ogpu 0.2.0.14

OpenGPU SDK for distributed AI task deployment

🧠 OpenGPU SDK

Welcome to the edge of distributed AI.

The ogpu.service SDK lets you write task handlers that will run on remote provider machines — not your laptop.
You define what your task expects and does, and we handle the wiring, serving, and background magic.

✨ Write your task. 🛰️ Deploy it. ⚡️ Let the network run it.

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🚀 What is this?

This SDK is used by client developers to write Python tasks (as functions) that will be deployed and executed on OpenGPU network providers.

Your code will:

• Accept inputs (Pydantic model)
• Process them inside a registered task handler
• Expose a /run/{task}/{task_address} endpoint via FastAPI
• Be served by remote compute

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🧪 Example: Your First Task

import ogpu.service
from pydantic import BaseModel

class MultiplyInput(BaseModel):
    a: int
    b: int

class MultiplyOutput(BaseModel):
    result: int

@ogpu.service.expose()
def multiply(data: MultiplyInput) -> MultiplyOutput:
    ogpu.service.logger.info(f"🧮 Starting multiplication: {data.a} * {data.b}")
    result = data.a * data.b
    ogpu.service.logger.info(f"✅ Result computed: {result}")
    return MultiplyOutput(result=result)

ogpu.service.start()

That's it. This exposes an endpoint at:

POST /run/multiply/{task_address}

With body:

{
  "a": 5,
  "b": 7
}

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📡 How It Works

• @expose(): Marks your function as a task handler.
• start(): Starts a FastAPI server that awaits tasks.
• Your task runs in a background thread.
• The result is logged, not returned over HTTP.

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📚 Documentation

Complete documentation is available at: https://opengpu-network.github.io/sdk-ogpu-py/

• Quick Start Guide
• API Reference
• Examples & Templates

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🧙 Guidelines

• Your task handler must accept one pydantic.BaseModel input
• It must return another pydantic.BaseModel
• Task (function) names must be unique
• Output will be logged to the console — keep it clean 💅

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🤝 Made for the OpenGPU Network

Unleash your code. Let the grid handle the rest.