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_gpu is 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, or replay_time_scale are 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.