Registers a model with Fiddler and uploads a model artifact to be used for explainability and fairness capabilities.

For more information, see Uploading a Model Artifact.

Input Parameter

Type

Default

Description

project_id

str

None

The unique identifier for the project.

model_id

str

None

A unique identifier for the model.

artifact_path

pathlib.Path

None

A path to the directory containing all of the model files needed to run the model.

deployment_type

Optional [str]

'predictor'

The type of deployment for the model. Can be one of

  • 'predictor' — Just a predict endpoint is exposed.
  • 'executor' — The model's internals are exposed.

image_uri

Optional [str]

None

A URI of the form '/:'. If specified, the image will be used to create a new runtime to serve the model.

namespace

Optional [str]

'default'

The Kubernetes namespace to use for the newly created runtime. image_uri must be specified.

port

Optional [int]

5100

The port to use for the newly created runtime. image_uri must be specified.

replicas

Optional [int]

1

The number of replicas running the model. image_uri must be specified.

cpus

Optional [int]

0.25

The number of CPU cores reserved per replica. image_uri must be specified.

memory

Optional [str]

'128m'

The amount of memory reserved per replica. image_uri must be specified.

gpus

Optional [int]

0

The number of GPU cores reserved per replica. image_uri must be specified.

await_deployment

Optional [bool]

True

If True, will block until deployment completes.

import pathlib

PROJECT_ID = 'example_project'
MODEL_ID = 'example_model'

artifact_path = pathlib.Path('model_dir')

client.upload_model_package(
    artifact_path=artifact_path,
    project_id=PROJECT_ID,
    model_id=MODEL_ID
)