Sentis overview

Sentis is a neural network inference library for Unity. The package is in a "pre-release" state in an open beta program available to all Unity users via the package manager. The features and documentation may change before it is verified for release near the end of 2024. If you would like to release a commercial project with Sentis before it is released, please reach out to bill.cullen@unity3d.com.

For additional information, updates from our team, and collaboration opportunities with other beta users, visit the Unity Discussions Sentis topic. If you have an interesting project that you would like to collaborate on with Unity, complete and submit this form and we'll be excited to speak with you.

You can use Sentis to import trained neural network models into Unity, and then run them in real-time locally on any runtime platform that Unity supports. Models can be deployed on either the GPU or the CPU.

To use Sentis, it helps if you have some experience in using machine learning models, for example in a framework like TensorFlow or PyTorch.

Section Description
Get started Install Sentis, find and use sample projects, and understand the Sentis workflow.
Create a model Create a runtime model by importing an ONNX model file or using the Sentis model API.
Run a model Create input data for a model, create an engine to run the model, and get output.
Use Tensors Get, set and modify input and output data.
Profile a model Use Unity tools to profile the speed and performance of a model.

Supported platforms

Sentis supports all the platforms Unity supports.

How long a model takes to run depends on the complexity of the model, the platform, and the engine type you use. Refer to Models and Create an engine for more information.

Supported model types

Sentis supports most models in Open Neural Network Exchange (ONNX) format with an opset version between 7 and 15. Refer to Supported models and Supported ONNX operators for more information.

Places to find pre-trained models

There are various sources to find pre-trained models, which may either be available in the ONNX format or in a format that you can convert. Examples include:

If you want to train your own models, refer to the following links:

Additional resources