import json from typing import Any from moto.sagemaker.exceptions import AWSValidationException from moto.core.common_types import TYPE_RESPONSE from moto.core.responses import BaseResponse from moto.utilities.aws_headers import amzn_request_id from .models import sagemaker_backends, SageMakerModelBackend def format_enum_error(value: str, attribute: str, allowed: Any) -> str: return f"Value '{value}' at '{attribute}' failed to satisfy constraint: Member must satisfy enum value set: {allowed}" class SageMakerResponse(BaseResponse): def __init__(self) -> None: super().__init__(service_name="sagemaker") @property def sagemaker_backend(self) -> SageMakerModelBackend: return sagemaker_backends[self.current_account][self.region] def describe_model(self) -> str: model_name = self._get_param("ModelName") model = self.sagemaker_backend.describe_model(model_name) return json.dumps(model.response_object) def create_model(self) -> str: model_name = self._get_param("ModelName") execution_role_arn = self._get_param("ExecutionRoleArn") primary_container = self._get_param("PrimaryContainer") vpc_config = self._get_param("VpcConfig") containers = self._get_param("Containers") tags = self._get_param("Tags") model = self.sagemaker_backend.create_model( model_name=model_name, execution_role_arn=execution_role_arn, primary_container=primary_container, vpc_config=vpc_config, containers=containers, tags=tags, ) return json.dumps(model.response_create) def delete_model(self) -> str: model_name = self._get_param("ModelName") self.sagemaker_backend.delete_model(model_name) return "{}" def list_models(self) -> str: models = self.sagemaker_backend.list_models() return json.dumps({"Models": [model.response_object for model in models]}) @amzn_request_id def create_notebook_instance(self) -> TYPE_RESPONSE: sagemaker_notebook = self.sagemaker_backend.create_notebook_instance( notebook_instance_name=self._get_param("NotebookInstanceName"), instance_type=self._get_param("InstanceType"), subnet_id=self._get_param("SubnetId"), security_group_ids=self._get_param("SecurityGroupIds"), role_arn=self._get_param("RoleArn"), kms_key_id=self._get_param("KmsKeyId"), tags=self._get_param("Tags"), lifecycle_config_name=self._get_param("LifecycleConfigName"), direct_internet_access=self._get_param("DirectInternetAccess"), volume_size_in_gb=self._get_param("VolumeSizeInGB"), accelerator_types=self._get_param("AcceleratorTypes"), default_code_repository=self._get_param("DefaultCodeRepository"), additional_code_repositories=self._get_param("AdditionalCodeRepositories"), root_access=self._get_param("RootAccess"), ) return 200, {}, json.dumps({"NotebookInstanceArn": sagemaker_notebook.arn}) @amzn_request_id def describe_notebook_instance(self) -> TYPE_RESPONSE: notebook_instance_name = self._get_param("NotebookInstanceName") notebook_instance = self.sagemaker_backend.get_notebook_instance( notebook_instance_name ) response = { "NotebookInstanceArn": notebook_instance.arn, "NotebookInstanceName": notebook_instance.notebook_instance_name, "NotebookInstanceStatus": notebook_instance.status, "Url": notebook_instance.url, "InstanceType": notebook_instance.instance_type, "SubnetId": notebook_instance.subnet_id, "SecurityGroups": notebook_instance.security_group_ids, "RoleArn": notebook_instance.role_arn, "KmsKeyId": notebook_instance.kms_key_id, # ToDo: NetworkInterfaceId "LastModifiedTime": str(notebook_instance.last_modified_time), "CreationTime": str(notebook_instance.creation_time), "NotebookInstanceLifecycleConfigName": notebook_instance.lifecycle_config_name, "DirectInternetAccess": notebook_instance.direct_internet_access, "VolumeSizeInGB": notebook_instance.volume_size_in_gb, "AcceleratorTypes": notebook_instance.accelerator_types, "DefaultCodeRepository": notebook_instance.default_code_repository, "AdditionalCodeRepositories": notebook_instance.additional_code_repositories, "RootAccess": notebook_instance.root_access, } return 