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name: distributed description: "Routes Ignite distributed launch, backend selection, auto-wrapping, and rank helper workflows." disable-model-invocation: true metadata: disco-role: operating license: BSD 3-Clause

Ignite distributed workflows

Use this sub-skill when the request is about distributed backends, launcher setup, rank helpers, or the auto_* wrappers in ignite.distributed.

Include here

• Parallel, initialize, finalize, spawn, show_config, available_backends, backend, device, and rank/world-size helpers.
• auto_dataloader, auto_model, auto_optim, and DistributedProxySampler.
• Collectives and coordination helpers such as all_reduce, all_gather, all_gather_tensors_with_shapes, broadcast, barrier, new_group, one_rank_only, and one_rank_first.
• Backend-specific routing for native PyTorch distributed (gloo, nccl, mpi), Horovod, and xla-tpu.
• Single-process serial runs, torchrun-style launches, and simple distributed smoke checks.

Exclude or route elsewhere

• Engine construction, resume logic, and deterministic loop behavior belong in sub-skills/engine/.
• Checkpointing, logger integration, schedulers, progress bars, and profiling belong in sub-skills/handlers/.
• Metric math and evaluator contracts belong in sub-skills/metrics/.
• Legacy ignite.contrib notes live in references/legacy-contrib.md.

Start here

• Read references/api-reference.md for backend names, launcher entry points, and collective-helper semantics.
• Read references/workflows.md for serial, single-process gloo, and launch-command recipes.
• Read references/troubleshooting.md when a backend is unavailable, initialization hangs, or a rank helper behaves unexpectedly.
• Run scripts/distributed_smoke.py for a safe serial-and-gloo check of the distributed helpers.

Common triggers

• "How do I use Ignite with torchrun or gloo?"
• "How do I adapt my dataloader, model, or optimizer for distributed training?"
• "Why does available_backends() only return ('gloo',)?"
• "How do I write rank-aware logging or a one-rank-only callback?"
• "How do I check the active backend, rank, or world size?"

Useful boundary notes

This route owns the backend and collective helpers, but not the training loop or the metric/handler logic that often uses them. When a workflow spans distributed setup plus model quality or checkpointing, keep the distributed-specific details here and send the loop or side-effect questions to the owning sub-skill.