Chainer

Chainer is a maintenance-phase deep learning framework. Use this skill when a task mentions Chainer, ChainerMN, ChainerX, onnx_chainer, or the core chainer training APIs.

Start here

• Read references/install.md when you need base install or optional-backend guidance.
• Read references/workflows.md for the high-level route map.
• Read references/api-overview.md when you need exact signatures or verified runtime facts.
• Read references/troubleshooting.md when import, backend, or build issues appear.
• Read references/repo-provenance.md when you need source freshness or version context.
• Run scripts/runtime_probe.py for a quick local health check.

Route map

sub-skills/training/

Use for define-by-run model building, datasets, iterators, trainers, serializers, GPU or CPU setup, static-graph usage, and single-node example workflows such as MNIST, CIFAR, PTB, or serialization.

sub-skills/export/

Use for ONNX-Chainer export, export_testcase, and Caffe export or conversion troubleshooting.

sub-skills/distributed/

Use for ChainerMN, MPI or mpi4py, communicator selection, data-parallel or model-parallel training, dataset scattering, multi-node evaluators, and multi-process debugging.

sub-skills/chainerx/

Use for ChainerX build or install questions, backend and device selection, native or cuda device parsing, ChainerX limitations, and fallback behavior.

Quick install and import check

pip install chainer
python - <<'PY'
import chainer
print(chainer.__version__)
PY

Common surfaces

• chainer.backends.cuda.available and chainer.backends.cuda.cudnn_enabled
• chainer.backends.intel64.is_ideep_available()
• chainerx.is_available()
• onnx_chainer.export(...)
• chainermn.create_communicator(...)
• chainer.print_runtime_info()

Shared smoke scripts

• scripts/runtime_probe.py
• scripts/training_smoke.py
• scripts/serialization_smoke.py
• scripts/export_smoke.py
• scripts/chainerx_probe.py
• scripts/chainermn_probe.py