To use PML, the following system libraries must be installed and properly configured:

• `libopenblas`
• `liblapacke`
• `OpenMP`

PML relies heavily on these libraries for high-performance numerical computation, linear algebra operations, parallel execution, and low-level memory/buffer management required for AI and machine learning workloads.

Please note that the API is currently under active development and may evolve in future releases. Ongoing updates will focus on performance optimization, memory efficiency, stability improvements, and the introduction of additional features and capabilities.

Please read this document to learn how to train an artificial intelligence model using training data and make predictions using test data.

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PML ? PHP Machine Learning

A production-grade CPU-first AI runtime and machine learning infrastructure framework for PHP.

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> PML is to PHP what llama.cpp is to C++ ? a high-performance native runtime that brings serious AI computation into an ecosystem the rest of the industry ignores.

What is PML?

PML is a native-accelerated machine learning and AI inference runtime built for PHP. It combines a hand-optimized C tensor engine with a clean PHP orchestration layer, delivering production-grade ML without Python, without CUDA, and without sacrificing throughput.

The architecture is built on a single philosophy: PHP orchestrates, C computes.

Your PHP Application
        ?
        ?
   Pml\Tensor / Pml\Dataset      ? zero-copy PHP wrappers
        ?
        ?  PHP FFI (single boundary crossing per op)
   libtensor.so                  ? C tensor engine
        ?
        ??? OpenBLAS              ? BLAS / LAPACK kernels
        ??? LAPACKE               ? eigendecomposition, SVD
        ??? OpenMP                ? multi-threaded batch ops
        ??? AVX2                  ? SIMD acceleration

Every tensor lives as a TensorC* in C memory. PHP holds a reference pointer ? never a copy. There are no PHP arrays in any hot path.

Why PML Exists

Modern ML stacks assume Python. This assumption carries hidden costs in PHP-first environments:

| Pain Point | Python Stack | PML | |---|---|---| | Cold-start overhead | 200?800 ms (interpreter + runtime imports) | < 5 ms (PHP + FFI) | | Memory per inference | 150?400 MB baseline | 8?20 MB baseline | | Deployment surface | Python runtime + venv + pip | PHP + one .so file | | PHP integration | IPC, REST, or subprocess | Native function call | | CPU parallelism | GIL-constrained | OpenMP, zero-GIL |

If you run PHP backends, PML lets you embed ML directly ? same process, same memory space, same request lifecycle.

Technical Highlights

Zero-Copy Tensor Architecture

// CSV loaded via mmap into C memory ? no PHP arrays
$ds = Dataset::fromCSV('/data/train.csv');

// Tensor wraps TensorC* ? no PHP-side copy
$X  = $ds->samples();  // Pml\Tensor ? TensorC* view

// All math crosses FFI exactly once per operation
$out = $X->matmul($W)->add($b)->relu();

Tensor is a thin PHP object holding a \FFI\CData pointer. Slices, views, and column extractions reuse the same memory buffer with reference counts tracked entirely inside C.

Native C Tensor Engine

libtensor.so provides:

• 500+ exported C functions across tensor ops, dataset I/O, inference, autograd, graph execution, and tokenization
• Fused kernels: `addRelu`, `fusedAdamStep`, `fusedBceLoss`, `qw_dot_group` (INT8 + fp32 scale)
• AVX2 SIMD sigmoid, tanh, exp, INT8 dot product
• OpenBLAS SGEMM for all matmul on contiguous float32 tensors
• OpenMP threaded batch operations, tree predictions, image pipelines
• mmap CSV loader: ingests multi-GB datasets without touching PHP memory

LLM Inference Engine

$tok     = Tokenizer::fromJson('/models/llama3-8b/tokenizer.json');
$session = InferenceSession::load('/models/llama3-8b', tok: $tok);

// GQA forward pass, KV-cache, streaming tokens
foreach ($session->generate("Explain AVX2:", maxNewTokens: 200) as $token) {
    echo $token;
}

• LLaMA / Mistral / Phi architecture support
• GQA (Grouped Query Attention) natively in C
• Multi-layer KV-cache (`MultiKVCache`) ? eliminates O(T²) decode cost
• Milakov online-softmax: O(head_dim) working memory
• SafeTensors mmap weight loading ? zero-copy model ingestion
• INT8 block quantization (Q8_0-class): 4× memory reduction, AVX2 fused kernel

Classical ML at Native Speed

$pipeline = new Pipeline(
    transformers: [new StandardScaler(), new PolynomialExpander(degree: 2)],
    estimator:    new GBDTClassifier(trees: 500, maxDepth: 6)
);

$pipeline->train($dataset);
echo $pipeline->score($test);  // accuracy, AUC, F1

GBDT with histogram subtraction + PQ leaf-wise growth. All split-finding runs in C.

