{"categories":[{"label":"Testing","url":"https://skillfed.io/packages/category/software-development-testing/6"}],"enrichment":{"capability":"Runs inference tests on ONNX models using TensorRT, CUDA, OpenVINO, or CPU execution providers, measuring performance and validating model outputs.","skillfed_tags":["onnx-inference","model-benchmarking","gpu-acceleration"],"use_cases":["Benchmark an ONNX model's inference speed on TensorRT or CUDA before deploying to production.","Validate that a model with dynamic input shapes runs correctly by specifying fixed dimensions and test data.","Compare inference latency across different execution providers (CPU vs. GPU vs. TensorRT) on the same model.","Profile a model's performance and export timing results to JSON for analysis and reporting.","Test a model end-to-end with custom numpy array inputs to verify output correctness before integration."],"what_it_does":"sit4onnx is a command-line and Python API tool for testing ONNX model inference across different hardware accelerators and execution providers. It accepts an ONNX model file and runs it repeatedly to measure average inference latency, with options to specify batch sizes, fixed input shapes, and custom test data via numpy arrays or .npy files. The tool supports TensorRT, CUDA, OpenVINO (CPU and GPU), and CPU-only execution, and can output performance profiling results and inference outputs to files.\n\nThe package is designed for model validation and benchmarking workflows where you need to verify that a model runs correctly on a target platform and measure its throughput or latency. It handles models with dynamic input shapes by allowing you to specify fixed dimensions, and it returns the final inference result as a numpy array when called from Python code.","worth_installing":"Yes, if you need to test and benchmark ONNX models across different execution providers. The low install friction and permissive license make it a practical choice. However, note that the package is aging (last commit 341 days ago) and you will need to separately install the execution provider libraries (TensorRT, OpenVINO, etc.) that you intend to use."},"id":"sit4onnx","links":{"html":"https://skillfed.io/packages/sit4onnx","md":"https://skillfed.io/packages/sit4onnx.md","pypi":"https://pypi.org/project/sit4onnx/"},"maintenance":{"status":"aging"},"meta":{"latest_release":"2025-09-07","license_spdx":null,"license_treatment":"permissive","name":"sit4onnx","python_support":"supports_current","summary":"Tools for simple inference testing using TensorRT, CUDA and OpenVINO CPU/GPU and CPU providers. Simple Inference Test for ONNX."},"popularity":{"monthly_downloads":82850,"position":14125,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.0.10"}