Serverless Deployment

Deploy GPUX models using serverless functions and event-driven architectures.

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🎯 Overview

Complete guide for deploying GPUX using serverless platforms including AWS Lambda, Google Cloud Functions, and Azure Functions.

In Development

Detailed deployment guide for serverless is being developed. Basic functionality is available.

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📚 What Will Be Covered

• 🔄 AWS Lambda: Function-as-a-Service with GPU support
• 🔄 Google Cloud Functions: Serverless inference patterns
• 🔄 Azure Functions: Event-driven model serving
• 🔄 Vercel Functions: Edge computing deployment
• 🔄 Model Caching: Persistent model storage strategies
• 🔄 Cold Start Optimization: Fast model loading
• 🔄 Cost Optimization: Pay-per-inference pricing

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🚀 Quick Start

AWS Lambda

import json
from gpux.core.runtime import GPUXRuntime
from gpux.config.parser import GPUXConfigParser

def lambda_handler(event, context):
    # Load model configuration
    config = GPUXConfigParser.parse("gpux.yml")

    # Initialize runtime
    runtime = GPUXRuntime(config)

    # Run inference
    input_data = json.loads(event['body'])
    result = runtime.run(input_data)

    return {
        'statusCode': 200,
        'body': json.dumps(result)
    }

Google Cloud Functions

import json
from gpux.core.runtime import GPUXRuntime
from gpux.config.parser import GPUXConfigParser

def inference_function(request):
    # Load model configuration
    config = GPUXConfigParser.parse("gpux.yml")

    # Initialize runtime
    runtime = GPUXRuntime(config)

    # Run inference
    input_data = request.get_json()
    result = runtime.run(input_data)

    return json.dumps(result)

Vercel Functions

// api/inference.js
export default async function handler(req, res) {
  const { spawn } = require('child_process');

  // Run GPUX inference
  const gpux = spawn('gpux', ['run', 'model-name', '--input', JSON.stringify(req.body)]);

  let result = '';
  gpux.stdout.on('data', (data) => {
    result += data.toString();
  });

  gpux.on('close', (code) => {
    res.status(200).json(JSON.parse(result));
  });
}

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💡 Key Takeaways

Success

✅ Event-Driven: Trigger inference on demand ✅ Auto-scaling: Automatic scaling based on load ✅ Pay-per-Use: Cost-effective for sporadic usage ✅ No Infrastructure: Managed serverless platforms ✅ Cold Start Optimization: Fast model loading

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