> For the complete documentation index, see [llms.txt](https://disdat.gitbook.io/disdat-documentation/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://disdat.gitbook.io/disdat-documentation/examples/untitled-1.md).

# Examples

## This example illustrates:

* How to setup a Disdat project to use TensorFlow (require tensorflow in your setup.py)
* How to assemble Disdat tasks into a pipeline
* How task outputs become bundles
* How to include extra data in your Docker container

## Setup

Like the Tutorial, let's clone our examples repo: 

1. Clone our examples github repo (<https://github.com/seanr15/disdat-examples>) [here.](https://github.com/seanr15/disdat-examples)
2. We'll assume you've installed it in `$CODE`
3. `cd` into the `$CODE`.&#x20;

Note that it has its own setup.py, defining the requirements for our example pipelines (in the `pipelines` package).  Let's install those requirements into your virtual environment.

```
$ cd $CODE
$ pip install -e .
```

We assume you have made an `examples` context and have switched into it. If not:&#x20;

```
$ dsdt context examples
$ dsdt switch examples
```

## Optional

If you want to push/pull your MNIST data to/from S3, then we assume you have bound the local context  `examples` to your remote ([described here](/disdat-documentation/examples/short-test-drive/push-pull-using-s3.md)).   So if you list your contexts, you should see that you are in `examples` and its bound to a directory `s3://disdat-prod/` on AWS.&#x20;

```
$dsdt context
*	examples	[examples@s3://disdat-prod/context]
```

## Now on to [TF and MNIST](/disdat-documentation/examples/untitled-1/mnist-and-tensorflow.md)