dria 0.0.22

Dria SDK - A Python library for interacting with the Dria AI Network

Dria-SDK

Dria SDK is a powerful SDK for building and executing AI-powered workflows and pipelines. It provides a flexible and extensible framework for creating complex AI tasks, managing distributed computing resources, and handling various AI models.

Table of Contents

1. Installation
2. Features
3. Login
4. Getting Started
5. Usage Examples
6. API Usage
7. License

Installation

To install Dria SDK, you can use pip:

pip install dria

Features

• Create and manage AI workflows and pipelines
• Support for multiple AI models
• Distributed task execution
• Flexible configuration options
• Built-in error handling and retries
• Extensible callback system

Login

Dria SDK uses authentication token for sending tasks to the Dria Network. You should get your rpc token from Dria Login API.

Getting Started

To get started with Dria SDK, you'll need to set up your environment and initialize the Dria client:

import os
from dria.client import Dria

# Initialize the Dria client
dria = Dria(rpc_token=os.environ["DRIA_RPC_TOKEN"])

# Initialize the client (should be called before using any other methods)
await dria.initialize()

Usage Examples

Creating a Simple Workflow

Here's an example of creating a simple workflow for generating a poem:

import asyncio

from dria.client import Dria
from dria.models import Task, TaskResult
from dria.models.enums import Model
from dria.workflows.lib.poem_generator import poem

dria = Dria()

async def generate_poem(prompt: str) -> list[TaskResult]:
    task = Task(
        workflow=poem(prompt),
        models=[Model.QWEN2_5_7B_FP16]
    )
    await dria.push(task)
    return await dria.fetch(task_id=task.id)

async def main():
    
    await dria.initialize()
    result = await generate_poem("Write a poem about love")

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

Building a Complex Pipeline

For more complex scenarios, you can use the PipelineBuilder to create multi-step pipelines:

from dria.client import Dria
from dria.models import Model, TaskInput
from dria.pipeline import PipelineConfig, StepConfig, PipelineBuilder, StepBuilder
from workflows import generate_entries, generate_subtopics


async def create_subtopic_pipeline(dria: Dria, topic, config: PipelineConfig = PipelineConfig(), max_depth=1):
    pipeline = PipelineBuilder(config, dria)
    depth = 0

    # handles single topic output
    subtopics = StepBuilder(input=TaskInput(topics=[topic]), config=StepConfig(models=[Model.QWEN2_5_7B_FP16,
                                                                                       Model.GPT4O]),
                            workflow=generate_subtopics).broadcast().build()
    pipeline.add_step(subtopics)

    while depth < max_depth:
        # handles multiple topics
        subtopics = StepBuilder(workflow=generate_subtopics,
                                config=StepConfig(models=[Model.QWEN2_5_7B_FP16, Model.GPT4O])).scatter().build()
        pipeline.add_step(subtopics)
        depth += 1

    # entry generation
    entries = StepBuilder(workflow=generate_entries, config=StepConfig(min_compute=0.8)).build()
    pipeline.add_step(entries)
    return pipeline.build()

API Usage

You can use the Dria SDK on the API level to create your own workflows and pipelines.

from fastapi import FastAPI, HTTPException, BackgroundTasks
from pydantic import BaseModel, Field
from dria.client import Dria
from dria.pipeline.pipeline import PipelineConfig, Pipeline
from pipeline import create_subtopic_pipeline

app = FastAPI(title="Dria SDK Example")
dria = Dria()


@app.on_event("startup")
async def startup_event():
    await dria.initialize()


class PipelineRequest(BaseModel):
    input_text: str = Field(..., description="The input text for the pipelines to process")


class PipelineResponse(BaseModel):
    pipeline_id: str = Field(..., description="Unique identifier for the created pipelines")


pipeline_config = PipelineConfig(retry_interval=5)
pipelines = {}


@app.post("/run_pipeline", response_model=PipelineResponse)
async def run_pipeline(request: PipelineRequest, background_tasks: BackgroundTasks):
    pipeline = await create_subtopic_pipeline(dria, request.input_text, pipeline_config)
    pipelines[pipeline.pipeline_id] = pipeline
    background_tasks.add_task(pipeline.execute)
    return PipelineResponse(pipeline_id=pipeline.pipeline_id)


@app.get("/pipeline_status/{pipeline_id}")
async def get_pipeline_status(pipeline_id: str):
    if pipeline_id not in pipelines:
        raise HTTPException(status_code=404, detail="Pipeline not found")

    pipeline = pipelines[pipeline_id]
    state, status, result = pipeline.poll()

    if result is not None:
        del pipelines[pipeline_id]

    return {"status": status, "state": state, "result": result}

# Usage example:
# uvicorn main:app --host 0.0.0.0 --port 8005

For more detailed API documentation, see on our documentation site.

License

Dria SDK is released under the MIT License.