---

name: tensorboard-x description: "Router for tensorboardX logging, summaries, graph/projector workflows, and integration-safe writer usage." metadata: disco-role: operating disable-model-invocation: true license: MIT

tensorboard-x

Use this repo skill when a task mentions tensorboardX, SummaryWriter, TensorBoard event files, or Python code that logs training progress for later inspection in TensorBoard.

This root skill is a router. It points to focused sub-skills for the common tensorboardX workflow families and keeps the public runtime guidance self-contained.

Quick start

1. Install the package:

   python -m pip install tensorboardX
   

2. Verify the import:

   python -c "from tensorboardX import SummaryWriter; print(SummaryWriter)"
   

3. If you want to inspect a run directory, open TensorBoard with:

   tensorboard --logdir <logdir>
   

4. If you want a fast install check, run the bundled helper:

   python scripts/tbx_inspect_install.py --help
   python scripts/tbx_inspect_install.py
   

If you need graph/projector, media summaries, or remote writer integrations, install the optional packages described in references/install-and-routing.md. Do not assume every optional dependency is needed for every workflow.

Choose the right sub-skill

logging-core

Use this route for ordinary event logging and writer lifecycle tasks:

• SummaryWriter setup and logdir policy
• scalar logging, grouped scalars, hparams, custom scalars
• use_metadata, purge_step, flush, close, reopen
• write_to_disk=False
• event-file sanity checks and tensorboard --logdir

Open sub-skills/logging-core/SKILL.md when the user asks about scalar series, run directories, hparams, or writer behavior.

rich-media-summaries

Use this route for image, audio, video, histogram, PR curve, text, or mesh summaries.

Open sub-skills/rich-media-summaries/SKILL.md when the user asks about payload shapes, media dependencies, or summary builders such as tensorboardX.summary.image() and SummaryWriter.add_video().

graph-and-embedding-plugins

Use this route for model graphs and projector output:

• SummaryWriter.add_graph() for PyTorch modules
• SummaryWriter.add_onnx_graph()
• SummaryWriter.add_openvino_graph()
• SummaryWriter.add_embedding() and projector metadata/label-image layout

Open sub-skills/graph-and-embedding-plugins/SKILL.md when the user asks about tracing models, loading ONNX/OpenVINO files, or writing embedding projector data.

remote-and-parallel-integrations

Use this route for integration boundaries:

• GlobalSummaryWriter
• multiprocessing or multi-module logging
• s3:// and gs:// record-writer paths
• Comet forwarding and GPU telemetry as optional input sources

Open sub-skills/remote-and-parallel-integrations/SKILL.md when the task crosses process, credential, or service boundaries.

What this skill does not do

• It does not expose a tensorboardX CLI; use Python APIs plus TensorBoard as the viewer.
• It does not rely on the original repository checkout at runtime.
• It does not ask future agents to run the source repo's examples or tests.
• It does not treat remote/cloud integrations as enabled by default.

Shared references

• references/install-and-routing.md: package install options, optional dependency map, and route selection guidance.
• references/troubleshooting.md: cross-cutting import, event-file, and optional dependency issues.
• references/repo-provenance.md: source commit and refresh baseline.
• references/repo-routing-metadata.json: router metadata used by the managed repo-skills router.

Common escape hatch

If you only need to confirm the installation before choosing a workflow, start with scripts/tbx_inspect_install.py. It is safe, self-contained, and does not depend on the original repository checkout.