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

# DeploymentParams

> Configuration parameters for deploying a model in the Fiddler platform.

Configuration parameters for deploying a model in the Fiddler platform.

DeploymentParams defines the deployment configuration for a model, including
the artifact type, deployment environment, resource allocation, and container
specifications. These parameters control how the model is packaged, deployed,
and scaled within the Fiddler infrastructure.

This class is used when deploying models to specify the runtime environment,
resource requirements, and deployment strategy that best fits your model's
needs and performance requirements.

## Examples

Creating basic deployment parameters:

```python theme={null}
basic_params = DeploymentParams()
```

Creating deployment with custom resources:

```python theme={null}
custom_params = DeploymentParams(
    artifact_type=ArtifactType.PYTHON_PACKAGE,
    deployment_type=DeploymentType.BASE_CONTAINER,
    replicas=3,
    cpu=2,
    memory=4096
)
```

Creating deployment with custom container:

```python theme={null}
container_params = DeploymentParams(
    artifact_type=ArtifactType.DOCKER_IMAGE,
    deployment_type=DeploymentType.CUSTOM_CONTAINER,
    image_uri="my-registry.com/my-model:v1.0",
    replicas=2,
    cpu=4,
    memory=8192
)
```

## artifact\_type

## deployment\_type

## image\_uri

## replicas

## cpu

## memory

## model\_config

Configuration for the model, should be a dictionary conforming to \[ConfigDict]\[pydantic.config.ConfigDict].