retrieving-mlflow-traces

You can search MLflow traces using filter expressions for status, execution_time_ms, tags, or metadata via the mlflow CLI or Python search_traces API to identify failed runs and inspect span-level details for root cause analysis.

Yes, you can retrieve a single MLflow trace by its trace ID to inspect spans, attributes, and human assessments. This allows detailed root cause analysis for specific slow traces or failed runs without searching the entire experiment history.

Filter slow MLflow traces by applying execution_time_ms conditions in your search expression. You can also combine filters for timestamp_ms, tags, metadata, span attributes, and assessment fields to narrow down performance bottlenecks across sessions and users.

Paginate MLflow trace search results by applying pagination parameters alongside filter expressions in either the mlflow CLI or Python search_traces API. This allows you to manage large result sets when investigating traces across multiple sessions, users, or experiments.

Yes, the MLflow Python search_traces API supports filtering traces by span attributes, along with execution_time_ms, timestamp_ms, tags, metadata, and assessment fields. This enables precise querying for debugging and performance analysis.

Format MLflow trace search output using JSON or table formatting options available in the mlflow CLI and Python API. Including span details in the output allows you to inspect span-level attributes and assessments effectively for root cause analysis.