Automating Regression Tests for TensorRT Inference Services

Frequent model updates and complex deployment environments can cause inference latency spikes, GPU memory overruns, or silent functional regressions, especially when using highly optimized TensorRT engines. Manual checks are insufficient, so a repeatable, measurable, and interceptable automated regression testing mechanism is required.

TensorRT provides 2–5× speedups through layer fusion, precision calibration, and hardware‑aware optimizations, but its deep customizations mean every model change must pass strict functional and performance validation.

The proposed system asks two questions for each change: does the new engine produce outputs within an acceptable error range, and are latency, throughput, and memory usage still within control? Functional consistency is measured against a "Golden Output" (e.g., L2 distance < 1e‑3 for FP32, tolerant thresholds for INT8). Performance control checks for average latency increases >10% or abnormal GPU utilization.

Understanding TensorRT as a compiler‑style optimizer is essential. Its workflow includes graph optimizations (e.g., Conv+BN+ReLU merging), constant folding, INT8 calibration, kernel auto‑tuning, and engine serialization. Because this process is lossy and irreversible, verification must happen immediately after build.

Typical engine‑build code (shown below) illustrates potential failure points such as insufficient max_workspace_size, FP16 incompatibility, low ONNX opset versions, or unhandled parser errors.

import tensorrt as trt
TRT_LOGGER = trt.Logger(trt.Logger.WARNING)
builder = trt.Builder(TRT_LOGGER)
network = builder.create_network(1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH))
config = builder.create_builder_config()
config.max_workspace_size = 1 << 30  # 1GB
if builder.platform_has_fast_fp16:
    config.set_flag(trt.BuilderFlag.FP16)
parser = trt.OnnxParser(network, TRT_LOGGER)
with open("model.onnx", "rb") as f:
    if not parser.parse(f.read()):
        for i in range(parser.num_errors):
            print(parser.get_error(i))
engine_bytes = builder.build_serialized_network(network, config)
with open("model.engine", "wb") as f:
    f.write(engine_bytes)

Automated testing first records build success, duration, warnings, and engine size. Only after passing the "build gate" does the pipeline proceed to functional and performance testing.

Functional testing runs the engine on a fixed input set (the "Golden Dataset") and compares outputs to stored golden tensors. Comparison strategies differ by task: top‑5 consistency or KL‑divergence for classification, anchor‑wise bbox and confidence checks for detection, IoU or PSNR for segmentation. Golden outputs must be version‑controlled alongside model and TensorRT versions.

Performance monitoring uses trtexec with warm‑up and duration flags to collect average latency, P99 latency, inferences‑per‑second, peak memory, SM utilization, and memory bandwidth. These metrics form trend curves; gradual latency creep can signal architectural changes that hinder TensorRT optimizations.

The CI/CD flow (illustrated in the ASCII diagram) orchestrates model repository checkout, conversion, engine build, functional testing, performance monitoring, and report generation. Containerizing the environment with NVIDIA NGC's official TensorRT image ensures consistent CUDA, cuDNN, and driver stacks.

+------------------+      +---------------------+
| Model Repo (Git/S3) | --> | Model Conversion   |
+------------------+      +----------+----------+
                               |
                               v
                     +-----------------------------+
                     | TensorRT Engine Build       |
                     +--------------+--------------+
                                    |
                                    v
               +------------+   +----------------+   +-------------------+
               | Functional |<--| Inference Exec |<--| Performance Monitor |
               +------------+   +----------------+   +-------------------+
                                    |
                                    v
                     +------------------------+
                     | Test Report & Alerts   |
                     +------------------------+

Common pitfalls include model compatibility issues (dynamic reshape, custom ops), INT8 quantization drift, and performance variability due to driver updates or thermal throttling. Mitigations involve using higher ONNX opsets, TensorRT plugins, strict calibration sets, fixed software stacks, dedicated test machines, and statistical averaging.

Best practices recommend covering multiple batch sizes, testing dynamic shapes, parallelizing tests for A/B comparisons, preserving full logs (build, inference trace, Nsight profiles), and regularly regenerating golden outputs to avoid reference drift.

The final report should detail functional deviations, performance trends versus the previous version, GPU memory delta, and critical warnings (e.g., "Layer Fusion skipped for 3 nodes"). If latency rises >10% or functional error exceeds tolerance, the system blocks the release and notifies developers, acting as a quality gate in MLOps.

Beyond immediate benefits, automated regression testing creates a transparent feedback loop that makes every model change auditable and roll‑back‑ready, a necessity for safety‑critical domains such as autonomous driving, medical imaging, and financial risk control. Future extensions will address LLM inference, dynamic batching, sparse computation, request‑level latency, and memory fragmentation analysis.

In summary, high performance must be guarded by high reliability, and an automated regression testing framework is the bridge connecting rapid AI innovation with system stability.