200, {}, json.dumps(response) @amzn_request_id def start_notebook_instance(self) -> TYPE_RESPONSE: notebook_instance_name = self._get_param("NotebookInstanceName") self.sagemaker_backend.start_notebook_instance(notebook_instance_name) return 200, {}, json.dumps("{}") @amzn_request_id def stop_notebook_instance(self) -> TYPE_RESPONSE: notebook_instance_name = self._get_param("NotebookInstanceName") self.sagemaker_backend.stop_notebook_instance(notebook_instance_name) return 200, {}, json.dumps("{}") @amzn_request_id def delete_notebook_instance(self) -> TYPE_RESPONSE: notebook_instance_name = self._get_param("NotebookInstanceName") self.sagemaker_backend.delete_notebook_instance(notebook_instance_name) return 200, {}, json.dumps("{}") @amzn_request_id def list_tags(self) -> TYPE_RESPONSE: arn = self._get_param("ResourceArn") max_results = self._get_param("MaxResults") next_token = self._get_param("NextToken") paged_results, next_token = self.sagemaker_backend.list_tags( arn=arn, MaxResults=max_results, NextToken=next_token ) response = {"Tags": paged_results} if next_token: response["NextToken"] = next_token return 200, {}, json.dumps(response) @amzn_request_id def add_tags(self) -> TYPE_RESPONSE: arn = self._get_param("ResourceArn") tags = self._get_param("Tags") tags = self.sagemaker_backend.add_tags(arn, tags) return 200, {}, json.dumps({"Tags": tags}) @amzn_request_id def delete_tags(self) -> TYPE_RESPONSE: arn = self._get_param("ResourceArn") tag_keys = self._get_param("TagKeys") self.sagemaker_backend.delete_tags(arn, tag_keys) return 200, {}, json.dumps({}) @amzn_request_id def create_endpoint_config(self) -> TYPE_RESPONSE: endpoint_config = self.sagemaker_backend.create_endpoint_config( endpoint_config_name=self._get_param("EndpointConfigName"), production_variants=self._get_param("ProductionVariants"), data_capture_config=self._get_param("DataCaptureConfig"), tags=self._get_param("Tags"), kms_key_id=self._get_param("KmsKeyId"), ) return ( 200, {}, json.dumps({"EndpointConfigArn": endpoint_config.endpoint_config_arn}), ) @amzn_request_id def describe_endpoint_config(self) -> str: endpoint_config_name = self._get_param("EndpointConfigName") response = self.sagemaker_backend.describe_endpoint_config(endpoint_config_name) return json.dumps(response) @amzn_request_id def delete_endpoint_config(self) -> TYPE_RESPONSE: endpoint_config_name = self._get_param("EndpointConfigName") self.sagemaker_backend.delete_endpoint_config(endpoint_config_name) return 200, {}, json.dumps("{}") @amzn_request_id def create_endpoint(self) -> TYPE_RESPONSE: endpoint = self.sagemaker_backend.create_endpoint( endpoint_name=self._get_param("EndpointName"), endpoint_config_name=self._get_param("EndpointConfigName"), tags=self._get_param("Tags"), ) return 200, {}, json.dumps({"EndpointArn": endpoint.endpoint_arn}) @amzn_request_id def describe_endpoint(self) -> str: endpoint_name = self._get_param("EndpointName") response = self.sagemaker_backend.describe_endpoint(endpoint_name) return json.dumps(response) @amzn_request_id def delete_endpoint(self) -> TYPE_RESPONSE: endpoint_name = self._get_param("EndpointName") self.sagemaker_backend.delete_endpoint(endpoint_name) return 200, {}, json.dumps("{}") @amzn_request_id def create_processing_job(self) -> TYPE_RESPONSE: processing_job = self.sagemaker_backend.create_processing_job( app_specification=self._get_param("AppSpecification"), experiment_config=self._get_param("ExperimentConfig"), network_config=self._get_param("NetworkConfig"), processing_inputs=self._get_param("ProcessingInputs"), processing_job_name=self._get_param("ProcessingJobName"), processing_output_config=self._get_param("ProcessingOutputConfig"), role_arn=self._get_param("RoleArn"), stopping_condition=self._get_param("StoppingCondition"), tags=self._get_param("Tags"), ) response = {"ProcessingJobArn": processing_job.processing_job_arn} return 200, {}, json.dumps(response) @amzn_request_id def describe_processing_job(self) -> str: processing_job_name = self._get_param("ProcessingJobName") response = self.sagemaker_backend.describe_processing_job(processing_job_name) return json.dumps(response) @amzn_request_id def create_transform_job(self) -> TYPE_RESPONSE: transform_job = self.sagemaker_backend.create_transform_job( transform_job_name=self._get_param("TransformJobName"), model_name=self._get_param("ModelName"), max_concurrent_transforms=self._get_param("MaxConcurrentTransforms"), model_client_config=self._get_param("ModelClientConfig"), max_payload_in_mb=self._get_param("MaxPayloadInMB"), batch_strategy=self._get_param("BatchStrategy"), environment=self._get_param("Environment"), transform_input=self._get_param("TransformInput"), transform_output=self._get_param("TransformOutput"), data_capture_config=self._get_param("DataCaptureConfig"), transform_resources=self._get_param("TransformResources"), data_processing=self._get_param("DataProcessing"), tags=self._get_param("Tags"), experiment_config=self._get_param("ExperimentConfig"), ) response = { "TransformJobArn": transform_job.transform_job_arn, } return 200, {}, json.dumps(response) @amzn_request_id def describe_transform_job(self) -> str: transform_job_name = self._get_param("TransformJobName") response = self.sagemaker_backend.describe_transform_job( transform_job_name=transform_job_name ) return json.dumps(response) @amzn_request_id def create_training_job(self) -> TYPE_RESPONSE: training_job = self.sagemaker_backend.create_training_job( training_job_name=self._get_param("TrainingJobName"), hyper_parameters=self._get_param("HyperParameters"), algorithm_specification=self._get_param("AlgorithmSpecification"), role_arn=self._get_param("RoleArn"), input_data_config=self._get_param("InputDataConfig"), output_data_config=self._get_param("OutputDataConfig"), resource_config=self._get_param("ResourceConfig"), vpc_config=self._get_param("VpcConfig"), stopping_condition=self._get_param("StoppingCondition"), tags=self._get_param("Tags"), enable_network_isolation=self._get_param("EnableNetworkIsolation", False), enable_inter_container_traffic_encryption=self._get_param( "EnableInterContainerTrafficEncryption", False ), enable_managed_spot_training=self._get_param( "EnableManagedSpotTraining", False ), checkpoint_config=self._get_param("CheckpointConfig"), debug_hook_config=self._get_param("DebugHookConfig"), debug_rule_configurations=self._get_param("DebugRuleConfigurations"), tensor_board_output_config=self._get_param("TensorBoardOutputConfig"), experiment_config=self._get_param("ExperimentConfig"), ) response = { "TrainingJobArn": training_job.training_job_arn, } return 200, {}, json.dumps(response) @amzn_request_id def describe_training_job(self) -> str: training_job_name = self._get_param("TrainingJobName") response = self.sagemaker_backend.describe_training_job(training_job_name) return json.dumps(response) @amzn_request_id def create_notebook_instance_lifecycle_config(self) -> TYPE_RESPONSE: lifecycle_configuration = ( self.sagemaker_backend.create_notebook_instance_lifecycle_config( notebook_instance_lifecycle_config_name=self._get_param( "NotebookInstanceLifecycleConfigName" ), on_create=self._get_param("OnCreate"), on_start=self._get_param("OnStart"), ) ) response = { "NotebookInstanceLifecycleConfigArn": lifecycle_configuration.notebook_instance_lifecycle_config_arn, } return 200, {}, json.dumps(response) @amzn_request_id def describe_notebook_instance_lifecycle_config(self) -> str: response = self.sagemaker_backend.describe_notebook_instance_lifecycle_config( notebook_instance_lifecycle_config_name=self._get_param( "NotebookInstanceLifecycleConfigName" ) ) return json.dumps(response) @amzn_request_id def delete_notebook_instance_lifecycle_config(self) -> TYPE_RESPONSE: self.sagemaker_backend.delete_notebook_instance_lifecycle_config( notebook_instance_lifecycle_config_name=self._get_param( "NotebookInstanceLifecycleConfigName" ) ) return 200, {}, json.dumps("{}") @amzn_request_id def search(self) -> TYPE_RESPONSE: response = self.sagemaker_backend.search( resource=self._get_param("Resource"), search_expression=self._get_param("SearchExpression"), ) return 200, {}, json.dumps(response) @amzn_request_id def list_experiments(self) -> TYPE_RESPONSE: MaxResults = self._get_param("MaxResults") NextToken = self._get_param("NextToken") paged_results, next_token = self.sagemaker_backend.list_experiments( MaxResults=MaxResults, NextToken=NextToken ) experiment_summaries = [ { "ExperimentName": experiment_data.experiment_name, "ExperimentArn": experiment_data.experiment_arn, "CreationTime": experiment_data.creation_time, "LastModifiedTime": experiment_data.last_modified_time, } for experiment_data in paged_results ] response = { "ExperimentSummaries": experiment_summaries, } if next_token: response["NextToken"] = next_token return 200, {}, json.dumps(response) @amzn_request_id def delete_experiment(self) -> TYPE_RESPONSE: self.sagemaker_backend.delete_experiment( experiment_name=self._get_param("ExperimentName") ) return 200, {}, json.dumps({}) @amzn_request_id def create_experiment(self) -> TYPE_RESPONSE: response = self.sagemaker_backend.create_experiment( experiment_name=self._get_param("ExperimentName") ) return 200, {}, json.dumps(response) @amzn_request_id def describe_experiment(self) -> TYPE_RESPONSE: response = self.sagemaker_backend.describe_experiment( experiment_name=self._get_param("ExperimentName") ) return 200, {}, json.dumps(response) @amzn_request_id def list_trials(self) -> TYPE_RESPONSE: MaxResults = self._get_param("MaxResults") NextToken = self._get_param("NextToken") paged_results, next_token = self.sagemaker_backend.list_trials( NextToken=NextToken, MaxResults=MaxResults, experiment_name=self._get_param("ExperimentName"), trial_component_name=self._get_param("TrialComponentName"), ) trial_summaries = [ { "TrialName": trial_data.trial_name, "TrialArn": trial_data.trial_arn, "CreationTime": trial_data.creation_time, "LastModifiedTime": trial_data.last_modified_time, } for trial_data in paged_results ] response = { "TrialSummaries": trial_summaries, } if next_token: response["NextToken"] = next_token return 200, {}, json.dumps(response) @amzn_request_id def create_trial(self) -> TYPE_RESPONSE: response = self.sagemaker_backend.create_trial( trial_name=self._get_param("TrialName"), experiment_name=self._get_param("ExperimentName"), ) return 200, {}, json.dumps(response) @amzn_request_id def list_trial_components(self) -> TYPE_RESPONSE: MaxResults = self._get_param("MaxResults") NextToken = self._get_param("NextToken") paged_results, next_token = self.sagemaker_backend.list_trial_components( NextToken=NextToken, MaxResults=MaxResults, trial_name=self._get_param("TrialName"), ) trial_component_summaries = [ { "TrialComponentName": trial_component_data.trial_component_name, "TrialComponentArn": trial_component_data.trial_component_arn, "CreationTime": trial_component_data.creation_time, "LastModifiedTime": trial_component_data.last_modified_time, } for trial_component_data in paged_results ] response = { "TrialComponentSummaries": trial_component_summaries, } if next_token: response["NextToken"] = next_token return 200, {}, json.dumps(response) @amzn_request_id def create_trial_component(self) -> TYPE_RESPONSE: response = self.sagemaker_backend.create_trial_component( trial_component_name=self._get_param("TrialComponentName"), trial_name=self._get_param("TrialName"), ) return 200, {}, json.dumps(response) @amzn_request_id def describe_trial(self) -> str: trial_name = self._get_param("TrialName") response = self.sagemaker_backend.describe_trial(trial_name) return json.dumps(response) @amzn_request_id def delete_trial(self) -> TYPE_RESPONSE: trial_name = self._get_param("TrialName") self.sagemaker_backend.delete_trial(trial_name) return 200, {}, json.dumps({}) @amzn_request_id def delete_trial_component(self) -> TYPE_RESPONSE: trial_component_name = self._get_param("TrialComponentName") self.sagemaker_backend.delete_trial_component(trial_component_name) return 200, {}, json.dumps({}) @amzn_request_id def describe_trial_component(self) -> str: trial_component_name = self._get_param("TrialComponentName") response = self.sagemaker_backend.describe_trial_component(trial_component_name) return json.dumps(response) @amzn_request_id def associate_trial_component(self) -> TYPE_RESPONSE: trial_name = self._get_param("TrialName") trial_component_name = self._get_param("TrialComponentName") response = self.sagemaker_backend.associate_trial_component( trial_name, trial_component_name ) return 200, {}, json.dumps(response) @amzn_request_id def disassociate_trial_component(self) -> TYPE_RESPONSE: trial_component_name = self._get_param("TrialComponentName") trial_name = self._get_param("TrialName") response = self.sagemaker_backend.disassociate_trial_component( trial_name, trial_component_name ) return 200, {}, json.dumps(response) @amzn_request_id def describe_pipeline(self) -> TYPE_RESPONSE: response = self.sagemaker_backend.describe_pipeline( self._get_param("PipelineName") ) return 200, {}, json.dumps(response) @amzn_request_id def start_pipeline_execution(self) -> TYPE_RESPONSE: response = self.sagemaker_backend.start_pipeline_execution( self._get_param("PipelineName"), self._get_param("PipelineExecutionDisplayName"), self._get_param("PipelineParameters"), self._get_param("PipelineExecutionDescription"), self._get_param("ParallelismConfiguration"), self._get_param("ClientRequestToken"), ) return 200, {}, json.dumps(response) @amzn_request_id def describe_pipeline_execution(self) -> TYPE_RESPONSE: response = self.sagemaker_backend.describe_pipeline_execution( self._get_param("PipelineExecutionArn") ) return 200, {}, json.dumps(response) @amzn_request_id def describe_pipeline_definition_for_execution(self) -> TYPE_RESPONSE: response = self.sagemaker_backend.describe_pipeline_definition_for_execution( self._get_param("PipelineExecutionArn") ) return 200, {}, json.dumps(response) @amzn_request_id def list_pipeline_parameters_for_execution(self) -> TYPE_RESPONSE: response = self.sagemaker_backend.list_pipeline_parameters_for_execution( self._get_param("PipelineExecutionArn") ) return 200, {}, json.dumps(response) @amzn_request_id def list_pipeline_executions(self) -> TYPE_RESPONSE: response = self.sagemaker_backend.list_pipeline_executions( self._get_param("PipelineName") ) return 200, {}, json.dumps(response) @amzn_request_id def create_pipeline(self) -> TYPE_RESPONSE: pipeline = self.sagemaker_backend.create_pipeline( pipeline_name=self._get_param("PipelineName"), pipeline_display_name=self._get_param("PipelineDisplayName"), pipeline_definition=self._get_param("PipelineDefinition"), pipeline_definition_s3_location=self._get_param( "PipelineDefinitionS3Location" ), pipeline_description=self._get_param("PipelineDescription"), role_arn=self._get_param("RoleArn"), tags=self._get_param("Tags"), parallelism_configuration=self._get_param("ParallelismConfiguration"), ) response = { "PipelineArn": pipeline.pipeline_arn, } return 200, {}, json.dumps(response) @amzn_request_id def delete_pipeline(self) -> TYPE_RESPONSE: