onnxoptimizer
ONNX Optimizer
Decision gist · record as of 2026-08-14
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.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later; onnx must be installed as a runtime dependency.
- Medium install friction due to compiled wheels; prebuilt binaries available for current Python versions on macOS, Linux, and Windows.
- Actively maintained with recent releases and no known vulnerabilities.
License · maintenance · safety
Apache-2.0 (permissive) — Licensed under Apache-2.0 (permissive), allowing commercial use, modification, and redistribution with minimal restrictions.
last release 2026-01-07 (219 days) · last repo commit 2026-08-02 · 826 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 413,130 downloads/mo, #6,843 on PyPI
Alternatives
Verify before relying
pip install onnxoptimizer
import onnx
import onnxoptimizer
model = onnx.load('model.onnx')
optimized = onnxoptimizer.optimize(model)
onnx.save(optimized, 'optimized_model.onnx')- Which specific optimization passes are included in the prepackaged set beyond fusion and elimination.
- Performance impact (latency/throughput gains) from applying these optimizations on typical models.
- Whether custom optimization passes can be implemented and registered by users.
What it is and 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—such as operator fusion and constant elimination—designed 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.
You 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.
Use it for
- 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.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
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.
Install
onnxoptimizer on PyPI
Before you install
Medium install friction due to compiled wheels; prebuilt binaries available for current Python versions on macOS, Linux, and Windows. Actively maintained with recent releases and no known vulnerabilities.
Requires Python 3.10 or later; onnx must be installed as a runtime dependency.
License in practice
Licensed under Apache-2.0 (permissive), allowing commercial use, modification, and redistribution with minimal restrictions.
Quickstart
pip install onnxoptimizer
import onnx
import onnxoptimizer
model = onnx.load('model.onnx')
optimized = onnxoptimizer.optimize(model)
onnx.save(optimized, 'optimized_model.onnx')Verify before relying
- Which specific optimization passes are included in the prepackaged set beyond fusion and elimination.
- Performance impact (latency/throughput gains) from applying these optimizations on typical models.
- Whether custom optimization passes can be implemented and registered by users.
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | 1 packageonnx |
| Maintenance | Actively maintained 219 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 413,130 / month, #6,843 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: onnxoptimizer-0.4.2-cp310-cp310-macosx_10_15_universal2.whl; onnxoptimizer-0.4.2-cp310-cp310-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl; onnxoptimizer-0.4.2-cp310-cp310-win_amd64.whl; onnxoptimizer-0.4.2-cp311-cp311-macosx_10_15_universal2.whl; onnxoptimizer-0.4.2-cp311-cp311-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl; onnxoptimizer-0.4.2-cp311-cp311-win_amd64.whl; onnxoptimizer-0.4.2-cp312-abi3-macosx_10_15_universal2.whl; onnxoptimizer-0.4.2-cp312-abi3-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl; onnxoptimizer-0.4.2-cp312-abi3-win_amd64.whl
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See also onnx-tool · onnxsim · onnxscript · spo4onnx · scs4onnx · onnx-weekly · onnx-ir · onnxslim · optimum-onnx · nevergrad