| name | run |
| description | Run a benchmark experiment using a TOML config file. Use when the user wants to run, train, or sweep experiments — single dataset/model or multi-axis sweeps — against any of the available configs/runs/ configs. |
When to use / what to ask
If the user has not specified a config, ask which experiment they want to run and suggest:
| Intent | Config |
|---|
| Single dataset + model | configs/runs/run_single_data.toml |
| Sweep over models | configs/runs/sweep_model.toml |
| Sweep over datasets | configs/runs/sweep_data.toml |
| Multi-axis sweep | configs/runs/multi_sweep.toml |
| GIFT-EVAL benchmark | configs/runs/gift_eval_sweep.toml |
Commands
uv run python tool/inspect_config.py --config <config_path>
uv run modern-tsf --config <config_path>
uv run python tool/tsf.py run <config ...> [--jobs N] [--gpus 0,1]
Config knobs (set in the TOML, not the CLI)
[training.tricks] — grad_clip, grad_accum, curriculum, aux-loss options.[evaluation] strategy = "rolling" — rolling forecast instead of single-shot eval.[evaluation] enable_profile = true — record params/MACs/latency per run.- For ablation/hyperparameter sweeps and forecast case plots, use the
experiments skill.
Notes
- Results land in
work_dirs/<dataset>/<model>/performance.csv; offer to aggregate (and optionally plot) afterwards via the aggregate skill.
Reference
Config structure, extends, and sweep syntax: docs/en/configs.md; tsf runner: docs/en/scripts.md.
