Runpodctl
0Runpodctl is a command-line tool designed for managing GPU pods and serverless endpoints on the Runpod platform. It is particularly useful for developers and data scientists who need to deploy and manage AI workloads efficiently.
000Updated 2mo ago
Management Tools
Quick InstallView Source
$ npx skills add https://github.com/runpod/skills --skill runpodctlOverview
About This Skill
Runpodctl is a command-line interface tool that enables developers and data scientists to manage GPU pods, serverless endpoints, templates, volumes, and models on the Runpod platform. The tool provides comprehensive commands for creating, listing, updating, and deleting GPU workloads across Linux, macOS, and Windows. You can deploy containerized applications using custom Docker images or pre-configured templates, search the Runpod Hub marketplace for deployable repositories, and configure scaling policies and resource allocation for serverless endpoints. Runpodctl streamlines GPU resource management, making it easier to provision and monitor AI workloads without navigating the web console.
How to Use
Install using the official installer or Homebrew for macOS.
Use Cases
Deploying models on serverless GPU endpoints
Managing GPU pods and templates
Handling serverless workloads effectively
Integrating with other Runpod CLI tools
Tags
Setup & Installation
Quick Install
Terminal
$ npx skills add https://github.com/runpod/skills --skill runpodctlAlternative Install (Git Clone)
git clone https://github.com/runpod/skillsRequirements
- Claude Code or compatible AI agent
Quick Start Guide
1
Install the Skill
Copy the install command above and run it in your terminal.
2
Open Your AI Agent
Launch Claude Code, Cursor, or your preferred AI coding agent.
3
Try It Out
Use the prompt template or examples below to test the skill.
4
Customize
Adapt the skill to your specific use case and workflow.
Usage Examples
Prompt Template
Use Runpodctl to [ACTION] on [COMPANY]'s Runpod account. Focus on [SPECIFIC_TASK] such as deploying a GPU pod, managing serverless endpoints, or monitoring resource usage. Provide [DETAILED_INSTRUCTIONS] for [COMPANY]'s [INDUSTRY] workflows.
Example Output
### Runpodctl Command Execution for [COMPANY] Here’s how to deploy a new GPU pod for your AI training workload: ```bash # Authenticate with Runpod runpodctl auth login --api-key YOUR_API_KEY # List available GPU templates runpodctl templates list # Deploy a new pod with the 'NVIDIA A100' template runpodctl pods create \ --template "NVIDIA A100" \ --name "ai-training-pod" \ --gpu-count 1 \ --volume-size 100 \ --image "nvcr.io/nvidia/pytorch:23.10-py3" # Monitor pod status runpodctl pods list --status "Running" ``` **Next Steps:** - SSH into the pod using `runpodctl pods ssh ai-training-pod`. - Install dependencies with `pip install -r requirements.txt`. - Start your training job with `python train.py`. Need help? Run `runpodctl --help` for all commands.
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