73 lines
2.9 KiB
JavaScript
73 lines
2.9 KiB
JavaScript
|
#!/usr/bin/env node
|
||
|
|
/**
|
||
|
|
* Example: GPU Acceleration in Brainy
|
||
|
|
*
|
||
|
|
* This example demonstrates how to use GPU acceleration for embeddings
|
||
|
|
* while keeping optimized CPU processing for distance calculations.
|
||
|
|
*/
|
||
|
|
|
||
|
|
import { BrainyData, TransformerEmbedding } from '@soulcraft/brainy'
|
||
|
|
|
||
|
|
async function demonstrateGPUAcceleration() {
|
||
|
|
console.log('🚀 Brainy GPU Acceleration Demo\n')
|
||
|
|
|
||
|
|
// 1. Auto-detect best device (default behavior)
|
||
|
|
console.log('1. Creating database with auto GPU detection...')
|
||
|
|
const db = new BrainyData({
|
||
|
|
embedding: {
|
||
|
|
type: 'transformers',
|
||
|
|
options: {
|
||
|
|
device: 'auto', // Automatically detects and uses best available device
|
||
|
|
verbose: true // Show device selection and performance info
|
||
|
|
}
|
||
|
|
}
|
||
|
|
})
|
||
|
|
await db.init()
|
||
|
|
|
||
|
|
// 2. Add some sample data (embeddings will use GPU if available)
|
||
|
|
console.log('\n2. Adding sample data with GPU-accelerated embeddings...')
|
||
|
|
await db.add({ text: 'The quick brown fox jumps over the lazy dog' })
|
||
|
|
await db.add({ text: 'Machine learning is revolutionizing technology' })
|
||
|
|
await db.add({ text: 'Vector databases enable semantic search capabilities' })
|
||
|
|
|
||
|
|
// 3. Search (distance calculations use optimized CPU)
|
||
|
|
console.log('\n3. Searching with optimized CPU distance calculations...')
|
||
|
|
const results = await db.search('artificial intelligence and ML', { k: 2 })
|
||
|
|
|
||
|
|
console.log('Search results:')
|
||
|
|
results.forEach((result, i) => {
|
||
|
|
console.log(` ${i + 1}. "${result.data.text}" (distance: ${result.distance.toFixed(4)})`)
|
||
|
|
})
|
||
|
|
|
||
|
|
// 4. Demonstrate explicit device selection
|
||
|
|
console.log('\n4. Creating explicit CPU-only embedder for comparison...')
|
||
|
|
const cpuEmbedder = new TransformerEmbedding({
|
||
|
|
device: 'cpu',
|
||
|
|
verbose: true
|
||
|
|
})
|
||
|
|
|
||
|
|
const start = Date.now()
|
||
|
|
const embedding = await cpuEmbedder.embed('This will use CPU-only processing')
|
||
|
|
const duration = Date.now() - start
|
||
|
|
|
||
|
|
console.log(` CPU embedding completed in ${duration}ms (${embedding.length} dimensions)`)
|
||
|
|
|
||
|
|
// 5. Show configuration options
|
||
|
|
console.log('\n5. Available device options:')
|
||
|
|
console.log(' • "auto" - Automatically detect best device (recommended)')
|
||
|
|
console.log(' • "cpu" - Force CPU processing')
|
||
|
|
console.log(' • "webgpu" - Use WebGPU in browsers (if supported)')
|
||
|
|
console.log(' • "cuda" - Use CUDA in Node.js (if available)')
|
||
|
|
console.log(' • "gpu" - Generic GPU (resolves to best available)')
|
||
|
|
|
||
|
|
console.log('\n6. Performance characteristics:')
|
||
|
|
console.log(' ✅ GPU Accelerated: Embedding generation (3-5x faster for batches)')
|
||
|
|
console.log(' ✅ CPU Optimized: Distance calculations (faster for small vectors)')
|
||
|
|
console.log(' ✅ Automatic Fallback: CPU fallback if GPU initialization fails')
|
||
|
|
|
||
|
|
// Cleanup
|
||
|
|
await cpuEmbedder.dispose()
|
||
|
|
console.log('\n🎉 Demo completed! Brainy automatically optimizes for your hardware.')
|
||
|
|
}
|
||
|
|
|
||
|
|
demonstrateGPUAcceleration().catch(console.error)
|