maedoc/tvb-wiki/runpod-compute

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Provision and manage GPU pods on RunPod for long-running experiments. Use when the user needs persistent GPU compute with SSH access, large datasets, or multi-step experiments.

Install

npx vskill@latest install maedoc/tvb-wiki/runpod-compute

SKILL.md

The same SKILL.md was published earlier as companion-inc/feynman/runpod-compute.

RunPod Compute

Use runpodctl CLI for persistent GPU pods with SSH access.

Setup

bash
brew install runpod/runpodctl/runpodctl   # macOS
runpodctl config --apiKey=YOUR_KEY

Commands

CommandDescription
runpodctl create pod --gpuType "NVIDIA A100 80GB PCIe" --imageName "runpod/pytorch:2.4.0-py3.11-cuda12.4.1-devel-ubuntu22.04" --name experimentCreate a pod
runpodctl get podList all pods
runpodctl stop pod <id>Stop (preserves volume)
runpodctl start pod <id>Resume a stopped pod
runpodctl remove pod <id>Terminate and delete
runpodctl gpu listList available GPU types and prices
runpodctl send <file>Transfer files to/from pods
runpodctl receive <code>Receive transferred files

SSH access

bash
ssh root@<IP> -p <PORT> -i ~/.ssh/id_ed25519

Get connection details from runpodctl get pod <id>. Pods must expose port 22/tcp.

GPU types

NVIDIA GeForce RTX 4090, NVIDIA RTX A6000, NVIDIA A40, NVIDIA A100 80GB PCIe, NVIDIA H100 80GB HBM3

When to use

  • Long-running experiments needing persistent state
  • Large dataset processing
  • Multi-step work with SSH access between iterations
  • Always stop or remove pods after experiments
  • Check availability: command -v runpodctl

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