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add an expanding branch navigator (#8)
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Co-authored-by: Jorge Orpinel <[email protected]>
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2 people authored and casperdcl committed Aug 27, 2021
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# dvc-checkpoints-mnist

This example DVC project uses
[checkpoints](https://dvc.org/doc/api-reference/make_checkpoint) to iteratively
train a model. The model is a simple convolutional neural network (CNN)
This example DVC project demonstrates the different ways to employ
[Checkpoint Experiments](https://dvc.org/doc/user-guide/experiment-management#checkpoints-in-source-code)
with DVC.

This scenario uses [DVCLive](https://dvc.org/doc/dvclive) to generate
[checkpoints](https://dvc.org/doc/api-reference/make_checkpoint) for iterative
model training. The model is a simple convolutional neural network (CNN)
classifier trained on the [MNIST](http://yann.lecun.com/exdb/mnist/) data of
handwritten digits to predict the digit (0-9) in each image.

<details>

<summary>🔄 Switch between scenarios</summary>
<br/>

This repo has several
[branches](https://github.com/iterative/dvc-checkpoints-mnist/branches) to this
that show different methods for using checkpoints on a similar pipeline:

- The [live](https://github.com/iterative/dvc-checkpoints-mnist/edit/live)
scenario introduces full-featured checkpoint usage — integrating with
[DVCLive](https://github.com/iterative/dvclive).
- The [basic](https://github.com/iterative/dvc-checkpoints-mnist/tree/basic)
scenario uses single-checkpoint experiments to illustrate how checkpoints work
in a simple way.
- The
[Python-only](https://github.com/iterative/dvc-checkpoints-mnist/tree/make_checkpoint)
variation features the
[make_checkpoint](https://dvc.org/doc/api-reference/make_checkpoint) function
from DVC's Python API.
- Contrastingly, the
[signal file](https://github.com/iterative/dvc-checkpoints-mnist/tree/signal_file)
scenario shows how to make your own signal files (applicable to any
programming language).
- Finally, our
[full pipeline](https://github.com/iterative/dvc-checkpoints-mnist/tree/full_pipeline)
scenario elaborates on the full-featured usage with a more advanced process.

</details>

## Setup

To try it out for yourself:
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