Skip to main content

profile-model

Measure one TensorRT-Model-Connect model through its public Task API or run a checked-in performance-matrix entry with comparable evidence.

Informações da origem

Repositório
NVIDIA/TensorRT-Model-Connect
Última atividade na origem
23 de setembro de 2026 às 05:11
Idioma detectado do SKILL.md
inglês
Estrelas
273
Forks
71

Opções de instalação

Por padrão, está selecionado o prompt que primeiro revisa a origem. Você pode mudar para um comando direto ou baixar uma cópia local.

Revise os arquivos de origem

Leia o SKILL.md e os arquivos complementares exibidos pelo SkillsMP antes de decidir se vai instalar.

Explorador de arquivos
2 arquivos

Exibindo SKILL.md

SKILL.md
Instruções da origem · Visualização somente leitura
name
profile-model
description
Measure one TensorRT-Model-Connect model through its public Task API or run a checked-in performance-matrix entry with comparable evidence.
# Profile a Model Choose the evidence level before running: - For one model or testcase, use `trtmc-bench`. - For a checked-in release comparison, use `python3 -m qualification_tests.benchmark_qualification.performance`. The public benchmark boundary is `public_task_call_wall`. It excludes bundle building, process startup, warmup, report generation, and bundle loading. Current public tooling does not provide layer attribution; do not infer a kernel or layer bottleneck from Task API wall time alone. ## Before timing Run the owning family correctness testcase on the exact bundle. Record the repository and checkpoint revisions, GPU and driver, runtime root, family DSO, operation, input, seed, warmups, iterations, and synchronization boundary. For a focused run: ```bash trtmc-bench run --model <model> --case <case> \ --bundle <bundle> --no-build \ --warmup 3 --iterations 10 --runtime-root <runtime-root> \ --output <result-dir> ``` Use the same bundle kind, hardware, input, seed, warmups, iterations, and runtime policy for before/after comparisons. If any differ, report the confounder instead of one causal speedup percentage. For release evidence, first check and then run the exact entry: ```bash python3 -m qualification_tests.benchmark_qualification.performance check <suite> \ --environment <environment> --entry <entry> python3 -m qualification_tests.benchmark_qualification.performance run <suite> \ --environment <environment> --entry <entry> ``` Lead the report with correctness and evidence level. Include exact commands, p50 and other suite-owned statistics, measurement scope, limitations, and any target not run.
Ver no GitHub