MacFleet: Distributed ML for Apple Silicon
Pool Apple Silicon Macs into a distributed ML training cluster with auto-discovery, adaptive compression, and thermal-aware scheduling.
PythonPyTorchMLXgRPCmDNSRing AllReduce
Why I Built This
Apple Silicon Macs have serious GPU power, but there's no native way to combine them for ML training. If you have three Macs sitting on a desk, that's three GPUs training could use, but they just sit idle. I wanted to make pip install macfleet and macfleet join the only things standing between a few Macs and a training cluster.
How It Works
- Zero-config discovery via mDNS/Bonjour.
macfleet joinis the only command needed. No IP addresses, no config files. - Framework-agnostic core where the communication layer uses only NumPy, never importing PyTorch or MLX. Both engines work through the same infrastructure.
- Adaptive gradient compression that auto-selects based on network: no compression over Thunderbolt 4, TopK 10% + FP16 (~20x) over Ethernet, TopK 1% + FP16 (~200x) over WiFi.
- Heterogeneous scheduling where faster Macs get proportionally larger batches based on GPU core count. The scheduler re-profiles throughput continuously and adjusts for thermal throttling (nominal → 100%, fair → 90%, serious → 70%, critical → 30%).
- Ring AllReduce for N-node gradient synchronization that scales linearly with cluster size.
- Dual engine support for both PyTorch (MPS backend) and Apple MLX with identical APIs.
Results
| Feature | Detail |
|---|---|
| Install | pip install macfleet |
| Discovery | Automatic via mDNS (zero config) |
| Engines | PyTorch (MPS) + Apple MLX |
| Compression | Up to 200x over WiFi |
| Thermal management | Real-time workload adjustment |
| CLI tools | join, status, train, bench, diagnose |
| API patterns | One-liner, context manager, decorator |