---

name: mmengine description: "Use MMEngine to configure, train, evaluate, inspect, visualize, and troubleshoot PyTorch/OpenMMLab workflows." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

MMEngine Repo Skill

Use this skill when a task mentions MMEngine, OpenMMLab engine utilities, Config, Registry, Runner, FlexibleRunner, hooks, optim wrappers, schedulers, BaseDataset, BaseModel, BaseMetric, Visualizer, or MMEngine distributed/runtime helpers.

Start Here

• references/package-overview.md: Understand MMEngine's package layout, dependency model, public API families, and how the sub-skills fit together.
• references/troubleshooting.md: Diagnose cross-cutting install/import, optional dependency, config, runner, data, model, visualization, and distributed issues.
• scripts/mmengine_import_check.py: Run a safe import/API smoke check for the installed mmengine package before deeper debugging.
• references/repo-provenance.md: Check the source version and evidence baseline used to create this skill.
• references/repo-routing-metadata.json: Structured routing metadata consumed by DisCo's managed repo-skills router.

Route by Task

User task signal	Read this sub-skill	Why
Config files, _base_, CLI overrides, lazy imports, custom_imports, Registry, build_from_cfg, _scope_, default scope	sub-skills/configuration-and-registry/SKILL.md	Owns MMEngine config parsing, merge/dump, registry scopes, object-build diagnostics, and a safe config inspection helper.
Runner, FlexibleRunner, training/validation/testing loops, hooks, checkpoint/resume, AMP, optimizers, schedulers, distributed launch, large-model strategies	sub-skills/runner-and-training/SKILL.md	Owns training orchestration, Runner config shape, hook/optimizer/scheduler interactions, checkpoint semantics, and dependency-gated distributed/strategy guidance.
BaseDataset, transforms, samplers, collate functions, InstanceData, PixelData, LabelData, mmengine.fileio, backend_args	sub-skills/data-structures-and-io/SKILL.md	Owns data contracts, dataset initialization, transform pipelines, data elements, and backend-agnostic file IO.
BaseModel, BaseModule, preprocessors, model step modes, BaseMetric, Evaluator, DumpResults, inferencers, TTA, complexity analysis	sub-skills/models-metrics-and-inference/SKILL.md	Owns model/evaluator contracts, prediction/result shapes, TTA wrappers, inferencer patterns, and analysis limits.
MMLogger, MessageHub, Visualizer, visual backends, init_dist, collect_results, master_only, devices, collect_env, timers, testing helpers	sub-skills/runtime-utilities-and-visualization/SKILL.md	Owns logging, visualization, optional tracking backends, distributed/device utilities, environment reports, and runtime diagnostics.

Common Workflows

1. For a full training project, read in this order: configuration-and-registry, data-structures-and-io, models-metrics-and-inference, runner-and-training, then runtime-utilities-and-visualization for logging/visual output.
2. For a config-only edit, stay in configuration-and-registry unless the edit changes runner loop placement, model/metric contracts, or dataset schemas.
3. For a failed training run, identify the failing layer first: config build, dataset/collate, model/metric contract, runner/hook/optimizer scheduling, or runtime backend.
4. For optional services or large-model/distributed workflows, keep the safe local path working first, then add TensorBoard/WandB/MLflow/DeepSpeed/FSDP/ColossalAI only when dependencies, credentials, launchers, and hardware are available.

Safe Checks

From this skill directory, these helpers are safe and self-contained:

python scripts/mmengine_import_check.py --json
python sub-skills/configuration-and-registry/scripts/inspect_config.py --help
python sub-skills/runner-and-training/scripts/runner_config_smoke.py --demo
python sub-skills/data-structures-and-io/scripts/data_contract_smoke.py --help
python sub-skills/models-metrics-and-inference/scripts/model_metric_smoke.py --help
python sub-skills/runtime-utilities-and-visualization/scripts/runtime_env_check.py --help

The helpers do not download data, start distributed jobs, require credentials, or run long training. Full examples and native repository tests remain verification evidence, not runtime dependencies for this skill.

Boundaries

• This skill is for using MMEngine as a package in user projects, not for maintaining MMEngine release infrastructure.
• Do not assume optional services such as WandB, MLflow, ClearML, Aim, DVCLive, Neptune, DeepSpeed, ColossalAI, or FSDP extras are installed unless the user's environment proves it.
• Do not route downstream task-library behavior, such as MMDetection-specific datasets or MMSegmentation models, into this skill except where generic MMEngine contracts apply.
• Prefer small import/config/smoke checks before running training, distributed launchers, or service-backed visualization.