runpod-compute

name	runpod-compute
description	Provision and manage GPU pods on RunPod for explicitly chosen long-running research experiments. Use when a Feynman replication, benchmark, or dataset-heavy research run needs persistent GPU compute with SSH access.

RunPod Compute

Use runpodctl CLI for persistent GPU pods with SSH access during a specific research run. Do not use this skill for provider administration outside that run; tie every pod to a replication, benchmark, or dataset-heavy research objective.

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 research experiments needing persistent state
• Large research datasets required by a replication or benchmark
• Multi-step research work with SSH access between iterations
• Always stop or remove pods after experiments
• Check availability: command -v runpodctl