When dealing with artificial intelligence, you’ll discover that there are a variety of frameworks for training models, runtimes for executing models, and maybe compilers to enhance the runtime of interactions, among other things. The hardware architectures the models may be deployed on can make a major impact when it comes to inference runtime optimization, including optimization of potentially highly expensive pre-processing.

Interoperability between these many tools is frequently required. For example, when training a model using algorithm A in framework X, the learned model may have greater prediction accuracy, execution runtime, and other “quality characteristics” than when training the same algorithm A in a different framework Y. The scenario may be the opposite way around with a different algorithm. The reason for this is that the algorithms’ low-level implementations change, or they employ slightly different algorithms that are referred to as the same. Furthermore, the development experience offered by framework Y might be far superior to that provided by framework X.

Nevertheless, when it comes to inference runtime deployment, you have two options: either deploy inference runtimes for the frameworks and plan to extend your overall runtime with extra frameworks in the future or utilize ONNX so that model can be executed by any interoperability standard runtime and any runtime implementation you can think of.

In terms of the predictability of runtime and inference execution runtime, the latter aspect is critical.

NVIDIA is a major participant in the high-performance computing industry. In addition, Intel AI provides significantly quicker inference in cases when the architecture lacks alternative hardware acceleration such as GPUs, compared to CPU-based acceleration. ONNX already has strong hardware acceleration support, and it will continue to improve in the future.

What is ONNX?

Open Neural Network Exchange Standard is a machine learning and deep learning model representation format.

TensorFlow, PyTorch, SAS, Matlab, Keras, and many others are examples of supporting frameworks. ONNX makes it easy to transfer models from one framework to another in this way. Furthermore, we can quickly deploy any model that has been stored in an ONNX format online using ONNX.js.

  • ONNX’s main goal is to bring all of the different AI frameworks together and make it as simple as possible for them to communicate with one another to build better models that can run on any platform or hardware.

ONNX model

It is quite simple to convert a model to the ONNX format. All we have to do now is make sure our trained model is in evaluation mode and build a simple dummy input with the same form as our trained model.

One of ONNX’s present limitations is that not all operations (particular neural network layers or custom loss functions, and so on) are supported by all frameworks.

The various operation set versions have been generated throughout the development of the ONNX library. As a result, we may choose which set of actions we wish to have available by default bypassing your favorite version as an argument to the export method.

Benefits of ONNX

The following are some of the advantages of having a model online:

  • Because data does not have to be transferred back and forth from the server, latency is reduced.
  • Because user data is not viewed remotely, not transferring data to a server increases privacy.

Scalability is not an issue because websites may be built entirely out of static files.

Tensorflow.js and ONNX.js are two strategies that may be used to deploy models online. One of the biggest benefits of Tensorflow.js is that it allows you to train models online. ONNX.js, on the other hand, is more efficient and hence quicker than Tensorflow.js when it comes to online inference.