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IceNet AI

A powerful, easy-to-use AI system optimized for Apple M4 Pro with a beautiful terminal interface.

Features

  • M4 Pro Optimized: Leverages Metal Performance Shaders (MPS) for GPU acceleration
  • Easy Training: Simple configuration-based training system
  • Beautiful Terminal UI: Nomadnet-inspired interface for intuitive interaction
  • Modular Architecture: Support for multiple model types (Transformers, CNNs, RNNs)
  • Auto-Updates: Built-in update checker and installer
  • Lightweight: Efficient memory usage optimized for Apple Silicon

System Requirements

  • macOS 14.0 or later
  • Apple Silicon (M1, M2, M3, M4 series)
  • Python 3.10 or later
  • 16GB+ RAM recommended

Quick Installation

curl -sSL https://raw.githubusercontent.com/IceNet-01/IceNet-AI/main/install.sh | bash

Or manual installation:

git clone https://github.com/IceNet-01/IceNet-AI.git
cd IceNet-AI
./install.sh

Quick Start

  1. Launch IceNet:

    icenet
  2. Train a model:

    icenet train --config configs/example.yaml
  3. Interactive mode:

    icenet interactive

Architecture

IceNet supports multiple neural network architectures:

  • Transformers: State-of-the-art attention-based models
  • CNNs: Convolutional networks for vision tasks
  • RNNs/LSTMs: Recurrent networks for sequence modeling
  • Hybrid Models: Custom architectures combining multiple approaches

All models are optimized for Apple Silicon using:

  • Metal Performance Shaders (MPS)
  • Unified Memory Architecture
  • Neural Engine integration
  • Mixed precision training (FP16/BF16)

Training

Training is as simple as creating a YAML config:

model:
  type: transformer
  hidden_size: 512
  num_layers: 6
  num_heads: 8

training:
  batch_size: 32
  learning_rate: 0.001
  epochs: 10
  optimizer: adamw

data:
  train_path: data/train
  val_path: data/val

Then run:

icenet train --config my_config.yaml

Documentation

License

MIT License - See LICENSE file for details

Contributing

Contributions welcome! Please read CONTRIBUTING.md for guidelines.

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