{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/5"}],"enrichment":{"capability":"Applies graph-level optimizations to ONNX models, including a library of prepackaged passes for common transformations like operator fusion and constant elimination.","skillfed_tags":["model-optimization","onnx","graph-rewriting"],"use_cases":["Reduce model size and inference latency by fusing adjacent operators into single kernels before deployment.","Apply constant folding to precompute static subgraphs and eliminate redundant computations at runtime.","Standardize model optimization across multiple ONNX backend implementations without reimplementing passes.","Batch-optimize a collection of ONNX models via command-line without writing Python scripts.","Prepare models for edge deployment by applying graph-level optimizations before quantization or pruning."],"what_it_does":"ONNX Optimizer is a C++ library with Python bindings that applies graph-level transformations to ONNX neural network models. It provides a set of prepackaged optimization passes\u2014such as operator fusion and constant elimination\u2014designed to be reusable across different ONNX backend implementations. The library aims to reduce duplication of optimization work by centralizing common transformations that can be expressed at the graph level without backend-specific knowledge.\n\nYou use it by loading an ONNX model, calling the optimizer with your chosen passes (or letting it apply the default fuse-and-elimination set), and saving the result. It also exposes a command-line interface for batch optimization. The package depends only on onnx and targets modern Python versions, with prebuilt wheels for common platforms.","worth_installing":"Yes, if you work with ONNX models and want to reduce inference cost. The package is actively maintained, has no known vulnerabilities, uses a permissive license, and offers both a Python API and command-line interface. Install friction is moderate due to compiled wheels, but prebuilt binaries for current Python versions mitigate that. Suitable for model optimization pipelines in production or research."},"id":"onnxoptimizer","links":{"html":"https://skillfed.io/packages/onnxoptimizer","md":"https://skillfed.io/packages/onnxoptimizer.md","pypi":"https://pypi.org/project/onnxoptimizer/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-01-07","license_spdx":"Apache-2.0","license_treatment":"permissive","name":"onnxoptimizer","python_support":"supports_current","summary":"ONNX Optimizer"},"popularity":{"monthly_downloads":413130,"position":6843,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.4.2"}