Experiment Deployment¶
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class
previsionio.experiment_deployment.
ExperimentDeployment
(_id: str, name: str, experiment_id, current_version, versions, deploy_state, current_type_violation_policy, access_type, project_id, training_type, models, url=None, **kwargs)¶ ExperimentDeployment objects represent experiment deployment resource that will be explored by Prevision.io platform.
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create_api_key
()¶ Create an api key of the experiment deployment from the actual [client] workspace.
Raises: PrevisionException
– If the dataset does not existrequests.exceptions.ConnectionError
– Error processing the request
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delete
()¶ Delete an experiment deployment from the actual [client] workspace.
Raises: PrevisionException
– If the experiment deployment does not existrequests.exceptions.ConnectionError
– Error processing the request
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classmethod
from_id
(_id: str)¶ Get a deployed experiment from the platform by its unique id.
Parameters: _id (str) – Unique id of the experiment version to retrieve Returns: Fetched deployed experiment Return type: ExperimentDeployment
Raises: PrevisionException
– Any error while fetching data from the platform or parsing result
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get_api_keys
()¶ Fetch the api keys client id and cient secret of the experiment deployment from the actual [client] workspace.
Raises: PrevisionException
– If the dataset does not existrequests.exceptions.ConnectionError
– Error processing the request
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classmethod
list
(project_id: str, all: bool = True) → List[previsionio.experiment_deployment.ExperimentDeployment]¶ List all the available experiment in the current active [client] workspace.
Warning
Contrary to the parent
list()
function, this method returns actualExperimentDeployment
objects rather than plain dictionaries with the corresponding data.Parameters: - project_id (str) – project id
- all (boolean, optional) – Whether to force the SDK to load all items of the given type (by calling the paginated API several times). Else, the query will only return the first page of result.
Returns: Fetched dataset objects
Return type: list(
ExperimentDeployment
)
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list_predictions
() → List[previsionio.prediction.DeploymentPrediction]¶ List all the available predictions in the current active [client] workspace.
Returns: Fetched deployed predictions objects Return type: list( DeploymentPrediction
)
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new_version
(name: str, main_model, challenger_model=None)¶ Create a new experiment deployment version.
Parameters: - name (str) – experiment deployment name
- main_model – main model
- challenger_model (optional) – challenger model. main and challenger models should be in the same experiment
Returns: The registered experiment deployment object in the current project
Return type: Raises: PrevisionException
– Any error while creating experiment deployment to the platform or parsing the resultException
– For any other unknown error
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predict_from_dataset
(dataset: previsionio.dataset.Dataset) → previsionio.prediction.DeploymentPrediction¶ Make a prediction for a dataset stored in the current active [client] workspace (using the current SDK dataset object).
Parameters: dataset ( Dataset
) – Dataset resource to make a prediction forReturns: The registered prediction object in the current workspace Return type: DeploymentPrediction
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wait_until
(condition, timeout: float = 14400.0)¶ Wait until condition is fulfilled, then break.
Parameters: - (func (condition) – (
BaseExperimentVersion
) -> bool.): Function to use to check the break condition - raise_on_error (bool, optional) – If true then the function will stop on error,
otherwise it will continue waiting (default:
True
) - timeout (float, optional) – Maximal amount of time to wait before forcing exit
Example:
experiment.wait_until(lambda experimentv: len(experimentv.models) > 3)
Raises: PrevisionException
– If the resource could not be fetched or there was a timeout.- (func (condition) – (
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