Most ML teams don't have perfect queue visibility. Until they do, how can they maintain experiment velocity? Here are practical strategies we've seen work.
Strategy #1: Batch Your Submits
Instead of submitting jobs as you think of them, collect experiments and submit in batches at predictable times.
Strategy #2: Have Backup Work Ready
When your GPU job is queued, what will you work on? The best teams always have CPU-bound work ready.
Strategy #3: Use Your Queue Insights
Even without prediction tools, you can observe patterns. When are queues shortest? Which job sizes move fastest?
Strategy #4: Right-Size Requests
The fastest way to slow down your queue time is to over-request resources. Be honest about what you actually need.
Strategy #5: Communicate Proactively
If you're blocked on a GPU job, tell your team. If you see queue patterns, share them.
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