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Metric standardizing in ai runtime #163
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brosoul
commented
Sep 12, 2024
Jeffwan
reviewed
Sep 12, 2024
BTW, beside the filtered metrics, do you think we should expose rest of the application metrics? Let's have say
|
Currently, my approach is to retain the original engine metrics while adding some new metrics based on the rules. |
the change looks good to me |
Jeffwan
approved these changes
Sep 13, 2024
gangmuk
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Jan 25, 2025
* feat: init standard metric framework * feat: add engine metric scrape support * format * format * adjust server run port * mv constant config to config.py * style * fix: vllm metric standard rule
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Pull Request Description
AI Runtime merge self metrics with enigne metrics. At present, only
Rename
standardizing rule of the metrics of the main pod is supported. The main data structures added are as follows:HTTPCollector
provides metrics collect from other http sourceStandardRule
provides the detail implemention of metric mergeXXX_METRIC_STANDARD_RULES
inengine_rules.py
records metric merge rules for various inference enginesAs shown in the following figure, AI Runtime rename

vllm:request_success_total
toaibrix:request_success_total
according to the rulesVLLM_METRIC_STANDARD_RULES
(Meanwhile, the original indicators were retained).Related Issues
Resolves: #106
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