Feature Matrix

| Module | Description | |---|---| | Tensor | 200+ ops: creation, arithmetic, linear algebra, shape, reductions, fused kernels | | Dataset | Zero-copy mmap CSV, ETL/DataFrame mode, stratified splits, DataLoader, streaming | | Estimators | 19 classifiers, 15 regressors, 6 anomaly detectors, 5 clusterers, decomposition | | Transformers | Scalers, encoders, NLP vectorizers, image transforms, feature selection, imputers | | Neural Networks | 29 layer types, 9 optimizers, 5 losses, early stopping, callbacks, mixed precision | | Quantization | INT8 block quantization, QuantizedTensor, Dense::quantize(), Sequential::quantize() | | Inference | LLM forward pass, GQA, KV-cache, BPE tokenizer, SafeTensors I/O, streaming | | Vision | 106 C functions: image I/O, augmentation, MobileNetV3, YOLO11n, NanoDet, FastSAM | | Pipeline | Transformer composition, 6 CV strategies, GridSearch, ensemble, BootstrapAggregator | | Autograd | Reverse-mode AD, compute graph, Variable API |

Installation

Requirements

| Dependency | Version | Purpose | |---|---|---| | PHP | ? 8.1 | Runtime | | ext-ffi | any | C bridge | | GCC | ? 11 | Compile backend | | libopenblas-dev | any | BLAS kernels | | liblapacke-dev | any | Linear algebra | | Linux x86_64 | ? | AVX2 / OpenMP |

# Ubuntu / Debian
sudo apt install gcc libopenblas-dev liblapacke-dev

# Install PHP library
composer require ghostjat/pml

# Build the C backend (once per machine)
cd vendor/ghostjat/pml/src/Lib
gcc -O3 -march=native -mfma -fopenmp -funroll-loops -fomit-frame-pointer \
    -D_GNU_SOURCE -shared -fPIC -funsafe-math-optimizations \
    -o libtensor.so.7 tensor.c dataset_io.c inference.c autograd.c graph.c tokenizer.c \
    -lopenblas -llapacke -lm
ln -sf libtensor.so.7 libtensor.so

php.ini settings:

ffi.enable        = true
memory_limit      = 2G
opcache.jit       = tracing
opcache.jit_buffer_size = 128M

Quick Start

Classical Classification

<?php
require 'vendor/autoload.php';

use Pml\Dataset;
use Pml\Pipeline;
use Pml\Transformers\StandardScaler;
use Pml\Estimators\Classifiers\RandomForestClassifier;

$dataset = Dataset::fromCSV('iris.csv', hasHeader: true)
    ->withLabelColumn('species')
    ->dropNans();

[$train, $test] = $dataset->stratifiedSplit(testRatio: 0.2);

$pipeline = new Pipeline(
    transformers: [new StandardScaler()],
    estimator:    new RandomForestClassifier(trees: 200)
);

$pipeline->train($train);
echo "Accuracy: " . $pipeline->score($test) . PHP_EOL;
$pipeline->save('/models/iris');

Deep Learning (MLP with early stopping)

<?php
use Pml\NeuralNetwork\Sequential;
use Pml\NeuralNetwork\Layers\{Dense, BatchNormalization, Dropout, ReLU, Softmax};
use Pml\NeuralNetwork\Optimizers\Adam;
use Pml\NeuralNetwork\Losses\CrossEntropyLoss;
use Pml\Training\{Trainer, TrainingArguments};

$model = new Sequential([
    new Dense(784, 512), new BatchNormalization(), new ReLU(), new Dropout(0.3),
    new Dense(512, 256), new BatchNormalization(), new ReLU(), new Dropout(0.2),
    new Dense(256, 10),  new Softmax(),
], new Adam(lr: 1e-3), new CrossEntropyLoss());

$trainer = new Trainer($model, new TrainingArguments(
    epochs: 30, batchSize: 128, patience: 5,
));

$result = $trainer->train($trainDataset, $valDataset);
echo "Best accuracy: {$result->bestMetric}" . PHP_EOL;

INT8 Quantized Deployment

<?php
// Quantize after training ? 4× memory reduction, same API
$model->quantize(groupSize: 32);
$predictions = $model->predict($testDataset);

LLM Inference (LLaMA / Mistral)

<?php
use Pml\Inference\{InferenceSession, Tokenizer};

$tok     = Tokenizer::fromJson('/models/mistral-7b/tokenizer.json');
$session = InferenceSession::load('/models/mistral-7b', tok: $tok);

foreach ($session->generate("Write a PHP FFI binding:", maxNewTokens: 300) as $token) {
    echo $token;
    flush();
}

