Gahow Wang
9f52812753
CPU calibration (chat vs coder) reproduces the paper's C-slowest ordering and shows C-convergence difficulty is driven by signal noise (low-reuse chat) not reuse magnitude. GPU fidelity check on Qwen3-30B-A3B: truncating at the L-C-A convergence prefix saves ~52% replay (tau_c=0.90) with 3/4 probe verdicts preserved; the one mismatch is a boundary false-positive at the feasibility knee (prefix 0.96 vs full 0.946), caused by second-half engine-state drift the offered L-C-A cannot see. Argues for revisiting the SLO-boundary guard before enabling. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
AITuner
AITuner is a small study orchestrator for OpenAI-compatible serving engines. It replays trace windows, searches for the highest feasible offered load under configured SLOs, and records enough trial context for LLM- or harness-guided configuration proposals.
Status
This repository is research tooling. Treat reported experiment numbers as valid
only when the matching study spec, trial artifacts, probe history, and
probe_details.jsonl files are available for audit.
Install
python3 -m pip install -e .
Test
The test suite uses the Python standard library unittest runner:
PYTHONPATH=src python3 -m unittest discover -s tests -v
If the package is installed in editable mode, PYTHONPATH=src is optional.
Basic Workflow
Initialize a study:
aituner study init --spec configs/examples/study.example.json
Run a local tuning loop:
aituner study tune --spec configs/examples/study.example.json --max-trials 2
Run a compare:
aituner compare run --spec configs/examples/compare.example.json
Remote experiment notes for this checkout live in AGENTS.md. The default
remote host is dash0, and code should be synchronized through Git before
remote runs.
Experiment Integrity
- Fixed-length replay requests are scored only when completion token usage is verifiable and matches the trace expectation.
- Each trial writes aggregate probe history and per-request probe details.
request_rate_per_gpuis the primary cross-topology metric:best_feasible_request_rate / (tensor_parallel_size * data_parallel_size).- Compare reports include failed and no-feasible window counts; do not interpret mean request rates without those counts.
- Bounded replays using
max_requests_per_probe,completion_tokens_override, orreplay_time_scaleare convergence tests for that bounded workload, not production benchmarks.
Configuration Notes
Example specs that use llm.endpoint.provider=codex resolve the endpoint from
the local Codex configuration unless llm.endpoint.base_url or
AITUNER_CODEX_BASE_URL is set. Public, reproducible examples should prefer an
explicit endpoint or omit the LLM endpoint and use proposal files.