onnx-weekly
Open Neural Network Exchange
Decision gist · record as of 2026-08-14
Yes, if you are actively developing with ONNX or need the latest features for testing and experimentation. The weekly release cadence, active maintenance, permissive Apache-2.0 license, and broad platform support make it a low-risk choice for early adopters. However, if you need production stability, use the stable onnx package instead. No known security vulnerabilities as of 2026-08-14.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python ≥3.10; numpy, protobuf, typing_extensions, and ml_dtypes must be installed as runtime dependencies.
- Medium install friction with prebuilt wheels across multiple Python versions (3.10–3.14) and platforms (macOS, Linux, Windows, WebAssembly).
- Active maintenance with a recent release; last commit 2026-08-14.
License · maintenance · safety
Apache-2.0 (permissive) — Apache-2.0 permissive license allows commercial and private use with minimal restrictions; you must include a copy of the license and state significant changes.
last release 2026-08-05 (9 days) · last repo commit 2026-08-14 · 21,312 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 264,500 downloads/mo, #8,338 on PyPI
Alternatives
Verify before relying
pip install onnx-weekly
import onnx
# Load or create an ONNX model
model = onnx.load('model.onnx')- Whether onnx-weekly is intended for production use or experimental/pre-release testing only
- Specific differences between onnx-weekly and the stable onnx package beyond version timing
- Performance characteristics or known limitations of the development release
What it is and what it does
onnx-weekly is a development release of the ONNX Python package, distributed weekly to enable early testing and experimentation with the latest ONNX specification and tooling. It provides APIs for loading, creating, validating, and manipulating ONNX computation graphs—the standardized intermediate representation used across deep learning and traditional ML frameworks.
The package serves as a bridge for model interoperability: you can export models from frameworks like PyTorch or TensorFlow into ONNX format, then load and run them in different environments or hardware. It includes utilities for shape and type inference, graph optimization, and opset version conversion. As a weekly build, it tracks the main development branch and is intended for developers who want to test new features or contribute to ONNX before stable releases.
Use it for
- Export trained models from PyTorch, TensorFlow, or other frameworks to ONNX format for cross-platform deployment
- Load and inspect ONNX model graphs to understand model structure, operators, and data types
- Test new ONNX specification features and operators before they appear in stable releases
- Validate model compatibility and perform shape/type inference on computation graphs
- Convert models between different ONNX opset versions for compatibility with target inference engines
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are actively developing with ONNX or need the latest features for testing and experimentation.
The weekly release cadence, active maintenance, permissive Apache-2.0 license, and broad platform support make it a low-risk choice for early adopters. However, if you need production stability, use the stable onnx package instead. No known security vulnerabilities as of 2026-08-14.
Install
onnx-weekly on PyPI
Before you install
Medium install friction with prebuilt wheels across multiple Python versions (3.10–3.14) and platforms (macOS, Linux, Windows, WebAssembly). Active maintenance with a recent release; last commit 2026-08-14. Depends on numpy, protobuf, typing_extensions, and ml_dtypes.
Requires Python ≥3.10; numpy, protobuf, typing_extensions, and ml_dtypes must be installed as runtime dependencies.
License in practice
Apache-2.0 permissive license allows commercial and private use with minimal restrictions; you must include a copy of the license and state significant changes.
Quickstart
pip install onnx-weekly
import onnx
# Load or create an ONNX model
model = onnx.load('model.onnx')Verify before relying
- Whether onnx-weekly is intended for production use or experimental/pre-release testing only
- Specific differences between onnx-weekly and the stable onnx package beyond version timing
- Performance characteristics or known limitations of the development release
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | 4 packagesnumpyprotobuftyping_extensionsml_dtypes |
| Maintenance | Actively maintained 9 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 264,500 / month, #8,338 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Programming Language :: Python :: 3 |
Evidence: onnx_weekly-1.23.0.dev20260805-cp310-cp310-macosx_13_0_universal2.whl; onnx_weekly-1.23.0.dev20260805-cp310-cp310-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl; onnx_weekly-1.23.0.dev20260805-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl; onnx_weekly-1.23.0.dev20260805-cp310-cp310-win32.whl; onnx_weekly-1.23.0.dev20260805-cp310-cp310-win_amd64.whl; onnx_weekly-1.23.0.dev20260805-cp311-cp311-macosx_13_0_universal2.whl; onnx_weekly-1.23.0.dev20260805-cp311-cp311-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl; onnx_weekly-1.23.0.dev20260805-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl; onnx_weekly-1.23.0.dev20260805-cp311-cp311-win32.whl; onnx_weekly-1.23.0.dev20260805-cp311-cp311-win_amd64.whl; onnx_weekly-1.23.0.dev20260805-cp311-cp311-win_arm64.whl; onnx_weekly-1.23.0.dev20260805-cp312-abi3-macosx_13_0_universal2.whl; onnx_weekly-1.23.0.dev20260805-cp312-abi3-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl; onnx_weekly-1.23.0.dev20260805-cp312-abi3-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl; onnx_weekly-1.23.0.dev20260805-cp312-abi3-pyemscripten_2026_0_wasm32.whl; onnx_weekly-1.23.0.dev20260805-cp312-abi3-win32.whl; onnx_weekly-1.23.0.dev20260805-cp312-abi3-win_amd64.whl; onnx_weekly-1.23.0.dev20260805-cp312-abi3-win_arm64.whl; onnx_weekly-1.23.0.dev20260805-cp314-cp314t-macosx_13_0_universal2.whl; onnx_weekly-1.23.0.dev20260805-cp314-cp314t-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl
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