---

name: logging-core description: "Ordinary TensorBoard scalar logging, writer lifecycle, and event-file checks for tensorboardX." metadata: disco-role: operating disable-model-invocation: true license: MIT

logging-core

Use this sub-skill for the ordinary tensorboardX event-writing path: creating a writer, logging scalar-like values, managing run directories, and checking that TensorBoard can read the result.

Use this when

• the user needs SummaryWriter setup or logdir naming guidance
• the task is about add_scalar, add_scalars, hparams, or custom scalar layouts
• the user asks why TensorBoard does not show a run yet
• the code must flush, close, reopen, or resume a writer safely
• the user wants write_to_disk=False for dry runs or tests
• a small local event-file smoke check is enough to validate the workflow

Route out

• Rich media summaries such as images, audio, video, PR curves, mesh, and deeper text handling: ../rich-media-summaries/SKILL.md
• Graph, embedding, ONNX, and OpenVINO logging: ../graph-and-embedding-plugins/SKILL.md
• Multiprocessing, global writer, S3/GCS, and Comet integrations: ../remote-and-parallel-integrations/SKILL.md
• Shared install issues before a workflow is selected: ../../references/install-and-routing.md

Operating sequence

1. Choose a writable logdir or let SummaryWriter() create one.
2. Use slash-separated tags for related scalar plots.
3. Log scalar-like data with explicit steps whenever reproducibility matters.
4. Use with writer.use_metadata(global_step=step): ... when several calls share one step or wall time.
5. Call flush() when immediate visibility matters and close() when the run is done.
6. Inspect output with tensorboard --logdir <logdir> after event files exist.
7. If the run resumed after a crash, use purge_step instead of mixing stale and new steps blindly.

Bundled helpers

• scripts/tbx_logging_smoke.py: writes a tiny scalar, scalar-group, custom-scalar, text, and optional JSON-export run, then reports event-file counts.
• scripts/tbx_hparams_smoke.py: writes a tiny hparams comparison with distinct trial names.

Run each helper with --help first. Both are safe by default and create temporary output when no logdir is supplied.

References

• references/api-reference.md: verified method signatures, writer stack, logdir behavior, scalar groups, hparams, and lifecycle details.
• references/workflows.md: short recipes for scalar runs, hparams, metadata defaults, custom scalars, crash recovery, and local event-file sanity checks.
• references/troubleshooting.md: missing event files, delayed flushes, duplicate steps, scalar shape errors, JSON-export surprises, path issues, and memory growth.

Decision hints

• Use add_scalar() for one numeric series and add_scalars() for grouped charts.
• Use add_hparams() for comparing explicit trial configurations, not as a full hyperparameter search engine.
• Use write_to_disk=False only when the absence of event files is intended.
• Prefer context managers in short scripts so event files are closed even on exceptions.