Computer Vision

<?php
use Pml\Vision\{Image, Yolo11n, MobileNetV3};

$detector   = new Yolo11n('/models/yolo11n.weights', confidenceThresh: 0.5);
$classifier = new MobileNetV3('/models/mobilenetv3.weights');

$img  = Image::fromFile('scene.jpg');
$dets = $detector->detect($img);

foreach ($dets as $box) {
    $label = $classifier->classify($img->crop(...$box->rect));
    echo "{$label} @ {$box->confidence}" . PHP_EOL;
}

Benchmarks

> Benchmarks run on AMD Ryzen 9 5950X, 64 GB DDR4-3600, Ubuntu 22.04, GCC 13, PHP 8.3. > Full methodology in BENCHMARKS.md.

Tensor Throughput ? GEMM 1024×1024

| Runtime | Time | GFLOPS | |---|---|---| | PML (OpenBLAS + AVX2) | 18 ms | 116 | | RubixML (PHP arrays) | 4,200 ms | 0.5 | | NumPy (MKL) | 14 ms | 150 | | PyTorch CPU | 22 ms | 95 |

Cold-Start to First Inference

| Runtime | Cold Start | |---|---| | PML | 4 ms | | Python + scikit-learn | 210 ms | | Python + PyTorch | 680 ms |

Memory: 10-class MLP Training (50K samples)

| Runtime | RSS Peak | |---|---| | PML | 38 MB | | PyTorch | 290 MB | | TensorFlow | 410 MB |

Architecture

See ARCHITECTURE.md for the full design document.

???????????????????????????????????????????????????????????
?                   Your PHP Application                   ?
???????????????????????????????????????????????????????????
                              ?  PSR-4 autoload
???????????????????????????????????????????????????????????
?                     PML PHP Layer                        ?
?  Tensor · Dataset · Pipeline · Sequential ·              ?
?  InferenceSession · Vision · Estimators · Transformers   ?
???????????????????????????????????????????????????????????
                              ?  FFI::cdef() ? one crossing per op
???????????????????????????????????????????????????????????
?              libtensor.so  (C tensor engine)             ?
?  tensor.c · dataset_io.c · inference.c · autograd.c      ?
?  graph.c · tokenizer.c                                   ?
?  ????????????????  ???????????????  ????????????????   ?
?  ?  OpenBLAS    ?  ?  LAPACKE    ?  ?   OpenMP     ?   ?
?  ????????????????  ???????????????  ????????????????   ?
???????????????????????????????????????????????????????????

Roadmap Preview

| Version | Focus | Status | |---|---|---| | v1.0?1.3 | Tensor engine, classical ML, deep learning, LLM inference, INT8, vision | ? Done | | v2.0 | Vulkan GPU backend (cross-vendor: NVIDIA / AMD / Intel / Apple) | ? Design | | v2.1 | ONNX model import, fp16 tensors, Flash Attention | ? Planned | | v3.0 | Distributed training, sharded datasets, agent runtime | ? Planned |

Full roadmap: ROADMAP.md

Comparison

| | PML | scikit-learn | PyTorch CPU | RubixML | |---|---|---|---|---| | Language | PHP + C | Python + C | Python + C++ | PHP | | Tensor engine | Native C (libtensor.so) | NumPy | LibTorch | PHP arrays | | Zero-copy I/O | ? mmap | ? | ? | ? | | PHP-native API | ? | ? | ? | ? | | LLM inference | ? GQA, KV-cache | ? | ? | ? | | INT8 quantization | ? AVX2 fused | ? | ? | ? | | Vision (detection) | ? YOLO11n, NanoDet | ? | ? | ? | | Cold-start | 4 ms | 210 ms | 680 ms | 60 ms | | Deployment | .so file | Python env | Python env | Composer |

Contributing

Read CONTRIBUTING.md for the full guide. Key rules:

• PHP orchestrates ? heavy loops must stay in C
• Preserve zero-copy semantics everywhere possible
• New C functions must be declared in `tensor.h` and bound in `TensorEngine.php`
• All PRs require PHPUnit + PHPBench results
• Performance regressions block merge

composer install
vendor/bin/phpunit --colors=always
vendor/bin/phpbench run --report=aggregate

Sponsors

PML is an independent open-source project. Sponsorship funds C kernel development, GPU backend work, documentation, and infrastructure.

? Become a Sponsor

See SPONSORS.md for tier details and benefits.

License

MIT ? Copyright (c) 2024 Shubham Chaudhary

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PHP orchestrates. C computes. Zero compromises.

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