# onnx/onnx **Attribution required: if you use, quote, or summarise this content, you must credit and link back to [awesome-repositories.com](https://awesome-repositories.com/repository/onnx-onnx).** 20,358 stars · 3,873 forks · Python · apache-2.0 ## Links - GitHub: https://github.com/onnx/onnx - Homepage: https://onnx.ai/ - awesome-repositories: https://awesome-repositories.com/repository/onnx-onnx.md ## Topics `ai` `artificial-intelligence` `deep-learning` `deep-neural-networks` `dnn` `keras` `machine-learning` `ml` `neural-network` `onnx` `pytorch` `scikit-learn` `tensorflow` ## Description ONNX is an open-source standard for machine learning interoperability that provides a unified format for representing neural network models. By defining a common set of operators and a standardized file structure, it enables models to be shared, exported, and executed consistently across different training frameworks and software ecosystems. The project functions as an intermediate representation layer that decouples model development from deployment. It utilizes a language-neutral binary serialization format to store model structures and weights, ensuring that computational graphs remain portable. This architecture allows developers to convert models between various frameworks and deploy them onto diverse hardware runtimes, including CPUs, GPUs, and specialized AI accelerators. Beyond standardization, the framework includes tools for graph-level optimization and visualization, allowing for the inspection of data flow and the application of performance enhancements. It also provides access to a centralized repository of pre-trained models, facilitating the integration of ready-to-use intelligence into production applications while supporting hardware-specific acceleration to improve inference efficiency. ## Tags ### Artificial Intelligence & ML - [Machine Learning Model Portability](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-management/machine-learning-model-portability.md) — Defines a common file format and set of operators for sharing and executing machine learning models across different frameworks. ([source](https://onnx.ai)) - [Model Interoperability Formats](https://awesome-repositories.com/f/artificial-intelligence-ml/model-interoperability-formats.md) — Provides a standardized format and tools to translate machine learning models between different training frameworks and deployment environments. ([source](https://onnx.ai/get-started.html)) - [Computational Graphs](https://awesome-repositories.com/f/artificial-intelligence-ml/computational-graphs.md) — Defines a standardized structure for portable computation graphs to ensure consistent execution across environments. - [Inference Execution Engines](https://awesome-repositories.com/f/artificial-intelligence-ml/inference-execution-engines.md) — Provides a standardized runtime for executing machine learning models on diverse hardware to simplify production deployment. ([source](https://onnx.ai/get-started.html)) - [Model Compatibility Layers](https://awesome-repositories.com/f/artificial-intelligence-ml/model-compatibility-layers.md) — Acts as a unified intermediate representation layer that decouples model training frameworks from deployment runtimes. - [Computational Graph Definitions](https://awesome-repositories.com/f/artificial-intelligence-ml/computational-graph-definitions.md) — Structures machine learning models as acyclic graphs of nodes and operators to guarantee consistent execution results. ([source](https://onnx.ai/about.html)) - [Hardware Acceleration Abstractions](https://awesome-repositories.com/f/artificial-intelligence-ml/hardware-acceleration-abstractions.md) — Maps high-level computational graphs to optimized low-level kernels for execution on CPUs, GPUs, and specialized AI accelerators. - [Inference Accelerators](https://awesome-repositories.com/f/artificial-intelligence-ml/inference-accelerators.md) — Utilizes specialized hardware runtimes to accelerate model inference and reduce latency during the prediction phase. ([source](https://onnx.ai/supported-tools.html)) - [Model Exporters](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-deployment-and-serving/serialization-and-export-formats/model-exporters.md) — Provides utilities to export machine learning models from various training frameworks into a unified, portable file format. ([source](https://onnx.ai/get-started.html)) - [Model Graph Optimizers](https://awesome-repositories.com/f/artificial-intelligence-ml/model-graph-optimizers.md) — Includes automated graph-level transformation techniques to improve memory efficiency and execution speed before runtime. - [Model Performance Optimizations](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/frameworks/training-systems/model-performance-optimizations.md) — Applies hardware-specific acceleration techniques to increase the speed and efficiency of model execution. ([source](https://onnx.ai)) - [Inference Optimization Tools](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-optimization-and-inference/serving-and-runtime/inference-optimization-utilities/inference-optimization-tools.md) — Applies hardware-specific acceleration techniques and specialized runtime libraries to improve inference speed and efficiency. - [Model Optimization Toolkits](https://awesome-repositories.com/f/artificial-intelligence-ml/model-optimization-toolkits.md) — Provides tools for translating machine learning models between frameworks and applying hardware-specific acceleration. - [Schema-Based Tool Definitions](https://awesome-repositories.com/f/artificial-intelligence-ml/agentic-systems-frameworks/model-integration-serving/model-integration-interfaces/schema-based-tool-definitions.md) — Defines a standardized set of mathematical operations as the functional interface between model definitions and hardware runtimes. - [Model Repositories](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/architectures/model-architecture-evaluation/model-repositories.md) — Maintains a centralized collection of pre-trained models for vision and language tasks to simplify integration. - [Pre-made Models](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-deployment-and-serving/model-hubs-and-pre-made-models/pre-made-models.md) — Offers a centralized repository of pre-trained models to accelerate the integration of ready-to-use intelligence into production applications. ([source](https://onnx.ai/get-started.html)) - [Computational Graph Visualizers](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-optimization-and-inference/computational-graph-visualizers.md) — Generates graphical representations of model structures to help developers inspect data flow and operations. ([source](https://onnx.ai/supported-tools.html)) ### Programming Languages & Runtimes - [Directed Acyclic Graph Execution Engines](https://awesome-repositories.com/f/programming-languages-runtimes/runtime-execution-environments/runtime-environments/runtimes/graph-symbolic-execution-engines/directed-acyclic-graph-execution-engines.md) — Provides a standardized runtime for executing machine learning models as directed acyclic graphs across diverse hardware backends. ### Data & Databases - [Protocol Buffers](https://awesome-repositories.com/f/data-databases/data-serialization-formats/binary-serialization-protocols/protocol-buffers.md) — Utilizes a language-neutral binary serialization format to store model structures and weights for cross-platform interoperability.