[FEAT] add log_metrics as Optuna trial user attrs#22
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Signed-off-by: Vincent Gimenes <vincent.gimenes@gmail.com>
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Pull request overview
Adds an optional optimization.log_metrics configuration to persist selected benchmark scalars onto Optuna trials as user_attrs (metric_<name>), improving Optuna Dashboard visibility without affecting objective values or study.tell() behavior.
Changes:
- Introduces
OptimizationConfig.log_metricswith validation/normalization against the same combined metric identifier vocabulary used by objectives. - Extends
StudyControllerto writemetric_<name>user attributes fromTrialResult.detailed_metrics(with float coercion and warnings on missing/non-numeric values). - Updates example config + documentation and adds unit tests for
log_metricsvalidation.
Reviewed changes
Copilot reviewed 5 out of 6 changed files in this pull request and generated 1 comment.
Show a summary per file
| File | Description |
|---|---|
auto_tune_vllm/core/config.py |
Adds log_metrics field and validates entries against allowed combined metric identifiers. |
auto_tune_vllm/core/study_controller.py |
Writes configured log_metrics values into Optuna trial user attributes after successful runs. |
docs/configuration.md |
Documents log_metrics behavior, constraints, and an example configuration. |
examples/study_config.yaml |
Shows how to configure log_metrics in the example study YAML. |
tests/core/test_optimization_config.py |
Adds unit tests covering log_metrics defaulting and validation errors. |
Comments suppressed due to low confidence (1)
examples/study_config.yaml:39
- This PR also removes the
gpu_memory_utilizationparameter block from the example configuration, but the PR description doesn’t mention this change. If this removal is intentional, it would help to either note it in the PR description or keep the example parameter to avoid an unrelated diff in this feature-focused PR.
parameters:
max_num_batched_tokens:
enabled: true
options: [1024, 2048, 10000]
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VincentG1234
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May 20, 2026
## Summary Add structured documentation for human maintainers and coding agents: `AGENTS.md`, `.ai/context/`, `.ai/skills/`, and a user-facing architecture guide with Mermaid diagrams. Link the new doc from README and quick start. Align the local example YAML with vLLM V1 constraints. ## Why The InseeFrLab fork needs a stable onboarding path for contributors and agents (local backend focus, safe commands, known issues/PRs) without reading the whole codebase. Architecture was previously only implicit in code; diagrams improve onboarding and reviews. ## What changed - `AGENTS.md` — entry point for agents (context files, skills, safe commands). - `.ai/context/` — repo map, execution flow, history, known issues, current work snapshot, external links. - `.ai/skills/` — pr-writer, pr-reviewer, test-writer, docs-writer, architecture-diagrams. - `docs/architecture.md` — end-to-end, layout, orchestration, trial lifecycle, outputs (Mermaid). - `README.md`, `docs/quick_start.md` — links to architecture doc. - `examples/study_config_local_exec.yaml` — disable `max_num_partial_prefills` (unsupported on V1; comment added). ## How tested - [x] `ruff check .` - [x] `pytest -v tests/` - [x] Manual E2E (maintainer): not required (docs-only PR) ## Risks / limitations - `.ai/context/current-work.md` is a point-in-time snapshot (open PRs #13, #17, #21, #22); it will drift until refreshed after merges. - Mermaid rendering depends on the viewer (GitHub, IDE); no runtime behavior change except the example YAML default. ## Links - (none — no issue closed) Signed-off-by: Vincent Gimenes <vincent.gimenes@gmail.com>
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Summary
Adds optional
optimization.log_metricsso selected benchmark scalars are copied onto each optimization trial as Optuna user attributes (metric_<name>) for dashboard visibility, without affecting objectives orstudy.tell().Motivation / context
Multi-objective runs already expose objectives in Optuna; teams still want extra latency/throughput snapshots in the dashboard for comparison and debugging without promoting every metric to an objective.
What changed
OptimizationConfig.log_metrics: optional list of identifiers validated against existing combined metric keys (same vocabulary as objectives).StudyController: after a successful optimization trial, sets user attrs fromdetailed_metrics(float coercion, warnings when missing/non-numeric).log_metricsvalidation.Testing
tests/core/test_optimization_config.py)