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guide: simplify Checkpoints intro and
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include details about checkpoint tracking (from exp run ref)
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jorgeorpinel committed Jan 13, 2022
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_New in DVC 2.0_

To track successive steps in a longer experiment, you can register checkpoints
from your code at runtime. This is especially helpful in machine learning, for
example to track the progress in deep learning techniques such as evolving
neural networks.

_Checkpoint experiments_ track a series of variations (the checkpoints) and
their execution can be stopped and resumed as needed. You interact with them
using the `--rev` and `--reset` options of `dvc exp run` (see also the
`checkpoint` field in `dvc.yaml` `outs`). They can help you
To track successive steps in a longer machine learning experiment, you can
register checkpoints from your code at runtime, for example to track the
progress with deep learning techniques. They can help you

- implement the best practice in deep learning to save your model weights as
checkpoints.
- track all code and data changes corresponding to the checkpoints.
- see when metrics start diverging and revert to the optimal checkpoint.
- automate the process of tracking every training epoch.

> Experiments and checkpoints are [implemented](/blog/experiment-refs) with
> hidden Git experiment commits branches.
Checkpoint [execution] can be stopped and resumed as needed. You interact with
them using the `--rev` and `--reset` options of `dvc exp run` (see also the
`checkpoint` field in `dvc.yaml` `outs`).

[execution]:
/doc/user-guide/experiment-management/running-experiments#checkpoint-experiments

<details>

### ⚙️ How are checkpoints captured?

Instead of a single reference like [regular experiments], checkpoint experiments
have multiple commits under the custom Git reference (in `.git/refs/exps`),
similar to a branch.

[regular experiments]:
/doc/user-guide/experiment-management/experiments-overview

</details>

Like with regular experiments, checkpoints can become persistent by
[committing them to Git](#committing-checkpoints-to-git).
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