| name | areno-tune-capacity |
| description | Fit an AReno training or rollout workload to available GPUs by tuning TP, mini-batch, batch, sample count, and rollout concurrency. Use for OOM prevention, memory headroom, smoke-infer, smoke-train, or tune-params requests. Do not change semantic token limits unless requested. |
Tune AReno Capacity
Inspect current train help and areno/cli/auto_tune.py. Validate relationships first:
python .agents/skills/areno-tune-capacity/scripts/check_capacity.py \
--batch-size N --n-samples N --max-running-prompts N \
--mini-bs N --world-size N --tp-size N
Workflow
- Record GPU count/memory, model config, dtype, TP constraints, optimizer, and semantic token lengths.
- Measure rollout with
--smoke-infer when useful; it must allocate cache and capture decode graphs.
- Measure train with
--smoke-train; it skips rollout/prefill and uses the candidate microbatch.
- Use
--tune-params when requested. Keep peak memory at or below the requested fraction, never above 0.9 when selecting a default safety target.
- Avoid excessive probes. Probe to make a decision, then confirm the chosen setting.
- Run a bounded real workload if the overall user goal is a working task.
Preserve max_new_tokens and max_context_len. Read references/parameter-relations.md before adjusting multiple dimensions.
