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description: EvalHub&#x27;s bring-your-own-framework (BYOF) pattern lets teams integrate custom or proprietary AI evaluation frameworks into the platform by implementing a...
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# Bring your own evaluation framework to EvalHub

**[Red Hat Developer](https://daily.dev/sources/rhdev)** · 9 min read · 0 upvotes · 0 comments

## Summary

EvalHub's bring-your-own-framework (BYOF) pattern lets teams integrate custom or proprietary AI evaluation frameworks into the platform by implementing a single Python method (`run_benchmark_job`) and packaging it as a container image. The guide covers the three core data structures — `JobSpec` (job inputs), `JobCallbacks` (progress reporting and artifact persistence), and `JobResults` (metrics output) — along with a complete minimal adapter implementation. Once containerized and registered, custom adapters gain full platform benefits: MLflow experiment tracking, OCI artifact persistence, Kubernetes orchestration via Kueue, and participation in weighted collection scoring alongside built-in benchmarks like lm-evaluation-harness and Garak. Local development mode (`EVALHUB_MODE=local`) allows testing without a cluster before deploying to OpenShift.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://developers.redhat.com/articles/2026/06/09/bring-your-own-evaluation-framework-evalhub>

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---

Tags: [#machine-learning](https://daily.dev/tags/machine-learning), [#python](https://daily.dev/tags/python), [#kubernetes](https://daily.dev/tags/kubernetes), [#llm](https://daily.dev/tags/llm)

[View this post on daily.dev](https://daily.dev/posts/bring-your-own-evaluation-framework-to-evalhub-avtfws2qe)

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