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后端与数据库

instrumenting-with-mlflow-tracing

mlflow/skills

Add or repair MLflow tracing in Python or TypeScript applications, including tracing required for evaluation. Covers framework autologging, custom spans, backend setup, and persisted trace verification.

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安装命令

npx skills add https://github.com/mlflow/skills --skill instrumenting-with-mlflow-tracing

SKILL.md

Instrument MLflow tracing

Reuse working tracing for investigation or evaluation.

Prerequisites

  • Check entry point, language/framework, SDK version, tracking URI, experiment, and deployment identity.
  • Preserve destinations and providers. Explicit application destinations override inherited environment values; assistant telemetry may use another experiment.
  • Read Python or TypeScript for the runtime.

Step 1: Check the destination (Databricks only)

For OSS or SageMaker, keep the configured backend and proceed to Step 2.

  • Read UC setup before editing; validate auth/workspace.
  • Reuse the explicit destination; otherwise resolve schema choice before provisioning.
  • Discover an accessible warehouse automatically. Default to UC; legacy storage requires an explicit request.

Step 2: Enable framework tracing

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