Update help.md (#92)

Author: RaymondLi0

docs/help.md

@@ -28,7 +28,8 @@ Let's stay one step ahead of those pesky gotchas. Here's a list of common issues
     Watchdog caught collective operation timeout: WorkNCCL(SeqNum=408951, OpType=_ALLGATHER_BASE, … , Timeout(ms)=600000) ran for 600351 milliseconds before timing out
     ```
 
-    appearing across all GPU workers, it usually means one or more hosts failed to complete a NCCL operation, causing others to block. NCCL errors can be frustrating to diagnose since they rarely specify which node or GPU caused the issue. It is difficult to surface which messages and operations are in progress during these crashes. In most cases, the best we can do is to restart the training job and hope it doesn't happen again. If the issue persists, it might be because of network congestion or a problematic GPU. If the worker that crashed is consistent across multiple runs, it's likely a hardware issue. If you can't resolve it, open an issue on GitHub, and we'll help you troubleshoot.
+    appearing across all GPU workers, it usually means one or more hosts failed to complete a NCCL operation, causing others to block. NCCL errors can be frustrating to diagnose since they rarely specify which node or GPU caused the issue. It is difficult to surface which messages and operations are in progress during these crashes. If the issue happens at a specific moment of training like dataset preparation or model export, the issue might be that this specific procedure took too long and timed out other processes (e.g. when preparing large datasets for long training runs, or saving large models on slow storage). In this case, it can help to increase the timeout `distributed_timeout: 3600`.
+    In some other cases, the best we can do is to restart the training job and hope it doesn't happen again. If the issue persists, it might be because of network congestion or a problematic GPU. If the worker that crashed is consistent across multiple runs, it's likely a hardware issue. If you can't resolve it, open an issue on GitHub, and we'll help you troubleshoot.
 
 For more detailed solutions, check out our GitHub Issues page. Odds are someone's already tackled a similar problem, and you might find the exact fix you need.