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PHP-ORT: Machine learning inference for the web
Software is changing faster than we've seen in 25 years. Machine learning isn't just becoming important, it's becoming essential.
The combination of a core Tensor API, a high performance Math library and optional integration with ONNX, PHP-ORT opens the door to direct inference in PHP, at scale. SIMD AccelerationHybrid Parallelism This is about finding PHP a place in an AI-first software engineering world, the one that's coming ... not only does AI need to be a first class citizen in the minds of developers, but also in the languages they use: Immutable Tensors: Zero-copy, lock-free data sharing, efficient memory usage, and predictable performance Dual Storage Class Tensors: ORT\Tensor\Persistent survive request cycles (and may be shared among threads, ie FrankenPHP), ORT\Tensor\Transient are request local Memory Management: Aligned allocator and optimized memcpy for maximum performance potential Thread Pool: Dedicated Slot scheduling implementation with alignment awareness SIMD Optimization: Runtime detection with thread pinning ensures stability and predictability at scale Type System: Schemas extracted from NumPy runtime; no guesswork, perfect compatibility Type Promotion: Automatic conversion between types for seamless integration and maximum predictability Zero Overhead Optimizations: Backend silently (opaque to the frontend) optimizes dispatch Call Site Scaling: ORT\Math\scale provides a high degree of control over scaling at the call site Modular Design: Math and ONNX systems are independent - use either or both as needed Flexible Tensor Generation: ORT\Tensor\Generator provides flexible generation (lazy loading, random, etc)
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