[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-2446":3},{"id":4,"name":5,"fullName":6,"owner":5,"repo":5,"description":7,"homepage":8,"htmlUrl":9,"language":10,"languages":9,"totalLinesOfCode":9,"stars":11,"forks":12,"watchers":13,"openIssues":14,"contributorsCount":15,"subscribersCount":15,"size":15,"stars1d":16,"stars7d":17,"stars30d":18,"stars90d":15,"forks30d":15,"starsTrendScore":19,"compositeScore":20,"rankGlobal":9,"rankLanguage":9,"license":21,"archived":22,"fork":22,"defaultBranch":23,"hasWiki":24,"hasPages":24,"topics":25,"createdAt":9,"pushedAt":9,"updatedAt":38,"readmeContent":39,"aiSummary":40,"trendingCount":15,"starSnapshotCount":15,"syncStatus":41,"lastSyncTime":42,"discoverSource":43},2446,"onnx","onnx\u002Fonnx","Open standard for machine learning interoperability","https:\u002F\u002Fonnx.ai\u002F",null,"Python",20980,3945,423,190,0,8,42,174,40,45,"Apache License 2.0",false,"main",true,[26,27,28,29,30,31,32,33,34,5,35,36,37],"ai","artificial-intelligence","deep-learning","deep-neural-networks","dnn","keras","machine-learning","ml","neural-network","pytorch","scikit-learn","tensorflow","2026-06-12 02:00:41","\u003C!--\nCopyright (c) ONNX Project Contributors\n\nSPDX-License-Identifier: Apache-2.0\n-->\n\n\u003Cp align=\"center\">\u003Cimg width=\"40%\" src=\"https:\u002F\u002Fgithub.com\u002Fonnx\u002Fonnx\u002Fraw\u002Fmain\u002Fdocs\u002Fonnx-horizontal-color.png\" \u002F>\u003C\u002Fp>\n\n[![PyPI - Version](https:\u002F\u002Fimg.shields.io\u002Fpypi\u002Fv\u002Fonnx.svg)](https:\u002F\u002Fpypi.org\u002Fproject\u002Fonnx)\n[![CI](https:\u002F\u002Fgithub.com\u002Fonnx\u002Fonnx\u002Factions\u002Fworkflows\u002Fmain.yml\u002Fbadge.svg)](https:\u002F\u002Fgithub.com\u002Fonnx\u002Fonnx\u002Factions\u002Fworkflows\u002Fmain.yml)\n[![CII Best Practices](https:\u002F\u002Fbestpractices.coreinfrastructure.org\u002Fprojects\u002F3313\u002Fbadge)](https:\u002F\u002Fbestpractices.coreinfrastructure.org\u002Fprojects\u002F3313)\n[![OpenSSF Scorecard](https:\u002F\u002Fapi.securityscorecards.dev\u002Fprojects\u002Fgithub.com\u002Fonnx\u002Fonnx\u002Fbadge)](https:\u002F\u002Fapi.securityscorecards.dev\u002Fprojects\u002Fgithub.com\u002Fonnx\u002Fonnx)\n[![REUSE compliant](https:\u002F\u002Fapi.reuse.software\u002Fbadge\u002Fgithub.com\u002Fonnx\u002Fonnx)](https:\u002F\u002Fapi.reuse.software\u002Finfo\u002Fgithub.com\u002Fonnx\u002Fonnx)\n[![Ruff](https:\u002F\u002Fimg.shields.io\u002Fendpoint?url=https:\u002F\u002Fraw.githubusercontent.com\u002Fastral-sh\u002Fruff\u002Fmain\u002Fassets\u002Fbadge\u002Fv2.json)](https:\u002F\u002Fgithub.com\u002Fastral-sh\u002Fruff)\n[![abi3 compatible](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fabi3-compatible-brightgreen)](https:\u002F\u002Fdocs.python.org\u002F3\u002Fc-api\u002Fstable.html)\n\n[Open Neural Network Exchange (ONNX)](https:\u002F\u002Fonnx.ai) is an open ecosystem that empowers AI developers\nto choose the right tools as their project evolves. ONNX provides an open source format for AI models, both deep learning and traditional ML. It defines an extensible computation graph model, as well as definitions of built-in operators and standard\ndata types. Currently we focus on the capabilities needed for inferencing (scoring).\n\nONNX is [widely supported](http:\u002F\u002Fonnx.ai\u002Fsupported-tools) and can be found in many frameworks, tools, and hardware. Enabling interoperability between different frameworks and streamlining the path from research to production helps increase the speed of innovation in the AI community. We invite the community to join us and further evolve ONNX.