# onnx/onnx

Open standard for machine learning interoperability

Repository: https://github.com/onnx/onnx
Canonical: https://ross.abutalabs.com/products/onnx
Homepage: https://onnx.ai/
Language: Python
License: Apache-2.0
License Family: permissive
Topics: deep-learning, deep-neural-networks, neural-network, onnx, pytorch, tensorflow, keras, scikit-learn, ml, machine-learning, dnn, ai, artificial-intelligence
Last push: 2026-08-26T19:23:48+00:00

## Health v2 (maintenance only)
Score: 92/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 77, longevity 100
- inputs: {"age_days": 3282, "days_push": 7, "days_rel": 79, "gap_med": 76, "n_releases_24m": 8}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 21366, forks 4011 (observed 2026-08-28T04:11:31.624439+00:00)

## What it is
ONNX (Open Neural Network Exchange) is an open standard and Python library for representing machine learning and deep learning models as an extensible computation graph with defined operators and data types. It enables model interoperability between frameworks like PyTorch, TensorFlow, and scikit-learn and across diverse inference runtimes and hardware.

## Use cases
- convert a pytorch model to run in a different inference engine
- export tensorflow models to a portable format
- run machine learning models on edge hardware
- share trained models between frameworks
- deploy models to production with hardware-optimized runtimes
- inspect and validate neural network computation graphs

## When to choose
- you need to move models between training frameworks and inference runtimes
- you want hardware-accelerated deployment across vendors
- you need a stable, widely supported open model format

## When to avoid
- you only train and infer within a single framework with no portability needs
- you need training-time features, since ONNX focuses on inferencing
- you need framework-specific optimizations not expressible in the ONNX operator set

## Facets
- artifact type: library
- maturity: stable
- function: machine-learning, deep-learning, serialization, sdk
- domain: machine-learning, deep-learning, artificial-intelligence, developer-tools
- platform: python, cross-platform
- tags: onnx, model-format, interoperability, neural-networks, inference, computation-graph, pytorch, tensorflow

## Member repositories
- onnx/onnx (main) score 92

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:11:31.624439+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T16:58:07.922411+00:00, confidence not recorded.
  - readme: https://github.com/onnx/onnx (fetched 2026-08-28T04:11:31.624439+00:00, sha 6df59a23ad78)
  - homepage: https://onnx.ai/ (fetched 2026-08-29T07:56:46.973725+00:00, sha d1e78f2c07f3)
  - site_page: https://onnx.ai/about.html (fetched 2026-08-29T07:56:46.976681+00:00, sha 2928d89a5b86)
- Data as of 2026-08-30T08:39:29.467469+00:00.