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Ollama and local LLM batch jobs with RunVouch

With a local model the failure modes change: the process hangs while the model loads, the GPU is taken by another job, or a model update changes output length by half. None of them cost money, all of them cost the result.

How it runs on Ollama and local LLM batch jobs

Wrap the batch script with rv run and set --max-runtime on the agent so a hang becomes STALLED.

Store the key

An environment variable in the cron environment.

Wrap the job

rv agent nightly-classify --cadence 24h --max-runtime 2h --evidence
0 1 * * * rv run nightly-classify --evidence-file /data/labels.jsonl -- python classify.py --model llama3.1

rv fails open: if RunVouch is unreachable the job still runs and you get one warning line.

Register the cadence and caps

rv agent nightly-report --cadence 24h --grace 30m --max-runtime 1h --evidence --cap-run-cost 2

Register the agent once, from anywhere with the key. Cadence is what turns a schedule that stopped into an alert; --evidence makes a run without evidence a failure; the caps pause the agent when it overspends.

What goes silent on Ollama and local LLM batch jobs

• A hang has no exit code; STALLED fires when max runtime passes without an end.
• Output size drift (DRIFT) is the first sign a model update or a prompt change broke the pipeline.
• Zero cost is fine; leave the cost caps unset and keep evidence and drift.

What RunVouch detects

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