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

# Versioning

For registered models, prediction function along with its requirements and resources can be logged to be used for further processes like evaluating and packaging.

## Prediction Class

SuperAlign expects the prediction function in a specific format:

```python theme={null}
from pureml import BasePredictor, Input, Output
import pureml

class Predictor(BasePredictor):
    label = "<model_name:model_version>"
    input = Input(type="<Input data type>")
    output = Output(type="<Output data type>")

    def load_models(self):
        self.model = #Load the model to this variable

    def predict(self, data):
        predictions = # Code to obtain model predictions

        return predictions
```

<Info>
  This is a template for writing Predictor class. SuperAlign expects this format for
  the class to use for evaluation, packaging of models etc.
</Info>

Let's look at the Structure of the `Predictor` class.

`Predictor` inherits the `BasePredictor` which contains variables and abstract methods that have to be overwritten by the user.

<Info>The class name should be `Predictor`.</Info>

### Variables

`label` : Contains the label for the model version

`input` : Contains the input data type that is passed into `predict` function

`output`: Contains the data type of the output returned by the `predict` function

### Functions

`load_models`: Contains the functionality to load the prediction model

`predict`: Contains the functionality to obtain the predictions on the `data` passed to the function.

<Info>
  Apart from the above-mentioned variables, and functions, user can add more
  functionality to the `Predictor` class
</Info>

## Logging Prediction

```python theme={null}
import pureml

pureml.predict.add(label='<model_name:model_version>',
                   paths={'predict': './predict.py'})
```

Logging Prediction function along with its requirements:

```python theme={null}
import pureml

pureml.predict.add(label='<model_name:model_version>',
                   paths={'predict': './predict.py', 'requirements': './requirements.txt', 'resources': './'})
```

## Fetching the model

```python theme={null}
import pureml

pureml.predict.fetch(label='<model_name:model_version>')
```