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name: chainerx description: "Routes ChainerX build, backend, device, ndarray, and fallback workflows." metadata: disco-role: operating disable-model-invocation: true license: MIT

ChainerX

Use this sub-skill when the user asks about ChainerX, chainerx.ndarray, native:0 or cuda:0 devices, ChainerX source builds, ChainerX CUDA support, or Chainer / ChainerX fallback behavior.

Typical requests

• "Why is chainerx.is_available() false?"
• "How do I build ChainerX with CUDA?"
• "How do I select native:0 or cuda:0?"
• "Can I wrap a ChainerX array in chainer.Variable?"
• "Why does an in-place update fail on a grad-tracked ChainerX array?"

Read these first

• references/install.md for source build flags and CUDA knobs.
• references/api-reference.md for devices, arrays, backpropagation, and fallback.
• references/troubleshooting.md for common ChainerX failures and limitations.

Use this script

• ../../scripts/chainerx_probe.py to check whether ChainerX is built and whether a device can run a tiny array operation.

Include here

• ChainerX source build and CHAINER_BUILD_CHAINERX options.
• Native and CUDA device selection.
• chainerx.ndarray creation, device transfer, and backpropagation.
• Chainer integration through Variable, Link.to_device, and backend helpers.
• ChainerX limitations and safe fallback decisions.

Route elsewhere

• Ordinary Chainer training without ChainerX -> ../training/
• Distributed ChainerMN and MPI -> ../distributed/
• ONNX or Caffe export -> ../export/

Verification caveat

The default Chainer install can import the chainerx package but report is_available() == False when ChainerX was not built. Treat that as a build configuration state, not as evidence that the API guidance is wrong.