querying-mlflow-metrics

MLflow Metrics Run scripts/fetch_metrics.py to query metrics from an MLflow tracking server. Examples Token usage summary: python scripts/fetch_metrics.py -s http://localhost:5000 -x 1 -m total_tokens -a SUM,AVG Output: AVG: 223.91 SUM: 7613 Hourly token trend (last 24h): python scripts/fetch_metrics.py -s http://localhost:5000 -x 1 -m total_tokens -a SUM \ -t 3600 --start-time = "-24h" --end-time = now Output: Time-bucketed token sums per hour Latency percentiles by trace: python scripts/fetch_metrics.py -s http://localhost:5000 -x 1 -m latency -a AVG,P95 -d trace_name Error rate by status: python scripts/fetch_metrics.py -s http://localhost:5000 -x 1 -m trace_count -a COUNT -d trace_status Quality scores by evaluator (assessments): python scripts/fetch_metrics.py -s http://localhost:5000 -x 1 -v ASSESSMENTS \ -m assessment_value -a AVG,P50 -d assessment_name Output: Average and median scores for each evaluator (e.g., correctness, relevance) Assessment count by name: python scripts/fetch_metrics.py -s http://localhost:5000 -x 1 -v ASSESSMENTS \ -m assessment_count -a COUNT -d assessment_name JSON output: Add -o json to any command. Arguments Arg Required Description -s, --server Yes MLflow server URL -x, --experiment-ids Yes Experiment IDs (comma-separated) -m, --metric Yes trace_count , latency , input_tokens , output_tokens , total_tokens -a, --aggregations Yes COUNT , SUM , AVG , MIN , MAX , P50 , P95 , P99 -d, --dimensions No Group by: trace_name , trace_status -t, --time-interval No Bucket size in seconds (3600=hourly, 86400=daily) --start-time No -24h , -7d , now , ISO 8601, or epoch ms --end-time No Same formats as start-time -o, --output No table (default) or json For SPANS metrics ( span_count , latency ), add -v SPANS . For ASSESSMENTS metrics, add -v ASSESSMENTS . See references/api_reference.md for filter syntax and full API details.