: detection happens too late. You need proactive monitoring before the entire run tanks.
In synchronous setups, every worker waits for the slowest one. One lagging GPU blocks the entire cluster.
In async setups, you get stale gradients that corrupt model convergence. The slow node's updates are outdated by the time they arrive.
Worse: most frameworks only detect stragglers AFTER wasting compute cycles. By the time you notice, you've burned hours on a broken training run.
The real problem: detection happens too late. You need proactive monitoring before the entire run tanks.