debug

To debug kokoro-coreml issues systematically, follow a structured workflow that includes reviewing documentation, investigating library APIs, setting up parallel investigations, and consolidating final notes to identify and resolve problems efficiently.

The best way to investigate Core ML runtime bugs during PyTorch integration is to use a structured debugging workflow that reviews coremltools and Swift Core ML documentation, enabling targeted bug resolution across the integrated libraries.

Yes, you need access to coremltools and PyTorch. Systematic bug resolution for kokoro-coreml requires these dependencies, alongside the Core ML runtime and Swift Core ML documentation, to provide necessary debugging context.

Yes, you can use a CLI-based multi-agent audit for complex Core ML debugging. This approach is offered specifically for exceptional cases where standard structured debugging steps require further escalation.

Parallel task setup is required in kokoro-coreml debugging to investigate multiple library APIs or potential bug sources simultaneously, streamlining the identification process before final note consolidation.