sorix 1.0.1

A minimalistic and high-performance Machine Learning library based on NumPy

🌌 Sorix

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Sorix is a high-performance, minimalist deep learning library built on top of NumPy/CuPy. Designed for research and production environments where efficiency and a clean API matter. If you know PyTorch, you already know how to use Sorix.

📖 Read the Full Documentation

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🚀 Key Features

• ⚡ High Performance: Run optimized neural networks on NumPy (CPU) or CuPy (GPU).
• 🧩 PyTorch-like API: Familiar and expressive syntax for a near-zero learning curve.
• 🍃 Lightweight: Minimal dependencies, ideal for resource-constrained environments.
• 🛠️ Production Ready: Straight path from prototype to real-world deployment.
• 📈 Autograd Engine: Simple yet powerful automatic differentiation.

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📦 Installation

Choose your preferred package manager:

Using pip:

pip install sorix

Using uv:

uv add sorix

Using Poetry:

poetry add sorix

Note for GPU support: Install the CuPy extra using pip install "sorix[cp13]" (Requires CuPy v13 and CUDA).

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⚡ Sorix in 30 Seconds

Building and training a model is intuitive. Here is a complete training loop:

import numpy as np
from sorix import tensor
from sorix.nn import Linear, MSELoss
from sorix.optim import SGD

# 1. Prepare data (y = 3x + 2)
X = np.linspace(-1, 1, 100).reshape(-1, 1)
y = 3 * X + 2 + 0.1 * np.random.randn(*X.shape)
X_t, y_t = tensor(X), tensor(y)

# 2. Define model, loss, and optimizer
model = Linear(1, 1) # Simple y = Wx + b
criterion = MSELoss()
optimizer = SGD(model.parameters(), lr=0.1)

# 3. Training loop
for epoch in range(100):
    y_pred = model(X_t)
    loss = criterion(y_pred, y_t)

    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

    if (epoch + 1) % 20 == 0:
        print(f"Epoch {epoch+1}, Loss: {loss.item():.4f}")

# Learned: y = 3.00x + 2.00
print(f"Learned: y = {model.W.item():.2f}x + {model.b.item():.2f}")

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📖 Learn & Examples

Learn Sorix through interactive notebooks. Open them directly in Google Colab:

Topic	Documentation	Colab
Tensor Basics	Tensors Guide
Autograd Engine	Autograd Guide
Linear Regression	Regression Guide
MNIST Classification	MNIST Guide

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🛠️ Roadmap

• Core Autograd Engine (NumPy/CuPy backends)
• Basic Layers: Linear, ReLU, Sigmoid, Tanh, BatchNorm1D
• Optimizers: SGD, Adam, RMSprop
• GPU Acceleration via CuPy
• Sequential API (Coming soon)
• Convolutional Layers (Conv2d, MaxPool2d)
• Dropout & Regularization
• Advanced Initializations (Kaiming, Orthogonal)

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🤝 Contribution

We appreciate any contribution from the community!

1. Report Bugs: Open an Issue.
2. Add Features: Submit a Pull Request.
3. Improve Docs: Help us make the documentation better.
4. Write Tests: Improve our code coverage.

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📌 Links

• Documentation: mitchell-mirano.github.io/sorix
• PyPI Package: sorix
• Samples: examples/ folder

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