> For the complete documentation index, see [llms.txt](https://gilad-rubin.gitbook.io/hypster/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://gilad-rubin.gitbook.io/hypster/how-to-guides/create-a-replayable-config.md).

# Create a Replayable Config

Use this recipe when you want a small config that can be inspected, instantiated, logged, and replayed later.

The config is a normal Python function. Keep it cheap and side-effect-free so `explore()` can run it to inspect parameters and replay tools can execute it safely.

## 1. Define A Typed Config

{% code overflow="wrap" %}

```python
from hypster import HP
from my_app.data import CsvDataset


def data_config(hp: HP) -> CsvDataset:
    path = hp.text("data/train.csv", name="path")
    batch_size = hp.int(64, name="batch_size", min=1)
    shuffle = hp.bool(True, name="shuffle")
    return CsvDataset(path=path, batch_size=batch_size, shuffle=shuffle)
```

{% endcode %}

## 2. Inspect The Parameters

Print the active tree when you want a quick human check:

{% code overflow="wrap" %}

```python
from hypster import explore

explore(data_config)
```

{% endcode %}

Use structured metadata when another tool needs to render fields:

{% code overflow="wrap" %}

```python
schema = explore(data_config, return_schema=True)
fields = schema.to_dict()["parameters"]
```

{% endcode %}

## 3. Instantiate With Overrides

{% code overflow="wrap" %}

```python
from hypster import instantiate

dataset = instantiate(data_config, values={"batch_size": 128})

assert dataset.path == "data/train.csv"
assert dataset.batch_size == 128
assert dataset.shuffle is True
```

{% endcode %}

## 4. Capture Params For Replay

{% code overflow="wrap" %}

```python
from hypster import instantiate_with_params

run = instantiate_with_params(data_config, values={"batch_size": 128})

assert run.value.batch_size == dataset.batch_size
assert run.params == {
    "path": "data/train.csv",
    "batch_size": 128,
    "shuffle": True,
}
```

{% endcode %}

## 5. Store And Replay

{% code overflow="wrap" %}

```python
import json

payload = json.dumps(run.params, sort_keys=True)
restored_params = json.loads(payload)

replayed = instantiate(data_config, values=restored_params)
assert replayed.batch_size == run.value.batch_size
```

{% endcode %}

Because `run.params` includes defaulted values, replay does not silently change if the config's defaults are edited later.


---

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