A powerful, easy-to-use AI system optimized for Apple M4 Pro with a beautiful terminal interface.
- 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
- macOS 14.0 or later
- Apple Silicon (M1, M2, M3, M4 series)
- Python 3.10 or later
- 16GB+ RAM recommended
curl -sSL https://raw.githubusercontent.com/IceNet-01/IceNet-AI/main/install.sh | bashOr manual installation:
git clone https://github.com/IceNet-01/IceNet-AI.git
cd IceNet-AI
./install.sh-
Launch IceNet:
icenet
-
Train a model:
icenet train --config configs/example.yaml
-
Interactive mode:
icenet interactive
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 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/valThen run:
icenet train --config my_config.yamlMIT License - See LICENSE file for details
Contributions welcome! Please read CONTRIBUTING.md for guidelines.