MLX Distributed Training v1.0.0 🚀
The first stable release of MLX Distributed Training framework, enabling efficient distributed training of machine learning models across multiple Apple Silicon devices.
🌟 Key Features
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Native Apple Silicon Support: Optimized for M1/M2/M3 chips using MLX
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Distributed Training Architecture:
- Training Coordinator for job orchestration
- Parameter Server for weight synchronization
- Worker Nodes for distributed computation
- Efficient Data Management System
- Robust Model Loading Mechanism
- High-performance Distributed Communication Layer
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Smart Resource Management:
- Automatic hardware detection and optimization
- Dynamic load balancing
- Efficient memory utilization
- Network bandwidth optimization
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Developer-Friendly Tools:
- Comprehensive verification scripts
- Network performance benchmarking
- Hardware compatibility checks
- Simple configuration system
💻 System Requirements
- macOS Sonoma 14.0+
- Python ≥3.12
- MLX ≥0.20.0
- MPI support (via MPICH)
🔧 Key Components
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Core Training Infrastructure:
- Distributed parameter synchronization
- Gradient aggregation
- Model state management
- Checkpoint handling
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Data Pipeline:
- Efficient data loading and preprocessing
- Distributed data sharding
- Dynamic batching
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Monitoring & Management:
- Training progress tracking
- Resource utilization monitoring
- Network performance metrics
- Error handling and recovery
📚 Documentation
Comprehensive documentation is available in the /docs directory, including:
- System Architecture Overview
- Getting Started Guide
- Configuration Reference
- API Documentation
- Best Practices
🔨 Installation
pip install -e ".[dev]"