Principle:Triton inference server Server Automated Profiling
| Field | Value |
|---|---|
| Page Type | Principle |
| Title | Automated_Profiling |
| Namespace | Triton_inference_server_Server |
| Knowledge Sources | Triton Server|https://github.com/triton-inference-server/server, source::Doc|Model Analyzer|https://github.com/triton-inference-server/model_analyzer |
| Domains | Performance, Model_Serving, Optimization |
| Last Updated | 2026-02-13 17:00 GMT |
Overview
Process of systematically exploring configuration parameter spaces to find optimal model serving settings. Automated profiling replaces manual trial-and-error tuning with a disciplined sweep of configuration variants, each evaluated under controlled benchmarking conditions.
Description
Automated profiling generates and evaluates multiple model configuration variants by sweeping parameters like instance count, max batch size, and dynamic batching settings. Each variant is profiled under controlled conditions to build a performance dataset that maps configuration to metrics. This eliminates manual trial-and-error tuning.
The profiling process works as follows:
- Configuration generation -- The profiler generates a set of model configuration variants by combining values across multiple parameter axes (instance count, max batch size, dynamic batching preferred sizes, queue delay).
- Sequential evaluation -- Each configuration variant is deployed on Triton and benchmarked using Perf Analyzer at multiple concurrency levels.
- Metric collection -- For each variant, the profiler records throughput, latency percentiles, and GPU memory consumption.
- Checkpoint storage -- All profiling results are persisted to a checkpoint directory for later analysis.
Configuration parameters swept during profiling:
- instance_group count -- Number of concurrent model instances (1, 2, 3, ...)
- max_batch_size -- Maximum batch size the server will form (1, 2, 4, 8, ...)
- dynamic_batching.preferred_batch_size -- Preferred batch sizes for the dynamic batcher
- dynamic_batching.max_queue_delay_microseconds -- Maximum time to wait for batch formation
The search space grows combinatorially, so profiling tools support bounds on each parameter to keep the total number of variants tractable.
Usage
Automated profiling is used in the following scenarios:
- Initial deployment tuning -- When deploying a model for the first time, profile across the configuration space to find settings that meet throughput and latency requirements.
- Hardware migration -- When moving a model to new GPU hardware, re-profile to find optimal settings for the new compute and memory characteristics.
- Model version updates -- When a model is retrained or its architecture changes, re-profile to ensure configuration settings remain optimal.
- Multi-model co-location -- When multiple models share a GPU, profile each to find configurations that balance resource usage.
Prerequisites for automated profiling:
- A model deployed in a model repository with a valid
config.pbtxt - A running or launchable Triton Inference Server instance
- Sufficient GPU memory to test configuration variants with higher instance counts
Theoretical Basis
Configuration space search: generate config variants (instance_group x max_batch_size x dynamic_batching) then profile each and collect metrics. The search can be exhaustive (brute force) or guided (Bayesian, grid).
The configuration space can be modeled as a discrete multi-dimensional search problem:
Config Space = { (i, b, p, d) |
i in [1..max_instances],
b in [1..max_batch_size],
p in PowerSet(preferred_batch_sizes),
d in [0..max_queue_delay]
}
For each configuration point, the profiler measures a performance vector:
Performance(config) = (throughput, p99_latency, gpu_memory)
Search strategies:
- Brute force -- Enumerate all combinations within specified bounds. Guarantees finding the global optimum within the search space but is expensive for large spaces.
- Quick search -- Model Analyzer's default mode that uses heuristics to prune unpromising regions of the search space, reducing total profiling time.
- Manual specification -- User explicitly defines the configuration variants to test, useful when domain knowledge narrows the search space.
The output is a dataset of (configuration, performance) pairs that serves as input to the analysis step.