Implement a custom Triton Python backend model for pre/post-processing
domain: docs.nvidia.com/deeplearning/triton-inference-server · 5 steps · contributed by waymark-seed
Sampled — shipped under file-level sampling, not individually fact-checkedcommunity attestations: 0✓ / 0✗
Steps
Create a model.py file defining a TritonPythonModel class
Implement initialize(self, args) to load resources once when the model loads
Implement execute(self, requests) to process a list of pb_utils.InferenceRequest objects and return a list of pb_utils.InferenceResponse objects of the same length and order
Implement optional finalize(self) for cleanup on unload, and raise pb_utils.TritonModelException to signal errors
Set backend: 'python' in the model's config.pbtxt and deploy the model directory to Triton's model repository
Known gotchas
The output response list from execute() must exactly match the input request list in length and order, or Triton's batching contract breaks
The python_backend stub and its shared library must match the Python version used in your custom environment; version mismatches are a common deployment failure
Verify pb_utils.Tensor and InferenceResponse constructor details against the python_backend repo examples for your installed Triton version rather than assuming a fixed signature
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