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name: tools-analysis-and-deployment description: "Route post-training analysis, visualization, checkpoint publishing, conversion, and TorchServe workflows." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Tools Analysis and Deployment

Use this sub-skill after you already have a log, result file, checkpoint, or image and need post-run analysis or packaging.

Use here for

• JSON log summaries and curve plots
• offline metrics and confusion matrices from saved predictions
• dataset browsing, scheduler plots, CAM, and t-SNE
• FLOPs / parameter estimates from a config or model name
• checkpoint publishing, conversion, reparameterization, and TorchServe packaging

Route elsewhere for

• Training, testing, resume, distributed launch, or command planning -> ../training-and-evaluation/SKILL.md
• Dataset schema, annotation formats, or custom registries -> ../datasets-and-customization/SKILL.md
• Simple inference or model listing -> ../model-zoo-inference/SKILL.md

Bundled helpers

• scripts/analyze_json_log.py — summarize JSON logs and optionally plot curves
• scripts/estimate_flops.py — estimate FLOPs/params from a config or model reference
• scripts/publish_checkpoint.py — publish a checkpoint without mutating the source file

Dependency gates

• Core: torch, mmcv, mmengine, matplotlib
• Optional: seaborn for plot styling, grad-cam for CAM, scikit-learn for t-SNE, torchserve and torch-model-archiver for packaging

Safe defaults

• Prefer file output over interactive display in headless environments.
• Treat external checkpoint formats as family-specific; choose the matching converter first.
• Preserve the source checkpoint and write published artifacts to a new path.