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training-observability

Inspect machine-learning training runs via the training-observe CLI.

2.6k|547|Updated May 22, 2015
Search Tags:#machine learning#metrics#observability#artifacts#training#plots
One-click install
npx skills add https://github.com/jolibrain/deepdetect --skill training-observability
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: training-observability
Source: https://github.com/jolibrain/deepdetect/tree/main/bindings/python/training-observability
Command: npx skills add https://github.com/jolibrain/deepdetect --skill training-observability

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps you inspect machine-learning training runs after the fact or while they are still running, without depending on a live Visdom dashboard or manually digging through artifacts.

Core Features & Use Cases

  • Run Summary Inspection: Check the latest status, scalar values, available plots, and artifact warnings for a training run.
  • Metric Trend Review: Pull raw metric points to verify whether loss, accuracy, or other signals are improving or plateauing.
  • Plot and Artifact Analysis: Render saved plots to PNG and inspect prediction images with their JSON metadata for deeper quality checks.
  • Use Case: A model trainer can quickly determine whether a segmentation run is converging, whether predictions look reasonable, and whether any visual evidence is missing before deciding on the next experiment.

Quick Start

Ask the assistant to inspect the specified training run with training-observe summary, then review the relevant metrics, plots, and latest artifacts for evidence.

Frequently Asked Questions about training-observability

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I check machine learning training metrics without a live dashboard?▼

You can inspect machine learning training metrics without a live dashboard by reading flushed metric values and generated plots directly from the run root to verify if loss or accuracy is improving.

Can I inspect prediction artifacts from a completed training run?▼

Yes, you can inspect prediction artifacts from a completed training run by rendering saved plots to PNG and reviewing prediction images alongside their associated JSON metadata for deeper quality checks.

How do I review metric trends for a live ML experiment?▼

To review metric trends for a live ML experiment, pull raw metric points from the active run to evaluate whether training signals are converging or plateauing without controlling the run itself.

Does this approach require Visdom to evaluate training results?▼

No, evaluating training results does not require Visdom, as you can inspect run summaries, metric trends, and visual outputs directly from the run root without relying on it as the source of truth.

What is needed to inspect a machine learning training run?▼

To inspect a machine learning training run, you need access to the run root directory and the training-observe CLI to execute summary, metrics, plots, render, and artifact inspection commands.

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