{"categories":[{"label":"Software Development","url":"https://skillfed.io/packages/category/software-development/7"},{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/4"}],"enrichment":{"capability":"Simplifies ONNX neural network models by running constant folding, shape inference, and graph optimization passes to reduce redundant operators and produce smaller, faster models.","skillfed_tags":["model-optimization","onnx","deep-learning"],"use_cases":["Reduce the size of PyTorch models exported to ONNX before deployment to edge devices or inference servers.","Remove redundant reshape, transpose, and constant-computation nodes introduced during framework export.","Validate that a simplified ONNX model produces numerically equivalent outputs on random test inputs.","Upgrade or downgrade an ONNX model's opset version while cleaning up any redundant nodes the conversion introduces.","Simplify models containing custom operators like TensorRT plugins without manually moving them to a custom domain."],"what_it_does":"ONNX Simplifier takes a compiled ONNX neural network model and reduces it by eliminating redundant operations through constant folding, shape inference, and graph optimization. It runs a fixed-point loop of these three techniques until the model stops changing, then optionally validates the result against the original on random inputs to ensure correctness.\n\nThe package is used to shrink models exported from frameworks like PyTorch, making them faster to load and run while preserving numerical behavior. It handles both static and dynamic input shapes, supports custom operators (including TensorRT plugins), can inline local functions, simplify subgraphs, and convert between ONNX opset versions. It is available as a Python package with a CLI, a C API, a Rust wrapper, and a zero-install web version.","worth_installing":"Yes. The package is actively maintained, has no known vulnerabilities, uses permissive licenses, and solves a real problem in ONNX model deployment. Medium install friction is acceptable given the precompiled wheels for modern Python versions and multiple platforms. Install if you work with exported ONNX models and need to reduce their size or remove redundant operations."},"id":"onnxsim","links":{"html":"https://skillfed.io/packages/onnxsim","md":"https://skillfed.io/packages/onnxsim.md","pypi":"https://pypi.org/project/onnxsim/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-12","license_spdx":"MIT AND (Apache-2.0 OR BSD-2-Clause)","license_treatment":"permissive","name":"onnxsim","python_support":"supports_current","summary":"Simplify your ONNX model"},"popularity":{"monthly_downloads":542989,"position":6087,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.7.3"}