> 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/examples/examples.md).

# Examples Overview

Use these examples as copyable shapes for real projects. Each page focuses on one workflow and only uses public Hypster APIs.

Hypster config functions are plain Python functions:

{% code overflow="wrap" %}

```python
from pathlib import Path

from hypster import HP

def config(hp: HP) -> Path:
    mode = hp.select(["fast", "accurate"], name="mode", default="fast")
    return Path("runs") / mode
```

{% endcode %}

The same function can serve several jobs:

* `explore(config)` prints the active parameter tree.
* `explore(config, return_schema=True)` returns a schema object for tools and UIs.
* `instantiate(config, values={...})` returns the runtime object.
* `instantiate_with_params(config, values={...})` returns the runtime object plus replayable selected params.
* `suggest_values(trial, config)` lets Optuna choose values for the reachable parameters.

## Example Map

| Page                                                              | Use it when you need                                                                           |
| ----------------------------------------------------------------- | ---------------------------------------------------------------------------------------------- |
| [Machine Learning](/hypster/examples/machine-learning.md)         | Model-family branches, numeric bounds, nullable values, and HPO-ready configs.                 |
| [Data Processing](/hypster/examples/data-processing.md)           | Ingestion, cleaning, feature flags, and export options in one pipeline config.                 |
| [AI Workflows](/hypster/examples/ai-workflows.md)                 | Provider selection, RAG knobs, prompt settings, and dict-backed complex choices.               |
| [Nested Workflows](/hypster/examples/nested-workflows.md)         | Reusable child configs, conditional nesting, deep value paths, and branch exploration.         |
| [Interactive UI From Schema](/hypster/examples/interactive-ui.md) | Generate form state from `explore(..., return_schema=True)` and feed it back to `instantiate`. |
| [Experiment Tracking](/hypster/examples/experiment-tracking.md)   | Capture selected params for logs, cards, and replay.                                           |

## A Small End-to-End Pattern

{% code overflow="wrap" %}

```python
from hypster import HP, explore, instantiate_with_params
from my_app.processing import PipelineRun

def pipeline_config(hp: HP) -> PipelineRun:
    stage = hp.select(["debug", "full"], name="stage", default="debug")

    if stage == "full":
        sample_rows = hp.int(1_000_000, name="sample_rows", min=100, max=10_000_000)
    else:
        sample_rows = hp.int(1000, name="sample_rows", min=100, max=1_000_000)

    return PipelineRun(stage=stage, sample_rows=sample_rows)

explore(pipeline_config)

run = instantiate_with_params(
    pipeline_config,
    values={"stage": "full", "sample_rows": 250_000},
)

assert run.value.stage == "full"
assert run.value.sample_rows == 250_000
assert run.params == {"stage": "full", "sample_rows": 250_000}
```

{% endcode %}

{% hint style="info" %}
Hypster raises when `values=` includes unknown parameters or parameters from a branch that was not reached. That default protects replayability. Use `explore(config, values=...)` to inspect the branch you are about to run.
{% endhint %}


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