runpod-flash 1.13.0

A Python library for distributed inference and serving of machine learning models

Flash

Flash is a Python SDK for developing cloud-native AI apps where you define everything—hardware, remote functions, and dependencies—using local code.

import asyncio
from runpod_flash import Endpoint, GpuType

# Mark the function below for remote execution
@Endpoint(name="hello-gpu", gpu=GpuType.NVIDIA_GEFORCE_RTX_4090, dependencies=["torch"]) 
async def hello(): # This function runs on Runpod
    import torch
    gpu_name = torch.cuda.get_device_name(0)
    print(f"Hello from your GPU! ({gpu_name})")
    return {"gpu": gpu_name}

asyncio.run(hello())
print("Done!") # This runs locally

Write @Endpoint decorated Python functions on your local machine. Run them, and Flash automatically handles GPU/CPU provisioning and worker scaling on Runpod Serverless.

Setup

Install Flash

Install Flash using pip or uv:

# Install with pip
pip install runpod-flash

# Or uv
uv add runpod-flash

Flash requires Python 3.10+, and is currently available for macOS and Linux. Windows support is in development.

Authentication

Before you can use Flash, you need to authenticate with your Runpod account:

flash login

This saves your API key securely and allows you to use the Flash CLI and run @Endpoint functions.

Coding agent integration (optional)

Install the Flash skill package for AI coding agents like Claude Code, Cline, and Cursor:

npx skills add runpod/skills

You can review the SKILL.md file in the runpod/skills repository.

Quickstart

Create gpu_demo.py:

import asyncio
from runpod_flash import Endpoint, GpuType

@Endpoint(
    name="flash-quickstart",
    gpu=GpuType.NVIDIA_GEFORCE_RTX_4090,
    workers=3,
    dependencies=["numpy", "torch"]
)
def gpu_matrix_multiply(size):
    # IMPORTANT: Import packages INSIDE the function
    import numpy as np
    import torch

    # Get GPU name
    device_name = torch.cuda.get_device_name(0)

    # Create random matrices
    A = np.random.rand(size, size)
    B = np.random.rand(size, size)

    # Multiply matrices
    C = np.dot(A, B)

    return {
        "matrix_size": size,
        "result_mean": float(np.mean(C)),
        "gpu": device_name
    }

# Call the function
async def main():
    print("Running matrix multiplication on Runpod GPU...")
    result = await gpu_matrix_multiply(1000)

    print(f"\n✓ Matrix size: {result['matrix_size']}x{result['matrix_size']}")
    print(f"✓ Result mean: {result['result_mean']:.4f}")
    print(f"✓ GPU used: {result['gpu']}")

if __name__ == "__main__":
    asyncio.run(main())

Run it:

python gpu_demo.py

First run takes 30-60 seconds (provisioning). Subsequent runs take 2-3 seconds.

What Flash does

• Remote execution: @Endpoint functions run on Runpod Serverless GPUs/CPUs
• Auto-scaling: Workers scale from 0 to N based on demand
• Dependency management: Packages install automatically on remote workers
• Two patterns: Queue-based (@Endpoint) for batch work, load-balanced (Endpoint() + routes) for REST APIs
• Concurrency control: max_concurrency lets each worker process multiple jobs simultaneously

Documentation

Full documentation: docs.runpod.io/flash

• Quickstart - First GPU workload in 5 minutes
• Create endpoints - Queue-based, load-balancing, and custom Docker endpoints
• CLI reference - flash run, flash deploy, flash build
• Configuration - All endpoint parameters

Flash apps

When you're ready to move beyond scripts and build a production-ready API, you can create a Flash app (a collection of interconnected endpoints with diverse hardware configurations) and deploy it to Runpod.

Follow this tutorial to build your first Flash app.

Flash CLI

The Flash CLI provides a set of commands for managing your Flash apps and endpoints.

flash --help

Learn more about the Flash CLI.

Examples

Browse working examples: github.com/runpod/flash-examples

Requirements

• Python 3.12
• macOS or Linux (Windows support in development)
• A Runpod account (email must be verified) with an API key

Contributing

We welcome contributions! See RELEASE_SYSTEM.md for development workflow.

# Clone and install
git clone https://github.com/runpod/flash.git
cd flash
pip install -e ".[dev]"

# Use conventional commits
git commit -m "feat: add new feature"
git commit -m "fix: resolve issue"

Support

• Discord - Community support
• GitHub Issues - Bug reports

License

MIT License - see LICENSE for details.