# Design your own model on AMD Platforms

In this page, we will focus on designing, training and validating your own
model on AMD CPUs and GPUs.
We will show you how to use PyTorch to define and train your model.

Technologies covered: CPU, GPU.

## Requisites


```{card}
GPUs: {bdg-primary}`AMD Instinct™ Accelerators`, {bdg-primary}`AMD Radeon™ RX Graphics Cards` or {bdg-primary}`AMD Radeon™ PRO Graphics Cards`
```

[Setup environment](env/env.md).

## Examples

```{tableofcontents}
```

## Resources

- [ResNet for image classification using AMD GPUs](https://rocm.blogs.amd.com/artificial-intelligence/resnet/README.html)
- [Question-answering Chatbot with LangChain on an AMD GPU](https://rocm.blogs.amd.com/artificial-intelligence/langchain-chatbot/README.html)
- [Building a decoder transformer model on AMD GPU(s)](https://rocm.blogs.amd.com/artificial-intelligence/decoder-transformer/README.html)
- [Simplifying deep learning: A guide to PyTorch Lightning](https://rocm.blogs.amd.com/artificial-intelligence/pytorch-lightning/README.html)
- [Training a Neural Collaborative Filtering (NCF) Recommender on an AMD GPU](https://rocm.blogs.amd.com/artificial-intelligence/ncf/README.html)
- [ResNet for image classification using AMD GPUs](https://rocm.blogs.amd.com/artificial-intelligence/resnet/README.html)

If you would like to suggest a resource, email us aup@amd.com

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