{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/6"}],"enrichment":{"capability":"onnx-weekly provides a Python package for working with ONNX (Open Neural Network Exchange) models\u2014an open format for representing AI models with a computation graph, built-in operators, and standard data types for model interoperability and inference.","skillfed_tags":["model-interchange","neural-networks"],"use_cases":["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"],"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\u2014the standardized intermediate representation used across deep learning and traditional ML frameworks.\n\nThe 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.","worth_installing":"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."},"id":"onnx-weekly","links":{"html":"https://skillfed.io/packages/onnx-weekly","md":"https://skillfed.io/packages/onnx-weekly.md","pypi":"https://pypi.org/project/onnx-weekly/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-05","license_spdx":"Apache-2.0","license_treatment":"permissive","name":"onnx-weekly","python_support":"supports_current","summary":"Open Neural Network Exchange"},"popularity":{"monthly_downloads":264500,"position":8338,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.23.0.dev20260805"}