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Research Radar · Experimental implementation

Train Pilot

Hardware-aware orchestration and optimization of neural-network training workloads.

Open repository ↗ Documentation ↗

What it investigates

Train Pilot explores a control loop for assigning a neural-network training workload to available local hardware.

Implementation record

The documented HardwareOrchestrator profiles model and dataset characteristics, observes hardware state, predicts a backend choice, and executes through a training environment. The example distinguishes Apple Neural Engine and CPU paths and exposes the observe, think, and act stages directly.

Public record

This build was recorded as a synthesis, with no single public source paper attached to it. The repository and its documentation are the public record for inspecting its approach. This page does not infer a source result or claim a reproduction.

Scope and limitations

This synthesis build has no single recorded public source paper. Its documented rules and estimates are a reference workflow, not evidence of measured speedups, optimal scheduling, or support for every accelerator.