Challenger

class datarobot.models.deployment.challenger.Challenger

A challenger is an alternative model being compared to the model currently deployed

Variables:

  • id (str) – The ID of the challenger.

  • deployment_id (str) – The ID of the deployment.

  • name (str) – The name of the challenger.

  • model (dict) – The model of the challenger.

  • model_package (dict) – The model package of the challenger.

  • prediction_environment (dict) – The prediction environment of the challenger.

classmethod create(deployment_id, model_package_id, prediction_environment_id, name, max_wait=600)

Create a challenger for a deployment

Parameters:

  • deployment_id (str) – The ID of the deployment

  • model_package_id (str) – The model package id of the challenger model

  • prediction_environment_id (str) – The prediction environment id of the challenger model

  • name (str) – The name of the challenger model

  • max_wait (Optional[int]) – The amount of seconds to wait for successful resolution of a challenger creation job.

Return type:

Challenger

Examples

from datarobot import Challenger
challenger = Challenger.create(
    deployment_id="5c939e08962d741e34f609f0",
    name="Elastic-Net Classifier",
    model_package_id="5c0a969859b00004ba52e41b",
    prediction_environment_id="60b012436635fc00909df555"
)
classmethod get(deployment_id, challenger_id)

Get a challenger for a deployment

Parameters:

  • deployment_id (str) – The ID of the deployment

  • challenger_id (str) – The ID of the challenger

Returns:

The challenger object

Return type:

Challenger

Examples

from datarobot import Challenger
challenger = Challenger.get(
    deployment_id="5c939e08962d741e34f609f0",
    challenger_id="5c939e08962d741e34f609f0"
)

challenger.id
>>>'5c939e08962d741e34f609f0'
challenger.model_package['name']
>>> 'Elastic-Net Classifier'
classmethod list(deployment_id)

List all challengers for a deployment

Parameters:

deployment_id (str) – The ID of the deployment

Returns:

challengers – A list of challenger objects

Return type:

list

Examples

from datarobot import Challenger
challengers = Challenger.list(deployment_id="5c939e08962d741e34f609f0")

challengers[0].id
>>>'5c939e08962d741e34f609f0'
challengers[0].model_package['name']
>>> 'Elastic-Net Classifier'
delete()

Delete a challenger for a deployment

Return type:

None

update(name=None, prediction_environment_id=None)

Update name and prediction environment of a challenger

Parameters:

  • name (Optional[str]) – The name of the challenger model

  • prediction_environment_id (Optional[str]) – The prediction environment id of the challenger model

Return type:

None

class datarobot.models.deployment.champion_model_package.ChampionModelPackage

Represents a champion model package.

Parameters:

  • id (str) – The ID of the registered model version.

  • registered_model_id (str) – The ID of the parent registered model.

  • registered_model_version (int) – The version of the registered model.

  • name (str) – The name of the registered model version.

  • model_id (str) – The ID of the model.

  • model_execution_type (str) – The type of model package (version). dedicated (native DataRobot models) and custom_inference_model` (user added inference models) both execute on DataRobot prediction servers, while external does not.

  • is_archived (bool) – Whether the model package (version) is permanently archived (cannot be used in deployment or replacement).

  • import_meta (ImportMeta) – Information from when this model package (version) was first saved.

  • source_meta (SourceMeta) – Meta information from where the model was generated.

  • model_kind (ModelKind) – Model attribute information.

  • target (Target) – Target information for the registered model version.

  • model_description (ModelDescription) – Model description information.

  • datasets (Dataset) – Dataset information for the registered model version.

  • timeseries (Timeseries) – Time series information for the registered model version.

  • bias_and_fairness (BiasAndFairness) – Bias and fairness information for the registered model version.

  • is_deprecated (bool) – Whether the model package (version) is deprecated (cannot be used in deployment or replacement).

  • build_status (str or None) – Model package (version) build status. One of complete, inProgress, failed.

  • user_provided_id (str or None) – User provided ID for the registered model version.

  • updated_at (str or None) – The time the registered model version was last updated.

  • updated_by (UserMetadata or None) – The user who last updated the registered model version.

  • tags (List[TagWithId] or None) – The tags associated with the registered model version.

  • mlpkg_file_contents (str or None) – The contents of the model package file.