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效率与协作 · SKILL.md

framework-compatibility

framework-compatibility

【效率与协作】Route TensorFlow 2, legacy TensorFlow 1 with Tensorpack, Keras/Keras-MXNet, and Chainer requests with explicit prerequisites, verification limits, and safe CPU fallbacks.

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技能说明

GitHub

name: framework-compatibility description: "Route TensorFlow 2, legacy TensorFlow 1 with Tensorpack, Keras/Keras-MXNet, and Chainer requests with explicit prerequisites, verification limits, and safe CPU fallbacks." disable-model-invocation: true metadata: disco-role: operating license: MIT

Framework compatibility

Use this sub-skill when a request names TensorFlow 2, TensorFlow 1/Tensorpack, Keras or Keras-MXNet, Chainer, a framework-specific model provider, or a framework-specific training/evaluation/demo command.

Route first

  1. Identify the requested backend and read the backend matrix.
  2. Check optional dependencies without importing a large framework or touching the network. The matrix and troubleshooting define the package/distribution and import names.
  3. Label the result bounded-unverified unless the caller supplies a separately verified backend environment. These four compatibility surfaces were not live-verified in this production run; do not report them as working merely because a provider or CLI exists.
  4. For a no-network CPU smoke test or when an optional backend is absent, route to the verified CPU Gluon/PyTorch paths in model-inference. Route dataset-backed runs to training-evaluation.
  5. Route any parameter conversion, TF2-to-TFLite export, or cross-framework checkpoint question to conversion after recording the backend gate.

Safe defaults

  • Prefer pretrained=False, an empty checkpoint argument, one small synthetic input, and --num-gpus=0 for diagnostics. A pretrained flag can download weights and is not an offline smoke test.
  • Run python <entrypoint>.py --help only to inspect a CLI. Do not start training, evaluate a downloaded dataset, or execute conversion tests while diagnosing availability.
  • Do not infer CUDA support from a visible GPU, a CPU wheel, or a successful Python import. Require a matching framework/CUDA environment and a separate live verification.

For exact provider names, prepare_model signatures, entrypoint flags, and failure recovery, use the bundled references rather than expanding this router.

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