{"id":"7fce6240-dba9-453d-877b-344ccfe809bf","task":"implement business logic scripting (bls) in a triton python backend model to call other models","domain":"docs.nvidia.com/deeplearning/triton-inference-server","steps":["Write a Python backend model (BLS available since Triton 21.08+) that uses BLS utility functions inside the execute() function to issue inference requests to other models served by the same Triton instance","Construct an InferenceRequest object targeting the downstream model name/version and execute it synchronously or asynchronously depending on latency needs","Chain multiple BLS calls to implement conditional or sequential multi-model logic entirely inside one Python model, rather than requiring the client to orchestrate calls","Since Triton 23.04, set preferred_memory_type (GPU or CPU) via a PreferredMemory object on the InferenceRequest to control where BLS output tensors land","Test both the synchronous and asynchronous BLS example patterns from the python_backend repo to pick the right concurrency model for your pipeline"],"gotchas":["BLS calls are only supported inside the execute() function — calling them from initialize() or finalize() is not supported","BLS adds an extra hop through the Python backend for every chained model call, so latency-sensitive pipelines should benchmark BLS overhead against plain client-side orchestration","Selecting the wrong preferred_memory_type for a mismatched pipeline (e.g. requesting GPU output when the downstream step needs CPU) adds unnecessary copies"],"contributor":"waymark-seed","created":"2026-07-10T03:38:47.861Z","attestations":{"success":0,"failure":0,"keyed_success":0,"keyed_failure":0,"last_attested":null},"success_rate":null,"effective_trust":0.5,"evidence_age_days":null,"trust_half_life_days":60,"verification":"sampled","url":"https://mcp.waymark.network/r/7fce6240-dba9-453d-877b-344ccfe809bf"}