\n\n\n# Use ONNX\n\n* [Documentation of ONNX Python Package](https:\u002F\u002Fonnx.ai\u002Fonnx\u002F)\n* [Tutorials for creating ONNX models](https:\u002F\u002Fgithub.com\u002Fonnx\u002Ftutorials)\n* [Pre-trained ONNX models](https:\u002F\u002Fgithub.com\u002Fonnx\u002Fmodels)\n\n# Learn about the ONNX spec\n\n* [Overview](https:\u002F\u002Fgithub.com\u002Fonnx\u002Fonnx\u002Fblob\u002Fmain\u002Fdocs\u002FOverview.md)\n* [ONNX intermediate representation spec](https:\u002F\u002Fgithub.com\u002Fonnx\u002Fonnx\u002Fblob\u002Fmain\u002Fdocs\u002FIR.md)\n* [Versioning principles of the spec](https:\u002F\u002Fgithub.com\u002Fonnx\u002Fonnx\u002Fblob\u002Fmain\u002Fdocs\u002FVersioning.md)\n* [Operators documentation](https:\u002F\u002Fgithub.com\u002Fonnx\u002Fonnx\u002Fblob\u002Fmain\u002Fdocs\u002FOperators.md)\n* [Operators documentation](https:\u002F\u002Fonnx.ai\u002Fonnx\u002Foperators\u002Findex.html) (latest release)\n* [Python API Overview](https:\u002F\u002Fgithub.com\u002Fonnx\u002Fonnx\u002Fblob\u002Fmain\u002Fdocs\u002FPythonAPIOverview.md)\n\n# Programming utilities for working with ONNX Graphs\n\n* [Shape and Type Inference](https:\u002F\u002Fgithub.com\u002Fonnx\u002Fonnx\u002Fblob\u002Fmain\u002Fdocs\u002FShapeInference.md)\n* [Graph Optimization](https:\u002F\u002Fgithub.com\u002Fonnx\u002Foptimizer)\n* [Opset Version Conversion](https:\u002F\u002Fgithub.com\u002Fonnx\u002Fonnx\u002Fblob\u002Fmain\u002Fdocs\u002Fdocsgen\u002Fsource\u002Fapi\u002Fversion_converter.md)\n\n# Contribute\n\nONNX is a community project and the open governance model is described [here](https:\u002F\u002Fgithub.com\u002Fonnx\u002Fonnx\u002Fblob\u002Fmain\u002Fcommunity\u002Freadme.md). We encourage you to join the effort and contribute feedback, ideas, and code. You can participate in the [Special Interest Groups](https:\u002F\u002Fgithub.com\u002Fonnx\u002Fonnx\u002Fblob\u002Fmain\u002Fcommunity\u002Fsigs.md) and [Working Groups](https:\u002F\u002Fgithub.com\u002Fonnx\u002Fonnx\u002Fblob\u002Fmain\u002Fcommunity\u002Fworking-groups.md) to shape the future of ONNX.\n\nCheck out our [contribution guide](https:\u002F\u002Fgithub.com\u002Fonnx\u002Fonnx\u002Fblob\u002Fmain\u002FCONTRIBUTING.md) to get started.\n\nIf you think some operator should be added to ONNX specification, please read\n[this document](https:\u002F\u002Fgithub.com\u002Fonnx\u002Fonnx\u002Fblob\u002Fmain\u002Fdocs\u002FAddNewOp.md).\n\n# Community meetings\n\nThe schedules of the regular meetings of the Steering Committee, the working groups and the SIGs can be found [here](https:\u002F\u002Fonnx.ai\u002Fcalendar)\n\nCommunity Meetups are held at least once a year. Content from previous community meetups are at:\n\n* 2020.04.09 \u003Chttps:\u002F\u002Flf-aidata.atlassian.net\u002Fwiki\u002Fspaces\u002FDL\u002Fpages\u002F14091402\u002FLF+AI+Day+-ONNX+Community+Virtual+Meetup+-+Silicon+Valley+-+2020+April+9>\n* 2020.10.14 \u003Chttps:\u002F\u002Flf-aidata.atlassian.net\u002Fwiki\u002Fspaces\u002FDL\u002Fpages\u002F14092138\u002FLF+AI+Day+-+ONNX+Community+Workshop+-+2020+October+14>\n* 2021.03.24 \u003Chttps:\u002F\u002Flf-aidata.atlassian.net\u002Fwiki\u002Fspaces\u002FDL\u002Fpages\u002F14092424\u002FInstructions+for+Event+Hosts+-+LF+AI+Data+Day+-+ONNX+Virtual+Community+Meetup+-+March+2021>\n* 2021.10.21 \u003Chttps:\u002F\u002Flf-aidata.atlassian.net\u002Fwiki\u002Fspaces\u002FDL\u002Fpages\u002F14093194\u002FLF+AI+Data+Day+ONNX+Community+Virtual+Meetup+-+October+2021>\n* 2022.06.24 \u003Chttps:\u002F\u002Flf-aidata.atlassian.net\u002Fwiki\u002Fspaces\u002FDL\u002Fpages\u002F14093969\u002FONNX+Community+Day+-+2022+June+24>\n* 2023.06.28 \u003Chttps:\u002F\u002Flf-aidata.atlassian.net\u002Fwiki\u002Fspaces\u002FDL\u002Fpages\u002F14094507\u002FONNX+Community+Day+2023+-+June+28>\n\n# Discuss\n\nWe encourage you to open [Issues](https:\u002F\u002Fgithub.com\u002Fonnx\u002Fonnx\u002Fissues), or use [Slack](https:\u002F\u002Flfaifoundation.slack.com\u002F) (If you have not joined yet, please use this [link](https:\u002F\u002Fjoin.slack.com\u002Ft\u002Flfaifoundation\u002Fshared_invite\u002Fzt-3wx5vohc3-MeSYi3_dscb~u~cqs7zlPg) to join the group) for more real-time discussion.