ONNX - Runtime
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.
Optimizing Inference with ONNX Runtime
Inference is a process of using a trained model to make predictions by analyzing live data, which means making predictions or decisions based on a trained machine learning model. It powers many well-known Microsoft products, like Office and Azure, and is also used in many community projects.
ONNX Runtime is particularly good at speeding up this process by optimizing how the model runs. Following are the examples of how ONNX runtime is used −
- Graph Optimizations: ONNX Runtime improves the model by making changes to the structure of the computation graph, which is how the model processes data. This helps the model run more efficiently.
- Faster Predictions: ONNX Runtime can make your model predictions quicker by optimizing how the model runs.
- Run on Different Platforms: You can train your model in Python and then use it in apps written in C#, C++, or Java.
- Switch Between Frameworks: With ONNX Runtime, you can train your model in one framework and use it in another without much extra work.
- Execution Providers: ONNX Runtime can work with different types of hardware through its flexible Execution Providers (EP) framework. Execution Providers are specialized components within ONNX Runtime that allow the model to take full advantage of the specific hardware it's running on.
How ONNX Runtime Works
The process of using ONNX Runtime is straightforward and consists of three main steps −
- Get a Model: The first step is to get a machine learning model that has been trained using any framework that supports export or conversion to the ONNX format. Popular frameworks like PyTorch, TensorFlow, and scikit-learn offer tools for exporting models to ONNX.
- Load and Run the Model: Once you have the ONNX model, you can load it into ONNX Runtime and execute it. This step is straightforward, and there are tutorials available for running models in different programming languages such as Python, C#, and C++.
- Improve Performance(optional): ONNX Runtime allows for performance tuning using various runtime configurations and hardware accelerators.
Integration with Different Platforms
One of the biggest strengths of ONNX Runtime is its ability to integrate with a wide variety of platforms and environments. This flexibility makes it a valuable tool for developers who need to deploy models across different systems.
Running ONNX Runtime on Different Hardware
ONNX Runtime supports a broad range of hardware, from powerful servers with GPUs to smaller edge devices like the Nvidia, Jetson, Nano. This allows developers to deploy their models wherever they are needed, without worrying about compatibility issues.
Programming Language Support
ONNX Runtime provides APIs for several popular programming languages, making it easy to integrate into various applications −
- Python
- C++
- C#
- Java
- JavaScript
Cross-Platform Compatibility
ONNX Runtime is truly cross-platform, working seamlessly on Windows, Mac, and Linux operating systems. It also supports ARM devices, which are commonly used in mobile and embedded systems.
Example: Simple ONNX Runtime API Example
Here's a basic example of how to use ONNX Runtime in Python.
import onnxruntime
# Load the ONNX model
session = onnxruntime.InferenceSession("mymodel.onnx")
# Run the model with input data
results = session.run([], {"input": input_data})
In this example, the InferenceSession is used to load the ONNX model, and the run method is called to perform inference with the provided input data. The output is stored in the results variable, which can then be used for further processing or analysis.