onnx/onnx

Features

• Machine Learning Model Portability - Defines a common file format and set of operators for sharing and executing machine learning models across different frameworks.
• Model Interoperability Formats - Provides a standardized format and tools to translate machine learning models between different training frameworks and deployment environments.
• Directed Acyclic Graph Execution Engines - Provides a standardized runtime for executing machine learning models as directed acyclic graphs across diverse hardware backends.
• Computational Graphs - Defines a standardized structure for portable computation graphs to ensure consistent execution across environments.
• Inference Execution Engines - Provides a standardized runtime for executing machine learning models on diverse hardware to simplify production deployment.
• Model Compatibility Layers - Acts as a unified intermediate representation layer that decouples model training frameworks from deployment runtimes.
• Computational Graph Definitions - Structures machine learning models as acyclic graphs of nodes and operators to guarantee consistent execution results.
• Hardware Acceleration Abstractions - Maps high-level computational graphs to optimized low-level kernels for execution on CPUs, GPUs, and specialized AI accelerators.
• Inference Accelerators - Utilizes specialized hardware runtimes to accelerate model inference and reduce latency during the prediction phase.
• Model Exporters - Provides utilities to export machine learning models from various training frameworks into a unified, portable file format.
• Model Graph Optimizers - Includes automated graph-level transformation techniques to improve memory efficiency and execution speed before runtime.
• Model Performance Optimizations - Applies hardware-specific acceleration techniques to increase the speed and efficiency of model execution.
• Inference Optimization Tools - Applies hardware-specific acceleration techniques and specialized runtime libraries to improve inference speed and efficiency.
• Model Optimization Toolkits - Provides tools for translating machine learning models between frameworks and applying hardware-specific acceleration.
• Machine Learning - Standard for machine learning interoperability.
• Model Optimization - Open format for model interoperability across frameworks.
• Core Development Tools - The core specification and library for the open neural network exchange.
• Model Conversion - Standardizes the exchange and representation of neural network models.
• Schema-Based Tool Definitions - Defines a standardized set of mathematical operations as the functional interface between model definitions and hardware runtimes.
• Model Repositories - Maintains a centralized collection of pre-trained models for vision and language tasks to simplify integration.
• Pre-made Models - Offers a centralized repository of pre-trained models to accelerate the integration of ready-to-use intelligence into production applications.
• Computational Graph Visualizers - Generates graphical representations of model structures to help developers inspect data flow and operations.
• Protocol Buffers - Utilizes a language-neutral binary serialization format to store model structures and weights for cross-platform interoperability.

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