\n\n# Follow Us\n\nStay up to date with the latest ONNX news. [[Facebook](https:\u002F\u002Fwww.facebook.com\u002Fonnxai\u002F)] [[Twitter\u002FX](https:\u002F\u002Ftwitter.com\u002Fonnxai)]\n\n# Roadmap\n\nA roadmap process takes place every year. More details can be found [here](https:\u002F\u002Fgithub.com\u002Fonnx\u002Fsteering-committee\u002Ftree\u002Fmain\u002Froadmap)\n\n# Installation\n\nONNX released packages are published in PyPi.\n\n```sh\npip install onnx # or pip install onnx[reference] for optional reference implementation dependencies\n```\n\n[ONNX weekly packages](https:\u002F\u002Fpypi.org\u002Fproject\u002Fonnx-weekly\u002F) are published in PyPI to enable experimentation and early testing.\n\nDetailed install instructions, including Common Build Options and Common Errors can be found [here](https:\u002F\u002Fgithub.com\u002Fonnx\u002Fonnx\u002Fblob\u002Fmain\u002FINSTALL.md)\n\n# Python ABI3 Compatibility\n\nThis package provides [abi3](https:\u002F\u002Fdocs.python.org\u002F3\u002Fc-api\u002Fstable.html)-compatible wheels, allowing a single binary wheel to work across multiple Python versions (from 3.12 onwards).\n\n\n# Testing\n\nONNX uses [pytest](https:\u002F\u002Fdocs.pytest.org) as test driver. In order to run tests, you will first need to install `pytest`:\n\n```sh\npip install pytest\n```\n\nAfter installing pytest, use the following command to run tests.\n\n```sh\npytest\n```\n\n# Development\n\nCheck out the [contributor guide](https:\u002F\u002Fgithub.com\u002Fonnx\u002Fonnx\u002Fblob\u002Fmain\u002FCONTRIBUTING.md) for instructions.\n\n# Reproducible Builds (Linux)\n\nThis project provides reproducible builds for Linux.\n\nA *reproducible build* means that the same source code will always produce identical binary outputs, no matter who builds it or where it is built.\n\nTo achieve this, we use the [`SOURCE_DATE_EPOCH`](https:\u002F\u002Freproducible-builds.org\u002Fdocs\u002Fsource-date-epoch\u002F) standard. This ensures that build timestamps and other time-dependent information are fixed, making the output bit-for-bit identical across different environments.\n\n### Why this matters\n- **Transparency**: Anyone can verify that the distributed binaries were created from the published source code.\n- **Security**: Prevents tampering or hidden changes in the build process.\n- **Trust**: Users can be confident that the binaries they download are exactly what the maintainers intended.\n\nIf you prefer, you can use the prebuilt reproducible binaries instead of building from source yourself.\n\n# License\n\n[Apache License v2.0](LICENSE)\n\n# Trademark\nCheckout [https:\u002F\u002Ftrademarks.justia.com](https:\u002F\u002Ftrademarks.justia.com\u002F877\u002F25\u002Fonnx-87725026.html) for the trademark.\n\n[General rules of the Linux Foundation on Trademark usage](https:\u002F\u002Fwww.linuxfoundation.org\u002Flegal\u002Ftrademark-usage)\n\n# Code of Conduct\n\n[ONNX Open Source Code of Conduct](https:\u002F\u002Fonnx.ai\u002Fcodeofconduct.html)\n","ONNX（开放神经网络交换）是一个旨在促进机器学习模型互操作性的开放标准。它通过提供一种开源格式支持深度学习和传统机器学习模型的表示，定义了可扩展的计算图模型、内置算子及标准数据类型，当前主要聚焦于推理能力。基于Python开发，ONNX能够在多种框架（如TensorFlow, PyTorch, Keras等）、工具及硬件平台上实现模型的无缝迁移，极大地简化了从研究到生产的流程，加速了AI领域的创新速度。适合需要跨平台使用AI模型或希望提高模型部署灵活性的应用场景。",2,"2026-06-11 02:49:55","top_language"]