ml-agents/docs/Unity-Inference-Engine.md

Unity Inference Engine

The ML-Agents Toolkit allows you to use pre-trained neural network models inside your Unity games. This support is possible thanks to the Unity Inference Engine (codenamed Barracuda). The Unity Inference Engine uses compute shaders to run the neural network within Unity.

Supported devices

See the Unity Inference Engine documentation for a list of the supported platforms.

Scripting Backends : The Unity Inference Engine is generally faster with IL2CPP than with Mono for Standalone builds. In the Editor, It is not possible to use the Unity Inference Engine with GPU device selected when Editor Graphics Emulation is set to OpenGL(ES) 3.0 or 2.0 emulation. Also there might be non-fatal build time errors when target platform includes Graphics API that does not support Unity Compute Shaders.

Using the Unity Inference Engine

When using a model, drag the model file into the Model field in the Inspector of the Agent. Select the Inference Device : CPU or GPU you want to use for Inference.

Note: For most of the models generated with the ML-Agents Toolkit, CPU will be faster than GPU. You should use the GPU only if you use the ResNet visual encoder or have a large number of agents with visual observations.

Unsupported use cases

Externally trained models

The ML-Agents Toolkit only supports the models created with our trainers. Model loading expects certain conventions for constants and tensor names. While it is possible to construct a model that follows these conventions, we don't provide any additional help for this. More details can be found in TensorNames.cs and BarracudaModelParamLoader.cs.

If you wish to run inference on an externally trained model, you should use Barracuda directly, instead of trying to run it through ML-Agents.

Model inference outside of Unity

We do not provide support for inference anywhere outside of Unity. The .onnx files produced by training use the open format ONNX; if you wish to convert a .onnx file to another format or run inference with them, refer to their documentation.