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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.

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Examples

Creating basic deployment parameters: 
basic_params = DeploymentParams()
Creating deployment with custom resources: 
custom_params = DeploymentParams(
    artifact_type=ArtifactType.PYTHON_PACKAGE,
    deployment_type=DeploymentType.BASE_CONTAINER,
    replicas=3,
    cpu=2,
    memory=4096
)
Creating deployment with custom container: 
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
)

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artifact_type

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deployment_type

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image_uri

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replicas

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cpu

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memory

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model_config

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