ml-engine-core 0.1.5

A unified, high-performance Machine Learning Framework with native C++ backends

MLEngine

MLEngine is a unified, high-performance Machine Learning framework tailored for speed and mathematical transparency.

Under the hood, MLEngine acts as a pure Python API routing to highly optimized, custom-built C++ native engines (knn-engine-core and nn-engine-core). By pushing computational bottlenecks—including the entire training loop—down into C++ via pybind11 and Eigen, MLEngine entirely bypasses the Python Global Interpreter Lock (GIL), resulting in execution speeds up to 100x faster than traditional Python-based ML libraries.

Installation

Install the complete suite via PyPI:

pip install ml-engine-core

Modules Overview

Once installed, use the unified mlengine namespace to access both classical and neural sub-modules.

1. mlengine.knn

Powered by knn-engine-core. Features a highly optimized K-Nearest Neighbors classifier with built-in native Principal Component Analysis (PCA) for dimensionality reduction.

import numpy as np
import mlengine as ml

X_train = np.random.rand(100, 64)
y_train = [str(i % 5) for i in range(100)]

# Configure Engine
cfg = ml.knn.KNNConfig()
cfg.k = 3
cfg.variance = 0.95 # Retain 95% variance via native PCA

engine = ml.knn.KNNEngine(cfg)
engine.train(X_train, y_train, scale=True)

predictions = engine.predict_batch(X_train[:5])
print("KNN Predictions:", predictions)

2. mlengine.nn

Powered by nn-engine-core. A multi-layer perceptron framework that executes its full mini-batch gradient descent loop entirely in C++, preventing the Python GIL from interrupting training epochs.

import numpy as np
import mlengine as ml

# Note: MLEngine Neural Nets are hardware-optimized for 32-bit floats
X_train = np.random.rand(100, 4).astype(np.float32)
y_train = np.eye(3)[np.random.choice(3, 100)].astype(np.float32) 
dataloader = ml.nn.DataLoader(X_train, y_train, batch_size=16)

# Build Architecture
class MyModel(ml.nn.Module):
    def __init__(self):
        super().__init__()
        self.fc1 = self.add_module(ml.nn.DenseLayer(4, 16))
        self.relu = self.add_module(ml.nn.ReLULayer())
        self.fc2 = self.add_module(ml.nn.DenseLayer(16, 3))

    def forward(self, tape, x):
        x = self.fc1(tape, x)
        x = self.relu(tape, x)
        return self.fc2(tape, x)

model = MyModel()

# Compile & Train using C++ JIT
optimizer = ml.nn.Adam(learning_rate=0.01)
loss_fn = ml.nn.SoftmaxCrossEntropyLoss()
trainer = ml.nn.JITCompiler(model, optimizer, loss_fn)

# C++ Native Training Loop
trainer.fit(dataloader, epochs=150, verbose=True)

# predict() returns raw logits. Use np.argmax for classification!
logits = model.predict(X_train[:1])
print("Neural Net Logits:", logits)

Architecture & Benchmarks

MLEngine's core philosophy is Native Loop Hoisting. Rather than returning to Python after every matrix operation or epoch step, MLEngine passes pointers to C++ once, executes the entire mathematical pipeline using AVX SIMD vectorization and a zero-allocation flat-memory Autograd graph, and returns only when finished.

In standard academic benchmarks (Iris, MNIST Digits, Olivetti Faces), mlengine.nn consistently achieves up to a 100x speedup over Scikit-Learn while maintaining mathematically identical (or superior) accuracy via Log-Sum-Exp fusion.