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compute

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Chooses and runs compute for research work, local shell or kernel versus a detached compute_job on this machine, a saved SSH or Slurm host, or Modal GPUs, with the target discovered from what is actually configured, the run sized and priced before dispatch, outputs declared, metrics tracked, and long work handed to the job system instead of a blocking shell. Use when a task needs a GPU, will run longer than a few minutes, must survive the session, or asks which cloud or provider to use. Provider-specific setup lives in the cloud-compute library skills; load one only after compute_job targets shows that provider.

backend/cli/skills/core/compute/SKILL.md

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Network or broad shell
Last checked
Nov 9, 2026

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Repository last updated
Nov 8, 2026
License
Apache-2.0

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# Compute

The question is never "which cloud is best"; it is "which of the targets this user has
configured fits this run, and what will it cost". Discover before you plan, size before
you dispatch, and hand anything longer than a coffee to the job system so the session,
the logs and the metrics outlive the shell.

## Rules

1. **Targets first.** `compute_job {"action":"targets"}` lists what exists: local runtime,
   saved SSH and scheduler hosts, Modal when connected. Plan only against a listed target.
   A provider skill from the library (`modal-serverless-gpu`, `runpod-gpu-cloud`,
   `lambda-gpu-cloud`, `vast-ai-gpu-cloud`, `skypilot-multi-cloud-orchestration`,
   `together-ai-inference`, `fireworks-ai-inference`, `tinker-fine-tuning`; the core index