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nebullvm-backends

nebullvm-backends

Guides NebullVM data/device handling, backend selection, compiler lists, and optional dependency troubleshooting.

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Documentation de la Skill

GitHub

name: nebullvm-backends description: "Guides NebullVM data/device handling, backend selection, compiler lists, and optional dependency troubleshooting." disable-model-invocation: true metadata: disco-role: operating license: NO_LICENSE

NebullVM backends

Use this sub-skill when the user is asking about the lower-level NebullVM support layer behind Speedster: data shapes, device parsing, backend lists, optional dependencies, or compiler selection.

Triggers

  • DataManager input formats and dataloader conversion.
  • check_device, gpu_is_available, TPU fallback, or AWS Neuron fallback behavior.
  • Auto-installer framework/backend/compiler selectors.
  • Compiler backend availability questions.
  • Optional dependency or platform-specific install failures.

Read next

  • references/api-reference.md for the verified enums, dataclasses, and selector functions.
  • references/data-and-devices.md for the accepted data and device formats.
  • references/compiler-selection.md for the framework/backend/compiler decision logic.
  • references/troubleshooting.md for compiler, platform, and optional dependency failures.
  • scripts/nebullvm_backend_probe.py for a safe selector/device/import smoke probe.
  • ../speedster-optimization/SKILL.md when the question is about the higher-level optimization API rather than the support layer.

What to include

  • Public enums and dataclasses from nebullvm.core.models.
  • DataManager and PytorchDataset behavior.
  • Backend and compiler selection rules from auto_installer.
  • Device parsing and accelerator fallback behavior.

What to exclude

  • Direct training workflows or long optimization runs.
  • Unsafe shell installer execution unless the user explicitly wants backend installation.

Quick decision rule

If the user is trying to understand which framework/backend/compiler combination is valid, start here. If they are already calling optimize_model, read the Speedster sub-skill first.

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