BYTES AND BRAINS v0.3.0 . CRATES.IO
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BYTES AND BRAINSv0.3.0
A SUBSTRATE FOR NETWORKED MACHINE LEARNING
/// building in public . CRT/01 ///

A framework for machine learning that runs outside the data center. On phones, sensors, hospital networks, on-prem fleets, and peer-to-peer overlays.

Write your workload as a Module. The framework partitions it across nodes, binds your compute and transport, and ships the bytes.

topology

                [ Module ]
                     │  recorded once
                     ▼
                [   IR   ]   ←  ONNX GraphProto
                     │  compiler partitions per peer
                     ▼
            [ per-node programs ]
                     │
       ┌────────┬────┴────┬─────────┐
       ▼        ▼          ▼         ▼
   [ Node ]←→[ Node ]←→[ Node ]←→[ Node ]   peer mesh,
       │         │         │         │            bytes through
       ▼         ▼         ▼         ▼            one envelope
   [adapter pool: libp2p / HTTP / in-process / ...]

runtime data flow: one Module, partitioned IR, per-node programs bound to adapters.

BUILDING IN PUBLIC. The library is on crates.io. The substrate design lives in the whitepaper. Source is on github. Research is on the research page.

If you work on federated learning, gossip protocols, peer-to-peer systems, or related fields, reach out: [email protected].

latest release

v0.3.0

First public release: framework + sans-IO Engine + Wire + role traits + CpuBackend reference.

// TIMELINE

What is coming. Two tracks: extensions to the framework itself, and protocols built on its primitives.

framework extensions

near-term
  • ONNX Runtime backend
  • ONNX import and interop utilities
  • CPU SIMD opt-in
  • benchmark harness
  • in-memory actor-based simulator
vision
  • wire-version negotiation
  • pure Node and Compiler bindings
  • Python DSL surface
  • JavaScript and TypeScript DSL surface
  • libp2p adapter (bb-libp2p)
  • HTTP adapter (bb-http)
  • observability story
  • snapshot and restore
  • memory-budget tooling
  • no_std build profile
  • deployment examples (Linux VM fleets, ROS 2, Android, Jetson, ESP32)

supported protocols

near-term
  • Protean integration
  • Kademlia overlay
  • Raft
  • gossip learning
vision
  • compression and low-communication aggregation strategies (e.g., product quantization Codec, decoupled local SGD)
  • privacy primitives (e.g., DP-SGD Strategy, secure aggregation Codec)

This list will keep growing as the framework core stabilizes and attention shifts to protocol implementations.

read more