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What & why
Training on the main thread can freeze the UI for large models. WebRTC's
RTCPeerConnectionis unavailable in workers, so the page retains WebRTC peers while the worker owns training, aggregation, weight serialization, and round coordination. A peer-transport adapter bridges the two using signaling events and opaque bytes.Technical plan
training.worker.tsruns Disco with WebGL and CPU fallback, using the existing dataset loaders on files and labels passed by the page.peer_transport.tsconnects page-owned peers to the worker through a MessagePort; an injectable peer factory lets DecentralizedClient use this transport without changing its training protocol.worker_messages.tshandles startup, progress, errors, cancellation, and the final model, while TrainerDashboard manages the worker lifecycle and UI. Cooperative stopping retains a forced-termination timeout and peer cleanup. Local validation covers 265 unit tests, static checks, and the webapp build; browser E2E tests run in CI.Deviations from plan
Following review, replaced the PageClient round-level bridge with peer transport and moved dataset loading into the worker.
Todo
Measure large-model main-thread overhead in the remaining WebRTC transport and final-model saving path.