# 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. The Unity Inference Engine is using [compute shaders](https://docs.unity3d.com/Manual/class-ComputeShader.html) to run the neural network within Unity. __Note__: The ML-Agents toolkit only supports the models created with our trainers. ## Supported devices 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__. The Unity Inference Engine supposedly works on any Unity supported platform but we only tested for the following platforms : * Linux 64 bits * Mac OS X 64 bits (`OpenGLCore` Graphics API is not supported) * Windows 64 bits * iOS * Android ## 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](https://onnx.ai/about.html) produced by the [tf2onnx package](https://github.com/onnx/tensorflow-onnx). 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.