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remote-gpu-trainer

authentic

Deploy, monitor, and debug long GPU jobs on RENTED/remote instances (AutoDL, RunPod, vast.ai, Lambda, Slurm, K8s): teardown/billing safety, spot resilience, resumable checkpointing, OOM/NaN triage.

When to use

• Use whenever the user deploys, trains, monitors, or troubleshoots a long-running GPU job on a **RENTED or remote instance they do not own** — training, eval, ablation sweeps, batch inference, or large data processing — on AutoDL, RunPod, v…

When not to use

• | Situation | Use instead | |---|---| | Local single-GPU, or multi-GPU **DDP inside one box** | `torchrun` / `accelerate` directly | | Managed multi-cloud price-shopping + auto spot-recovery across **Western** clouds | **SkyPilot** (has it…
• Does not replace a real cloud orchestrator or managed provisioner; use it to make rented-box work survivable, not to optimize multi-cloud procurement.
• Platform billing, stop, destroy, and data-retention behavior can drift; re-check current provider docs before destructive or money-impacting actions.
• Requires user-owned credentials, SSH/API access, and explicit confirmation before teardown, deletion, or other irreversible cleanup.
• Companion skills named above are not bundled here; treat them as optional references unless installed in the current agent environment.

Skill page

Source