Principle:Tensorflow Serving Performance Monitoring
| Knowledge Sources | |
|---|---|
| Domains | Monitoring, Operations |
| Last Updated | 2026-02-13 17:00 GMT |
Overview
A metrics exposition mechanism that exports TensorFlow Serving performance data in Prometheus format for monitoring and alerting.
Description
Performance monitoring in TensorFlow Serving exposes internal metrics via a Prometheus-compatible HTTP endpoint. This enables operators to track:
- Batching metrics: Queue latency, batch sizes, wrapped run counts
- Model warmup metrics: Warmup request latency
- Inference metrics: Request counts, latencies, error rates
The monitoring system uses TensorFlow's built-in monitoring framework (CollectionRegistry) and exports metrics in Prometheus text format at /monitoring/prometheus/metrics.
Key batching metrics:
- /tensorflow/serving/batching_session/queuing_latency — Time spent waiting in batch queue
- /tensorflow/serving/batching_session/wrapped_run_count — Number of batched session runs
- /tensorflow/serving/model_warmup_latency — Model warmup execution time
Usage
Enable monitoring by configuring the MonitoringConfig protobuf (via --monitoring_config_file) and accessing the metrics endpoint. Integrate with Prometheus scraping and Grafana dashboards for production monitoring.
Theoretical Basis
# Abstract metrics exposition (NOT real implementation)
# GET /monitoring/prometheus/metrics
# Response format (Prometheus text):
# TYPE batching_session_queuing_latency histogram
# batching_session_queuing_latency_bucket{le="100"} 42
# batching_session_queuing_latency_bucket{le="120"} 55
# batching_session_queuing_latency_sum 5432.1
# batching_session_queuing_latency_count 100
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