# Model Settings

### Config

| Attribute              | Type    | Default                   |
| ---------------------- | ------- | ------------------------- |
| `extra`                | `str`   | `"ignore"`                |
| `env_prefix`           | `str`   | `"MLSERVER_MODEL_"`       |
| `env_file`             | `str`   | `".env"`                  |
| `protected_namespaces` | `tuple` | `('model_', 'settings_')` |

### Fields

| Field              | Type                        | Default     | Description                                                                                                                                                                                                                       |
| ------------------ | --------------------------- | ----------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `cache_enabled`    | `bool`                      | `False`     | Enable caching for a specific model. This parameter can be used to disable cache for a specific model, if the server-level caching is enabled. If the server-level caching is disabled, this parameter value will have no effect. |
| `implementation_`  | `str`                       | `-`         | *Python path* to the inference runtime to use to serve this model (e.g. `mlserver_sklearn.SKLearnModel`).                                                                                                                         |
| `inputs`           | `List[MetadataTensor]`      | `<factory>` | Metadata about the inputs accepted by the model.                                                                                                                                                                                  |
| `max_batch_size`   | `int`                       | `0`         | When adaptive batching is enabled, maximum number of requests to group together in a single batch.                                                                                                                                |
| `max_batch_time`   | `float`                     | `0.0`       | When adaptive batching is enabled, maximum amount of time (in seconds) to wait for enough requests to build a full batch.                                                                                                         |
| `name`             | `str`                       | `''`        | Name of the model.                                                                                                                                                                                                                |
| `outputs`          | `List[MetadataTensor]`      | `<factory>` | Metadata about the outputs returned by the model.                                                                                                                                                                                 |
| `parallel_workers` | `Optional[int]`             | `None`      | Use the `parallel_workers` field in the server-wide settings instead.                                                                                                                                                             |
| `parameters`       | `Optional[ModelParameters]` | `None`      | Extra parameters for each instance of this model.                                                                                                                                                                                 |
| `platform`         | `str`                       | `''`        | Framework used to train and serialise the model (e.g. sklearn).                                                                                                                                                                   |
| `versions`         | `List[str]`                 | `<factory>` | Versions of dependencies used to train the model (e.g. sklearn/0.20.1).                                                                                                                                                           |
| `warm_workers`     | `bool`                      | `False`     | Inference workers will now always be `warmed up` at start time.                                                                                                                                                                   |


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