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

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

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

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.