Feynman runpod-compute

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

source · Clone the upstream repo

git clone https://github.com/getcompanion-ai/feynman

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/getcompanion-ai/feynman "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/runpod-compute" ~/.claude/skills/getcompanion-ai-feynman-runpod-compute && rm -rf "$T"

manifest: skills/runpod-compute/SKILL.md

source content

RunPod Compute

Use

runpodctl

CLI for persistent GPU pods with SSH access.

Setup

brew install runpod/runpodctl/runpodctl   # macOS
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 ~/.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
```