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retrieving-mlflow-traces

Retrieve MLflow traces by ID or search filters via CLI or Python API.

1|1|Updated Oct 1, 2025
Search Tags:#debugging#api#cli#search#filters#traces#mlflow
One-click install
npx skills add https://github.com/mkgs-databricks-demos/synthea-on-fhir --skill retrieving-mlflow-traces-mkgs-databricks-demos
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: retrieving-mlflow-traces
Source: https://github.com/mkgs-databricks-demos/synthea-on-fhir/tree/main/.cursor/skills/retrieving-mlflow-traces
Command: npx skills add https://github.com/mkgs-databricks-demos/synthea-on-fhir --skill retrieving-mlflow-traces-mkgs-databricks-demos

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Retrieves MLflow traces via CLI or Python API to support debugging and monitoring.

Core Features & Use Cases

  • Retrieve a single trace by trace ID using CLI or API.
  • Search traces by run, session, user, status, time range, tags, or metadata.
  • Inspect trace structures and spans to diagnose performance and errors.
  • Support pagination and configurable output formats to fit dashboards and reports.

Quick Start

Run a trace by ID with mlflow traces get --trace-id <trace_id> or search traces with mlflow traces search using your filters.

Frequently Asked Questions about retrieving-mlflow-traces

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

FAQPage Schema
How do I retrieve a specific MLflow trace by ID?▼

Retrieve a specific MLflow trace by running the CLI command `mlflow traces get --trace-id <trace_id>` or by calling the Python API with that trace ID to fetch structured trace and span details.

Can I search MLflow traces by run, user, or time range?▼

Yes, you can search MLflow traces by run, session, user, status, time range, tags, metadata, or execution time using the `mlflow traces search` CLI command or equivalent Python API filters.

How do I use filters and pagination when searching MLflow traces?▼

Searching MLflow traces supports filter syntax for tags, metadata, and execution time, along with pagination to handle large result sets and configurable output formats for reports.

What is the best way to debug MLflow models using traces?▼

The best way to debug MLflow models using traces is to inspect trace structures and spans to diagnose performance issues and errors, fetching them via CLI or Python API for monitoring.

Does MLflow trace retrieval work with both CLI and Python API?▼

Yes, MLflow trace retrieval works with both CLI and Python API, allowing you to fetch by trace ID or search with filters and output structured span details for dashboards.

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