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name: distributed description: "Routes ChainerMN distributed training, MPI, communicator, and model-parallel workflows." metadata: disco-role: operating disable-model-invocation: true license: MIT

Distributed

Use this sub-skill for ChainerMN workflows: multi-process data parallelism, MPI launch issues, communicator selection, distributed optimizers, dataset scattering, multi-node evaluators, and model-parallel links.

Typical requests

• "How do I modify a Chainer trainer for ChainerMN?"
• "Which communicator should I use?"
• "Why does create_communicator fail?"
• "How do I scatter datasets across workers?"
• "How do I use MultiNodeChainList or model-parallel seq2seq?"
• "Why do MPI workers hang after an exception?"

Read these first

• references/workflows.md for distributed training patterns.
• references/api-reference.md for communicators, optimizers, datasets, links, and extensions.
• references/troubleshooting.md for MPI, mpi4py, NCCL, and launch failures.

Use this script

• ../../scripts/chainermn_probe.py for a safe MPI / ChainerMN prerequisite check.

Include here

• chainermn.create_communicator(...)
• chainermn.create_multi_node_optimizer(...)
• chainermn.scatter_dataset(...)
• chainermn.create_multi_node_evaluator(...)
• chainermn.MultiNodeChainList
• MPI, mpi4py, NCCL, CUDA-aware MPI, and CPU-only naive communicator guidance

Route elsewhere

• Ordinary single-node training -> ../training/
• ONNX or Caffe model export -> ../export/
• ChainerX-specific device behavior -> ../chainerx/

Verification caveat

ChainerMN runtime verification requires MPI and usually mpi4py; GPU-scale workflows also require CuPy, NCCL, and CUDA-aware MPI. If those dependencies are unavailable, treat distributed execution as environment-blocked rather than disproven.