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.

Note: The ML-Agents Toolkit only supports the models created with our trainers.

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.

Supported formats

There are currently two supported model formats:

• Barracuda (.nn) files use a proprietary format produced by the tensorflow_to_barracuda.py script.
• ONNX (.onnx) files use an industry-standard open format produced by the tf2onnx package.

Export to ONNX is currently considered beta. To enable it, make sure tf2onnx>=1.5.5 is installed in pip. tf2onnx does not currently support tensorflow 2.0.0 or later, or earlier than 1.12.0.

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.