Onnx

ONNX (Open Neural Network Exchange) is an open-source format that enables interoperability between different deep learning frameworks, allowing users to transfer trained machine learning models between frameworks seamlessly. The main goal of ONNX is to provide a common intermediate representation for deep learning models, making it easier to develop and deploy models across various platforms.

The idea behind ONNX is to allow models to be trained in one deep learning framework and then transferred and used in another framework seamlessly, without the need to rewrite or retrain the model. This is achieved by defining a standard format that captures the model architecture, weights, and parameters in a consistent manner.

Key features of ONNX include:

1. Interoperability: ONNX supports interoperability between various popular deep learning frameworks, including Tensorflow, PyTorch, Keras, Caffe, and more. This allows models trained in one framework to be easily used in another framework for inference.
2. Model Portability: By exporting models to the ONNX format, users can easily transfer models between different devices and platforms, such as CPUs, GPUs, and edge devices. This portability is especially valuable for deploying models on resource-constrained edge devices.
3. Performance: ONNX is designed to be efficient, allowing models to be executed with high performance on different hardware and software environments. The ONNX Runtime, a high-performance engine developed by Microsoft, further enhances the inference speed of ONNX models.
4. Extensibility: ONNX is designed to be extensible, enabling support for new operators and operations as deep learning frameworks evolve and new research breakthroughs emerge.

ONNX Workflow

The typical workflow with ONNX involves training a machine learning model in one framework (e.g., PyTorch, Tensorflow) and then converting or exporting the model to the ONNX format. Once in the ONNX format, the model can be loaded into an ONNX-compliant runtime environment (e.g., ONNX Runtime) for inference or deployment on various platforms.

1. Install Required Libraries: pip install onnx
2. Choose a Deep Learning Framework: e.g. Tensorflow, PyTorch, and Keras.
3. Train or Load a pre-trained Model: You can either train a model from scratch using your preferred framework or load a pre-trained model that supports ONNX export.
4. Export the Model to ONNX Format: Export your trained model to the ONNX format using the appropriate function provided by the deep learning framework.

For PyTorch:

• import torch
• import torchvision.models as models
• import torch.onnx
• # Load a pre-trained model
• # Set the model to evaluation mode
• # Export the model to ONNX format

For Tensorflow/Keras:

• import tensorflow as tf
• # Load a pre-trained model
• # Export the model to ONNX format

5. Load and Run the ONNX Model:

• import onnxruntime as ort
• # Load the ONNX model
• # Run the inference

6. Deploy and Integrate the ONNX Model: Deploy your exported and validated ONNX model on your desired target platform (e.g., edge devices, cloud servers) and integrate it into your application for inference.

Additionally, consider using the ONNX Runtime for optimized execution of ONNX models.