| name | runpod-compute |
| description | 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. |
RunPod Compute
Use runpodctl CLI for persistent GPU pods with SSH access.
Setup
brew install runpod/runpodctl/runpodctl
runpodctl config --apiKey=YOUR_KEY
Commands
| Command | Description |
|---|
runpodctl create pod --gpuType "NVIDIA A100 80GB PCIe" --imageName "runpod/pytorch:2.4.0-py3.11-cuda12.4.1-devel-ubuntu22.04" --name experiment | Create a pod |
runpodctl get pod | List 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 list | List available GPU types and prices |
runpodctl send <file> | Transfer files to/from pods |
runpodctl receive <code> | Receive transferred files |
SSH access
ssh root@<IP> -p <PORT> -i <path-to-ephemeral-runpod-key>
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
/goal Inheritance
This skill inherits the SeaBridgeAI /goal default protocol. Frame the work with a persistent goal, Definition of Done, validation plan, risks, dependencies, expected artifacts, and completion evidence. Do not claim completion until the DoD is validated or a hard blocker is documented.
Canonical protocol: C:\Users\adelm\SeaBridgeAI\everything-claude-code\protocols\GOAL_PROTOCOL.md
