> 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/capture-replayable-params.md).

# Capture Replayable Params

Use `instantiate_with_params()` when a run needs a durable parameter record.

## Capture Params

{% code overflow="wrap" %}

```python
from hypster import HP, instantiate, instantiate_with_params
from my_app.reporting import ReportRequest

def report_config(hp: HP) -> ReportRequest:
    audience = hp.select(["exec", "technical"], name="audience", default="technical", options_only=True)
    include_appendix = hp.bool(True, name="include_appendix")
    max_pages = hp.int(12, name="max_pages", min=1, max=100)
    return ReportRequest(audience=audience, include_appendix=include_appendix, max_pages=max_pages)

run = instantiate_with_params(
    report_config,
    values={"audience": "exec", "max_pages": 6},
)

assert run.value.audience == "exec"
assert run.value.include_appendix is True
assert run.value.max_pages == 6
assert run.params == {
    "audience": "exec",
    "include_appendix": True,
    "max_pages": 6,
}
```

{% endcode %}

## Replay Later

{% code overflow="wrap" %}

```python
replayed = instantiate(report_config, values=run.params)
assert replayed.max_pages == run.value.max_pages
```

{% endcode %}

Captured params include defaults, so replay does not silently pick up later default changes:

{% code overflow="wrap" %}

```python
def old_config(hp: HP) -> int:
    return hp.int(64, name="batch_size")


old_run = instantiate_with_params(old_config)
assert old_run.params == {"batch_size": 64}


def new_config(hp: HP) -> int:
    return hp.int(128, name="batch_size")


assert instantiate(new_config, values=old_run.params) == 64
```

{% endcode %}

## Store Params As JSON

`run.params` only contains values selected by `hp.*` calls. It is intended to be JSON-friendly when your parameter values are JSON-friendly.

{% code overflow="wrap" %}

```python
import json

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

assert instantiate(report_config, values=restored_params) == run.value
```

{% endcode %}

## Complex Runtime Objects

Use dict-backed `select` when a runtime choice is a complex object. The params record the simple key, not the complex mapped value.

{% code overflow="wrap" %}

```python
from my_app.models import LargeMLP, SmallMLP

def small_model(hp: HP) -> SmallMLP:
    return SmallMLP(layers=2, units=[64, 32])

def large_model(hp: HP) -> LargeMLP:
    return LargeMLP(layers=4, units=[256, 128])

model_options = {
    "small": small_model,
    "large": large_model,
}

def model_config(hp: HP):
    selected_config = hp.select(model_options, name="model", default="small", options_only=True)
    return hp.nest(selected_config, name="settings")

run = instantiate_with_params(model_config, values={"model": "large"})

assert isinstance(run.value, LargeMLP)
assert run.params == {"model": "large"}
```

{% endcode %}


---

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