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[Metrics] Add average kv cache and waiting queue size metrics for inference pool #304
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Original file line number | Diff line number | Diff line change |
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# HELP inference_pool_average_kv_cache_utilization [ALPHA] The average kv cache utilization for an inference server pool. | ||
# TYPE inference_pool_average_kv_cache_utilization gauge | ||
inference_pool_average_kv_cache_utilization{name="p1"} 0.3 |
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Original file line number | Diff line number | Diff line change |
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@@ -0,0 +1,3 @@ | ||
# HELP inference_pool_average_queue_size [ALPHA] The average number of requests pending in the model server queue. | ||
# TYPE inference_pool_average_queue_size gauge | ||
inference_pool_average_queue_size{name="p1"} 0.4 |
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@raywainman we are reporting average queue length across model servers, what alternatives would you suggest to use this for HPA? Can HPA consume a distribution and do the aggregation on its end and so the user have more flexibility on how to aggregate?
/cc @smarterclayton
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HPA can't consume a distribution directly today unless we put a Prometheus adapter infront of the metric and convert it to a direct gauge metric (which is doable). For example you could do something like "Get 90%ile queue size over the last 5 minutes" this way. Do we anticipate that being useful?
If so we could maybe emit both?
One simple gauge metric emitting the instantaneous average queue size across all model servers and another metric with a distribution.
@JeffLuoo what do you think?
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In our benchmarking, we scrape gauge metrics for cache utilization and queue size. Let's discuss whether distribution for queue size is more helpful or other metrics from model servers are more helpful.
Inference pool metrics are calculated from metrics from model servers (vLLM in current implementation) directly.
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Let's target to have new metrics added in a follow-up CL (e.g. percentiles) to unblock this CL.
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That sounds great, made #306 to track