> For the complete documentation index, see [llms.txt](https://hypergan.gitbook.io/hypergan/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://hypergan.gitbook.io/hypergan/tutorials/training.md).

# Training a GAN

Thanks for trying out hypergan. This tutorial will walk you through creating your first model, exploring it, and building/deploying the generator.

## Acquiring a dataset

You can acquire a dataset by having a folder full of loose images.

## Getting started

First install hypergan.

## Testing install

To test hypergan is installed correctly, run `hypergan new . -l`

You can view video card usage with `nvidia-smi`

## Training hypergan

You can run:

```
  hypergan train /path/to/data -c myconfig  -s 256x256x3 -format png --sampler static_batch --sample_every 10
```

Once hypergan is working, you can sample your

## Sampling your model

You've created an infinite manifold. Great! With any luck it's diverse. You can see how diverse your model is by using:

```
  hypergan sample /path/to/data -c myconfig -s 256x256x3 -format png --sampler batch_walk
```

## Building your model

```
  hypergan build /path/to/data -c myconfig -s 256x256x3 -format png --sampler batch --sample_every 10
```

## Deploying your model

You've made a great model you'd like to share. Awesome! You can deploy it to users through a few methods.

## TODO


---

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