M2: Harden matrix execution (resume/timeout), add coverage notebooks, and expand M2 tests#6
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guru-code-expert wants to merge 22 commits intoAgentOpt:mainfrom
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M2: Harden matrix execution (resume/timeout), add coverage notebooks, and expand M2 tests#6guru-code-expert wants to merge 22 commits intoAgentOpt:mainfrom
guru-code-expert wants to merge 22 commits intoAgentOpt:mainfrom
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Implements the M1 milestone for Trace-Bench: CLI surface: - trace-bench list-tasks, list-trainers, validate --config --strict, run, ui - Strict validation: trainer kwarg checking, optimizer/guide/logger resolution, trainable parameter detection, matrix expansion with manifest output Runner & training: - BenchRunner with deterministic SHA256-based job IDs - Algorithm-aware kwarg mapping (PrioritySearch vs GEPA-Base/UCB/Beam) - DummyLLM stub mode for offline testing - Training error capture in feedback field Canonical artifact layout: - meta/config.snapshot.yaml, manifest.json, env.json (redacted), git.json - Per-job: job_meta.json, results.json, events.jsonl, artifacts/, tb/ - Run-level: results.csv (16 columns) + summary.json Task coverage: - 4 internal types (code_param, numeric_param, multi_param, non_trainable) - trace_examples:greeting_stub - llm4ad:circle_packing (bounded timeout) - veribench:smoke_placeholder (NotImplementedError stub) Trainer coverage: - PrioritySearch + GEPA-Base exercised in real mode - GEPA-UCB + GEPA-Beam configured (M4 scope) Tests: 30 pass, 2 skipped (m0 smoke, m1 artifacts, matrix e2e, internal tasks, opentrace examples, trainer config, veribench CLI) Notebook: 01_m1_minimal_api.ipynb with Colab badge, auto-detect API key (real/stub mode), 2x2 matrix smoke (4/4 ok), executed outputs committed.
This reverts commit 51622f2.
…nd crossbench smoke
# Conflicts: # LLM4AD/benchmark_tasks/science_discovery_bactgrow/__init__.py # LLM4AD/benchmark_tasks/science_discovery_ode_1d/__init__.py # LLM4AD/benchmark_tasks/science_discovery_oscillator1/__init__.py # LLM4AD/benchmark_tasks/science_discovery_oscillator2/__init__.py # LLM4AD/benchmark_tasks/science_discovery_stresstrain/__init__.py # configs/m2_coverage.yaml # notebooks/02_m2_coverage.ipynb # notebooks/04_m2_full_coverage.ipynb # trace_bench/runner.py
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Summary
This PR delivers M2 hardening for Trace-Bench runner behavior and validation flow:
What changed
Runner / CLI hardening
--resume auto|failed|none(defaultauto)--force(rerun all)max_workers, incl. modest parallel runs).Resume semantics
auto: reuse completed jobs, rerun failed/new.failed: rerun only failed; skip completed and never-run.none/--force: rerun everything.jobs/<job_id>/job_meta.json.Backward compatibility
LLM4AD/trainers_benchmark.pyremains as a compatibility wrapper:trace-bench runtrace-benchdirectly.Configs / notebooks
configs/m2_coverage.yamlconfigs/m2_optimizing_subset.yamlnotebooks/02_m2_coverage.ipynb(main M2 workflow, OpenRouter path)notebooks/02a_m2_coverage_openai.ipynb(alternate backend validation notebook)notebooks/04_m2_full_coverage.ipynb(full/high-resource coverage flow)Tests
tests/m2/*for:Validation
Local test run:
PYTEST_DISABLE_PLUGIN_AUTOLOAD=1 pytest -q66 passed, 7 skipped, 0 failedNotes for reviewer
notebooks/02_m2_coverage.ipynb.notebooks/04_m2_full_coverage.ipynb.