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name: runtime-utilities-and-visualization description: "Use MMEngine logging, message hubs, visualizer backends, distributed and device utilities, environment checks, timers, progress bars, manager mixins, and testing helpers safely." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Runtime Utilities and Visualization

Use this sub-skill when a task mentions MMLogger, print_log, MessageHub, Visualizer, LocalVisBackend, TensorboardVisBackend, WandbVisBackend, MLflow/ClearML/Aim/DVCLive/Neptune backends, init_dist, get_dist_info, collect_results, master_only, get_device, collect_env, ManagerMixin, progress bars, Timer, or MMEngine testing helpers.

Read Order

• references/logging-visualization-workflows.md: Choose and use loggers, message hubs, visualizers, local outputs, and optional tracking-service backends.
• references/distributed-device-utilities.md: Use distributed rank helpers, safe result collection, device detection, environment reports, timers, progress helpers, manager mixins, and testing utilities.
• references/troubleshooting.md: Diagnose duplicate loggers, optional backend dependency failures, visualization output surprises, distributed no-op behavior, collection mismatches, device fallback, and test-helper pitfalls.
• scripts/runtime_env_check.py: Run a safe local import/environment diagnostic, construct a temporary LocalVisBackend visualizer, and report distributed availability without launching multi-process jobs.

Scope

This sub-skill owns MMEngine runtime utilities: mmengine.logging, mmengine.visualization, mmengine.dist, mmengine.device, mmengine.utils, mmengine.utils.dl_utils.collect_env, mmengine.testing, ManagerMixin-based singleton behavior, progress helpers, and timers.

Route adjacent issues to sibling skills:

• Use ../runner-and-training/SKILL.md for Runner LoggerHook, NaiveVisualizationHook, launch command placement, training log intervals, checkpoint/resume, and hook ordering.
• Use ../models-metrics-and-inference/SKILL.md for model complexity/analysis APIs, BaseModel, BaseMetric, evaluator contracts, and inference/TTA behavior.
• Use ../data-structures-and-io/SKILL.md for file format IO, file backends, datasets, samplers, collation, and data element schemas.
• Use ../configuration-and-registry/SKILL.md for config syntax, registries, DefaultScope, and config-driven object construction.

Fast Workflow

1. For one-off logging, call print_log; for reusable project logging, create a named MMLogger.get_instance(...) and reuse that instance name.
2. For training/runtime state shared across components, store numeric histories with MessageHub.update_scalar(s) and overwrite current metadata with MessageHub.update_info.
3. For visualization without credentials, prefer Visualizer(..., vis_backends=[dict(type='LocalVisBackend')], save_dir=...); add TensorBoard or service backends only when dependencies and credentials are available.
4. For distributed-safe code, call get_dist_info() first; expect (0, 1) outside distributed launch and remember master_only silently returns None on non-main ranks.
5. Before debugging user environments, run python scripts/runtime_env_check.py --help from this sub-skill directory, then run it with an optional --work-dir that points to a disposable output directory.

Safe Operating Rules

• Do not initialize distributed jobs from helper scripts unless the user has explicitly launched the process group with PyTorch, MPI, or Slurm environment variables.
• Do not require optional tracking services, credentials, network access, or original MMEngine repository files; use LocalVisBackend as the safe fallback.
• Treat visualizer save_dir as a user project output path and keep generated runtime skill files independent from any user experiment outputs.