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runpodctl

Manage GPU workloads on Runpod via the runpodctl CLI.

1|Updated Feb 1, 2026
Search Tags:#templates#serverless#models#CLI#GPU#workload management#Runpod#network volumes
One-click install
npx skills add https://github.com/safzanpirani/pi-config --skill runpodctl-safzanpirani
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: runpodctl
Source: https://github.com/safzanpirani/pi-config/tree/main/skills/runpodctl
Command: npx skills add https://github.com/safzanpirani/pi-config --skill runpodctl-safzanpirani

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires runpodctl, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill solves the problem of managing GPU workloads by providing a comprehensive CLI for Runpod, allowing users to manage pods, serverless endpoints, templates, volumes, and models with ease.

Core Features & Use Cases

  • Pod Management: List, create, start, stop, and delete GPU pods.
  • Serverless Endpoints: Create, update, and delete serverless endpoints for serverless computing.
  • Templates: List, search, get, create, update, and delete templates for GPU workloads.
  • Network Volumes: List, get, create, update, and delete network volumes for data storage.
  • Models: List, add, and remove models for training and inference.
  • Registry: List, get, create, and delete registry authentication for container images.
  • Info: Get account info, list GPUs, datacenters, and billing history.
  • SSH: Get SSH info and list SSH keys.
  • File Transfer: Send and receive files with pods.
  • Utilities: Diagnose CLI issues, update CLI, and get the version.
  • Use Case: Imagine you need to run a deep learning model on a GPU. Use this Skill to create a pod from a template, start the pod, and execute your training script.

Quick Start

To list all available GPUs, use the command: runpodctl gpu list.

Frequently Asked Questions about runpodctl

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I manage GPU pods and serverless endpoints on Runpod?▼

You can manage Runpod GPU pods and serverless endpoints by executing CLI commands to create, start, stop, or delete compute resources. This workload management supports provisioning deep learning environments and configuring serverless computing infrastructure.

What is the best way to create a GPU pod from a template for deep learning?▼

The best way to create a GPU pod from a template is using CLI commands to search available templates, provision the pod, and start the compute instance. This workflow streamlines environment setup for training scripts and model inference.

Do I need the runpodctl CLI installed to manage network volumes and models?▼

Yes, you need the runpodctl CLI installed on your system and Runpod platform access to manage network volumes and models. The CLI client provides the necessary commands to list, add, remove, and update storage and model configurations.

Can I transfer files and get SSH access to my Runpod pods?▼

Yes, you can send and receive files with pods and retrieve SSH information using specific CLI commands. The client allows you to list SSH keys and get connection details to access your running GPU instances directly.

How does Runpod CLI handle template and registry management for container images?▼

Runpod CLI handles template and registry management by executing commands to create, update, and delete templates, alongside managing registry authentication for container images. This centralizes workload configuration and container deployment.

Why is my Runpod workload deployment not working and how can I troubleshoot it?▼

If your Runpod workload deployment is not working, you can troubleshoot it using built-in CLI utility commands to diagnose issues. The diagnostic tools help identify configuration problems, and you can update the CLI client to resolve potential version-related conflicts.

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