# Customizing a TensorFlow operation Implement a custom operation that uses Metal kernels to accelerate neural-network training performance. ## Discussion> Note: This sample code project is associated with WWDC22 session [10063: Accelerate machine learning with Metal](https://developer.apple.com/wwdc22/10063/). ### Configure the sample code 1. Follow the instructions in [Getting started with tensorflow-metal](https://developer.apple.com/metal/tensorflow-plugin/). 2. Install ffmpeg using `brew`. ```shell brew install ffmpeg ``` 1. Install the required Python packages. ```shell pip install -r requirements.txt ``` 1. Use `make` to build the custom operation with Xcode. ```shell cd hash_encoder make cd .. ``` 1. Run the sample. ```shell python tiny_nerf_hash.py ``` 1. View the resutls in the `result_nerf_hash` folder. - To compare the performance benefits provided by this sample, you can run the original NeRF sample code included with the project. View the resutls in the `result_nerf_mlp` folder. ```shell python tiny_nerf_mlp.py ``` > Note: The sample uses low-resolution (100x100) images by default. You can alternatively use a high-resolution version of the data to produce a clearer rendering. --- Copyright © 2026 Apple Inc. All rights reserved. | [Terms of Use](https://www.apple.com/legal/internet-services/terms/site.html) | [Privacy Policy](https://www.apple.com/privacy/privacy-policy)