---
id: runpod
version: "1.12.0"
license: MIT License
license_treatment: permissive
maintenance: active
---

# runpod — 🐍 | Python library for Runpod API and serverless worker SDK.

License: permissive · Maintenance: active · Downloads: 1.4M/mo

## What it is and what it does

Runpod is the official Python SDK for Runpod's GPU cloud platform, providing two main capabilities: a serverless worker framework for deploying custom AI inference endpoints, and an API client library for submitting jobs to those endpoints and managing GPU pods. The serverless component lets you define a handler function that processes incoming jobs, register fitness checks to validate worker health at startup, and optionally warm network-volume caches across cold starts. The API component provides methods to run jobs synchronously or asynchronously, check job status, and manage GPU pod lifecycle.

The package depends on a broad set of runtime libraries including aiohttp for async HTTP, boto3 for cloud storage integration, fastapi for web serving, paramiko for SSH, and various utilities for CLI interaction, configuration parsing, and progress tracking. It targets modern Python (3.10+) and is actively maintained with recent releases, making it suitable for production AI workload deployment on Runpod infrastructure.

Use it for:

- Deploy a custom machine-learning model as a serverless endpoint that auto-scales based on incoming job volume.
- Submit inference requests to a Runpod endpoint from a Python application and wait for results synchronously or poll asynchronously.
- Validate GPU availability and disk space at worker startup to ensure the environment meets requirements before processing jobs.
- Warm a shared model cache from a network volume on container startup to avoid repeated multi-GB downloads across cold starts.
- Manage GPU pod lifecycle (create, list, query) programmatically from Python for infrastructure automation.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Runpod is a Python SDK and API client library for interacting with Runpod's GPU cloud infrastructure and deploying serverless AI workers that process jobs on remote endpoints.

Yes. Runpod is actively maintained, has low install friction, carries no known vulnerabilities, and is the official SDK for a popular GPU cloud platform. Install it if you are deploying AI workloads to Runpod infrastructure or integrating Runpod endpoints into a Python application. The broad dependency set and requirement for Python 3.10+ are minor trade-offs for a production-grade platform SDK.

## Install

pip install runpod
uv add runpod
poetry add runpod

## Installing runpod

Before you install: Low install friction with a pure-wheel distribution. Actively maintained with a recent release (4 days old) and steady repository activity. Supports current Python versions (3.10–3.14).

License in practice: MIT License permits free use, modification, and distribution with minimal restrictions, making it suitable for both open-source and commercial projects.

Quickstart:

pip install runpod

import runpod

# Serverless worker
def handler(job):
    return {"output": job["input"]}

runpod.serverless.start({"handler": handler})

# Or API client
runpod.api_key = "your_api_key"
endpoint = runpod.Endpoint("ENDPOINT_ID")
result = endpoint.run_sync({"input": "data"})

Requires Python 3.10 or higher.

Verify before relying:

- Whether the 19 runtime dependencies (aiohttp, boto3, fastapi, paramiko, etc.) are all required for basic usage or if subsets suffice for specific workflows.
- Performance characteristics and latency overhead of the SDK's API wrapper layer.
- Whether fitness checks and VolumeCache features are production-ready or still experimental.

## Package facts

- License: MIT License (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 1.4M/month (top 5,000 on PyPI)
- Known vulnerabilities: none known

## Tags

runpod serverless worker sdk, gpu cloud api python, ai endpoint deployment, runpod python library, serverless gpu inference, runpod api client, distributed ai workload, gpu-cloud, serverless-inference, ai-deployment

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