areno-tune-capacity

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

1. Record GPU count/memory, model config, dtype, TP constraints, optimizer, and semantic token lengths.
2. Measure rollout with --smoke-infer when useful; it must allocate cache and capture decode graphs.
3. Measure train with --smoke-train; it skips rollout/prefill and uses the candidate microbatch.
4. Use --tune-params when requested. Keep peak memory at or below the requested fraction, never above 0.9 when selecting a default safety target.
5. Avoid excessive probes. Probe to make a decision, then confirm the chosen setting.
6. 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.