> ## Documentation Index
> Fetch the complete documentation index at: https://docs.adaptive-ml.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Load Graders

> Load existing Graders within your recipe

You can load [Graders](/v0.12/core/graders) that have been previously created in the platform automatically using the `Grader` class from `adaptive_harmony.parameters` (excluding Recipe Graders, which only exist as code within your custom recipe).

## Include Grader in InputConfig

In order to do this, you must only make sure that one or more `Grader` parameters can be passed to your recipe as input:

```python theme={null}
from pydantic import Field
from typing import Annotated, Set

from adaptive_harmony.runtime import InputConfig
from adaptive_harmony.parameters import Grader

class GraderRecipeConfig(InputConfig):
    graders: Annotated[
        Set[Grader],
        Field(
            description="The graders to load.",
            title="Graders",
        ),
    ]
```

The `Grader` object will contain all the configuration necessary to instantiate a grader in code, with the exact same properties you've defined in the Adaptive platform.

## Load with Grader.load()

You can then automatically load and setup your graders with `await grader.load(ctx)`:

```python theme={null}
from adaptive_harmony.runtime import RecipeContext, recipe_main

@recipe_main
async def main(config: GraderRecipeConfig, ctx: RecipeContext):
    graders = []
    for grader_param in config.graders:
        grader = await grader_param.load(ctx)
        graders.append(grader)
```

Now the Graders are ready to use for training, evaluation, or anything else you find use for!