Production Compilation Pipeline and Failure Modes

Building a robust production compilation pipeline requires treating compiled artifacts as versioned, cacheable build outputs rather than runtime side effects. The standard pattern is to compile during the continuous integration or model release phase, store engines in an artifact repository keyed by model hash and hardware profile, then deploy them through your serving infrastructure with health checks and rollback capabilities. This avoids cold start latency from just in time compilation and surfaces conversion failures before they reach production. Implement shape profiling and artifact segmentation. For models with variable input shapes, such as sequence models with dynamic lengths or vision models accepting multiple resolutions, define a small set of shape profiles like sequence length buckets of 32, 64, 128, 256 tokens or image sizes of 224, 384, 512 pixels. Compile a separate engine per profile and route requests to the correct artifact based on input dimensions. Avoid dynamic shape support when possible because it often falls back to slower code paths or triggers recompilation at runtime. For truly dynamic cases, use explicit batch dimension ranges and test boundary conditions thoroughly. The most insidious failure mode is training serving skew. If your model is trained with certain preprocessing, data types, or numerical ranges but compiled with different assumptions, accuracy silently degrades. For example, training with pixel values in 0 to 255 range but serving with 0 to 1 normalized inputs causes a 20% accuracy drop that offline validation may miss if it uses training preprocessing. Maintain a manifest that records exact preprocessing steps, normalization constants, and expected input ranges. Build golden test suites that run the same inputs through uncompiled reference paths and compiled engines, comparing outputs tensor by tensor. Calibration data mismatch is another common trap. If you calibrate INT8 models on clean validation data but production receives images with different lighting, compression artifacts, or demographic distributions, quantization parameters become suboptimal. The model may produce overconfident predictions on out of distribution inputs or output zeros for certain activation patterns. Collect calibration samples from production traffic or augment validation data to match production characteristics. Implement drift detection that compares input statistics between calibration and serving. For heterogeneous fleets, prebuild engines per device type and hardware generation. An engine compiled for compute capability 7.5 may fail or degrade on 8.0 devices. Driver version mismatches cause parse errors or missing kernel symbols. Tag artifacts with CUDA compute capability, driver version, and TensorRT version. At deployment, match container images to hardware profiles and fail loudly if mismatches occur. For multi tenant GPUs, peer workload interference can invalidate tuning assumptions, causing latency spikes. Monitor p99 latency per model and per device, and implement isolation or throttling when interference is detected.

NVIDIA Triton deploys engines from model repository, loading hash keyed artifacts per device type, validating with warmup requests, and rolling back on latency regression

Meta content moderation pipeline compiles models per shape profile, stores in versioned artifact store, and runs shadow production traffic through reference PyTorch paths to detect drift

Amazon SageMaker compiles models during endpoint creation, caches engines per instance type, and implements health checks that compare compiled vs uncompiled outputs before serving traffic