src/sagemaker/automl/automl.py [398:506]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - self._best_candidate = self._auto_ml_job_desc["BestCandidate"] return self._best_candidate def list_candidates( self, job_name=None, status_equals=None, candidate_name=None, candidate_arn=None, sort_order=None, sort_by=None, max_results=None, ): """Returns the list of candidates of an AutoML job for a given name. Args: job_name (str): The name of the AutoML job. If None, will use object's _current_job name. status_equals (str): Filter the result with candidate status, values could be "Completed", "InProgress", "Failed", "Stopped", "Stopping" candidate_name (str): The name of a specified candidate to list. Default to None. candidate_arn (str): The Arn of a specified candidate to list. Default to None. sort_order (str): The order that the candidates will be listed in result. Default to None. sort_by (str): The value that the candidates will be sorted by. Default to None. max_results (int): The number of candidates will be listed in results, between 1 to 100. Default to None. If None, will return all the candidates. Returns: list: A list of dictionaries with candidates information. """ if job_name is None: job_name = self.current_job_name list_candidates_args = {"job_name": job_name} if status_equals: list_candidates_args["status_equals"] = status_equals if candidate_name: list_candidates_args["candidate_name"] = candidate_name if candidate_arn: list_candidates_args["candidate_arn"] = candidate_arn if sort_order: list_candidates_args["sort_order"] = sort_order if sort_by: list_candidates_args["sort_by"] = sort_by if max_results: list_candidates_args["max_results"] = max_results return self.sagemaker_session.list_candidates(**list_candidates_args)["Candidates"] def create_model( self, name, sagemaker_session=None, candidate=None, vpc_config=None, enable_network_isolation=False, model_kms_key=None, predictor_cls=None, inference_response_keys=None, ): """Creates a model from a given candidate or the best candidate from the job. Args: name (str): The pipeline model name. sagemaker_session (sagemaker.session.Session): A SageMaker Session object, used for SageMaker interactions (default: None). If not specified, the one originally associated with the ``AutoML`` instance is used.: candidate (CandidateEstimator or dict): a CandidateEstimator used for deploying to a SageMaker Inference Pipeline. If None, the best candidate will be used. If the candidate input is a dict, a CandidateEstimator will be created from it. vpc_config (dict): Specifies a VPC that your training jobs and hosted models have access to. Contents include "SecurityGroupIds" and "Subnets". enable_network_isolation (bool): Isolates the training container. No inbound or outbound network calls can be made, except for calls between peers within a training cluster for distributed training. Default: False model_kms_key (str): KMS key ARN used to encrypt the repacked model archive file if the model is repacked Callable[[string, sagemaker.session.Session], Any]: A function to call to create a predictor (default: None). If specified, ``deploy()`` returns the result of invoking this function on the created endpoint name. inference_response_keys (list): List of keys for response content. The order of the keys will dictate the content order in the response. Returns: PipelineModel object. """ sagemaker_session = sagemaker_session or self.sagemaker_session if candidate is None: candidate_dict = self.best_candidate() candidate = CandidateEstimator(candidate_dict, sagemaker_session=sagemaker_session) elif isinstance(candidate, dict): candidate = CandidateEstimator(candidate, sagemaker_session=sagemaker_session) inference_containers = candidate.containers self.validate_and_update_inference_response(inference_containers, inference_response_keys) # construct Model objects models = [] for container in inference_containers: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - src/sagemaker/automl/automlv2.py [942:1049]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - self._best_candidate = self._auto_ml_job_desc["BestCandidate"] return self._best_candidate def list_candidates( self, job_name=None, status_equals=None, candidate_name=None, candidate_arn=None, sort_order=None, sort_by=None, max_results=None, ): """Returns the list of candidates of an AutoML job for a given name. Args: job_name (str): The name of the AutoML job. If None, will use object's _current_job name. status_equals (str): Filter the result with candidate status, values could be "Completed", "InProgress", "Failed", "Stopped", "Stopping" candidate_name (str): The name of a specified candidate to list. Default to None. candidate_arn (str): The Arn of a specified candidate to list. Default to None. sort_order (str): The order that the candidates will be listed in result. Default to None. sort_by (str): The value that the candidates will be sorted by. Default to None. max_results (int): The number of candidates will be listed in results, between 1 to 100. Default to None. If None, will return all the candidates. Returns: list: A list of dictionaries with candidates information. """ if job_name is None: job_name = self.current_job_name list_candidates_args = {"job_name": job_name} if status_equals: list_candidates_args["status_equals"] = status_equals if candidate_name: list_candidates_args["candidate_name"] = candidate_name if candidate_arn: list_candidates_args["candidate_arn"] = candidate_arn if sort_order: list_candidates_args["sort_order"] = sort_order if sort_by: list_candidates_args["sort_by"] = sort_by if max_results: list_candidates_args["max_results"] = max_results return self.sagemaker_session.list_candidates(**list_candidates_args)["Candidates"] def create_model( self, name, sagemaker_session=None, candidate=None, vpc_config=None, enable_network_isolation=False, model_kms_key=None, predictor_cls=None, inference_response_keys=None, ): """Creates a model from a given candidate or the best candidate from the job. Args: name (str): The pipeline model name. sagemaker_session (sagemaker.session.Session): A SageMaker Session object, used for SageMaker interactions (default: None). If not specified, the one originally associated with the ``AutoML`` instance is used.: candidate (CandidateEstimator or dict): a CandidateEstimator used for deploying to a SageMaker Inference Pipeline. If None, the best candidate will be used. If the candidate input is a dict, a CandidateEstimator will be created from it. vpc_config (dict): Specifies a VPC that your training jobs and hosted models have access to. Contents include "SecurityGroupIds" and "Subnets". enable_network_isolation (bool): Isolates the training container. No inbound or outbound network calls can be made, except for calls between peers within a training cluster for distributed training. Default: False model_kms_key (str): KMS key ARN used to encrypt the repacked model archive file if the model is repacked predictor_cls (Callable[[string, sagemaker.session.Session], Any]): A function to call to create a predictor (default: None). If specified, ``deploy()`` returns the result of invoking this function on the created endpoint name. inference_response_keys (list): List of keys for response content. The order of the keys will dictate the content order in the response. Returns: PipelineModel object. """ sagemaker_session = sagemaker_session or self.sagemaker_session if candidate is None: candidate_dict = self.best_candidate() candidate = CandidateEstimator(candidate_dict, sagemaker_session=sagemaker_session) elif isinstance(candidate, dict): candidate = CandidateEstimator(candidate, sagemaker_session=sagemaker_session) inference_containers = candidate.containers self.validate_and_update_inference_response(inference_containers, inference_response_keys) models = [] for container in inference_containers: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -