ONNX Tutorial
ONNX Tutorial
ONNX (Open Neural Network Exchange) is an open-source format designed to represent machine learning models, allowing them to be transferred seamlessly between different frameworks. By providing a standardized format, ONNX allows developers to use a wide range of tools and libraries, optimizing workflows and enhancing model interoperability.
Our ONNX tutorial helps you learn ONNX from understanding its core concepts to converting models between popular frameworks such as TensorFlow, PyTorch, and scikit-learn.
ONNX, originally developed by the PyTorch team at Facebook under the name Toffee, was re-branded and announced as ONNX in September 2017 by Facebook and Microsoft.
Why to Learn ONNX?
ONNX addresses a major challenge in the deep learning and machine learning domains especially in the fragmentation of tools, frameworks, and run-times. AI developers often find themselves locked into specific ecosystems like TensorFlow, PyTorch, or others, ONNX provides a solution by allowing models to be shared across different platforms without needing to be retrained or heavily modified.
ONNX defines a common set of operators, the building blocks of machine learning and deep learning models, and a unified file format. This standardization allows models to be trained in one framework and then easily used or deployed in another, enhancing the flexibility of AI development.
ONNX Runtime and Applications
ONNX is not just about interoperability between different frameworks, it also includes the ONNX Runtime, a high-performance engine that optimizes and executes ONNX models across various hardware platforms. Whether it's deploying models on powerful GPUs for large-scale inference or on smaller edge devices like the Jetson Nano, ONNX Runtime ensures that the models run efficiently.
What are the advantages of ONNX?
Following are the major advantages of ONNX −
Interoperability: With ONNX, models can be trained in one framework and then used in another, which enhances the flexibility in model development and deployment.
Platform Independence: ONNX includes a high-performance ecosystem that can optimize and execute models across various hardware platforms. This ensures that models run efficiently, regardless of the deployment environment.
Pre-trained Models: ONNX provides a wide range of models that are pre-trained on large datasets, saving time and computational resources.
Operators: ONNX provides a common set of operators to map operations from various frameworks (like TensorFlow, PyTorch, etc.) into a standardized ONNX format.
Community Support: ONNX is managed by a strong community of developers and major tech companies, ensuring continuous development and innovation.
Regular Updates: ONNX is managed by a large community of developers and major tech companies, it is regularly updated to include new features and improvements.
What are the Design Principles of ONNX?
Following are the Key design principles of ONNX −
ONNX is designed to support both deep learning models and traditional machine learning algorithms.
ONNX is adaptable to rapid technological advances.
ONNX provides a compact and cross-platform representation for model serialization.
ONNX uses a standardized list of well-defined operators informed by real-world usage.
ONNX File Format
The ONNX file format is a flexible way to represent machine learning models from different frameworks. An ONNX file contains following components −
Model
Version info
Metadata
Acyclic computation data-flow graph
Graph
Inputs and outputs
List of computation nodes
Graph name
Computation Node
Inputs
Outputs
Operator
Operator parameters
Who Should Learn ONNX
This ONNX tutorial will help both students as well as working professionals who want to develop applications into different platforms.
Machine Learning Engineers and Data Scientists: Those who are familiar with building, training, and deploying machine learning models and are looking to enhance model portability and framework interoperability.
AI Developers: Developers who want to optimize their machine learning models for different environments or integrate them into various platforms.
Researchers: Individuals exploring new frameworks and tools for machine learning, who need a format that supports seamless model sharing and deployment.
Prerequisites to Learn ONNX
Before diving into this tutorial, it's recommended that you have a basic understanding of machine learning concepts, including familiarity with models, layers, and training processes, which will make it easier to grasp the ONNX model structure.
Experience with at least one popular machine learning framework, such as TensorFlow, PyTorch, or scikit-learn, is beneficial, especially when converting models to and from ONNX.
Additionally, basic Python programming skills are essential, as ONNX is often used with Python-based tools and libraries. While not mandatory, familiarity with deep learning architectures and techniques is recommended for dealing with more complex models in ONNX.
Frequently Asked Questions about ONNX
There are some very Frequently Asked Questions(FAQ) about ONNX, this section tries to answer them briefly.
ONNX also called as, Open Neural Network Exchange is an open-source format designed to represent machine learning models. It provides a standard format for machine learning models from different fram works like TensorFlow, PyTorch, scikit-learn, Keras, Chainer and more. Once the models are in the ONNX format, then it helps you to run machine learning models faster and more efficiently in various platforms and devices.
ONNX operators are the fundamentals blocks that define computations in a machine learning model, mapping operations from various frameworks (like TensorFlow, PyTorch, etc.) into a standardized ONNX format. Each operator defines a specific type of operation, such as mathematical computations, data processing, or neural network layers.
And an operator is identified by <name, domain, version>
The ONNX Model Zoo is a repository that contains a collection of pre-trained models that are available for download and inference. These models are trained on large datasets and are provided in ONNX format, allowing you to use them across different frameworks and platforms without worrying about model conversion or compatibility.
ONNX Runtime, is a high-performance engine designed to efficiently run ONNX models. It is a tool that helps run machine learning models faster and more efficiently. It works on different platforms like Windows, Mac, and Linux, and can use various types of hardware, such as CPUs and GPUs, to speed up the models. ONNX Runtime supports models from popular frameworks like PyTorch, TensorFlow, and scikit-learn, making it easy to move models between different environments.
Train your model using any of the popular framework.
Convert the trained model into the ONNX format using the converting libraries.
Then load and run the ONNX model in the ONNX Runtime for optimizing the performance.
A converting library is a tool that translates a machine learning model's logic from its original framework like TensorFlow or scikit-learn into the ONNX format.
For different machine learning frameworks like TensorFlow, scikit-learn, and Pytorch require different converting libraries. Here are some popular converting libraries in ONNX −
Sklearn-onnx
tensorflow-onnx
onnxmltools
torch.onnx