pipeline_arn = self.sagemaker_backend.delete_pipeline( pipeline_name=self._get_param("PipelineName"), ) response = {"PipelineArn": pipeline_arn} return 200, {}, json.dumps(response) @amzn_request_id def update_pipeline(self) -> TYPE_RESPONSE: pipeline_arn = self.sagemaker_backend.update_pipeline( pipeline_name=self._get_param("PipelineName"), pipeline_display_name=self._get_param("PipelineDisplayName"), pipeline_definition=self._get_param("PipelineDefinition"), pipeline_definition_s3_location=self._get_param( "PipelineDefinitionS3Location" ), pipeline_description=self._get_param("PipelineDescription"), role_arn=self._get_param("RoleArn"), parallelism_configuration=self._get_param("ParallelismConfiguration"), ) response = {"PipelineArn": pipeline_arn} return 200, {}, json.dumps(response) @amzn_request_id def list_pipelines(self) -> TYPE_RESPONSE: max_results_range = range(1, 101) allowed_sort_by = ("Name", "CreationTime") allowed_sort_order = ("Ascending", "Descending") pipeline_name_prefix = self._get_param("PipelineNamePrefix") created_after = self._get_param("CreatedAfter") created_before = self._get_param("CreatedBefore") sort_by = self._get_param("SortBy", "CreationTime") sort_order = self._get_param("SortOrder", "Descending") next_token = self._get_param("NextToken") max_results = self._get_param("MaxResults") errors = [] if max_results and max_results not in max_results_range: errors.append( f"Value '{max_results}' at 'maxResults' failed to satisfy constraint: Member must have value less than or equal to {max_results_range[-1]}" ) if sort_by not in allowed_sort_by: errors.append(format_enum_error(sort_by, "SortBy", allowed_sort_by)) if sort_order not in allowed_sort_order: errors.append( format_enum_error(sort_order, "SortOrder", allowed_sort_order) ) if errors: raise AWSValidationException( f"{len(errors)} validation errors detected: {';'.join(errors)}" ) response = self.sagemaker_backend.list_pipelines( pipeline_name_prefix=pipeline_name_prefix, created_after=created_after, created_before=created_before, next_token=next_token, max_results=max_results, sort_by=sort_by, sort_order=sort_order, ) return 200, {}, json.dumps(response) @amzn_request_id def list_processing_jobs(self) -> TYPE_RESPONSE: max_results_range = range(1, 101) allowed_sort_by = ["Name", "CreationTime", "Status"] allowed_sort_order = ["Ascending", "Descending"] allowed_status_equals = [ "Completed", "Stopped", "InProgress", "Stopping", "Failed", ] max_results = self._get_int_param("MaxResults") sort_by = self._get_param("SortBy", "CreationTime") sort_order = self._get_param("SortOrder", "Ascending") status_equals = self._get_param("StatusEquals") next_token = self._get_param("NextToken") errors = [] if max_results and max_results not in max_results_range: errors.append( f"Value '{max_results}' at 'maxResults' failed to satisfy constraint: Member must have value less than or equal to {max_results_range[-1]}" ) if sort_by not in allowed_sort_by: errors.append(format_enum_error(sort_by, "sortBy", allowed_sort_by)) if sort_order not in allowed_sort_order: errors.append( format_enum_error(sort_order, "sortOrder", allowed_sort_order) ) if status_equals and status_equals not in allowed_status_equals: errors.append( format_enum_error(status_equals, "statusEquals", allowed_status_equals) ) if errors != []: raise AWSValidationException( f"{len(errors)} validation errors detected: {';'.join(errors)}" ) response = self.sagemaker_backend.list_processing_jobs( next_token=next_token, max_results=max_results, creation_time_after=self._get_param("CreationTimeAfter"), creation_time_before=self._get_param("CreationTimeBefore"), last_modified_time_after=self._get_param("LastModifiedTimeAfter"), last_modified_time_before=self._get_param("LastModifiedTimeBefore"), name_contains=self._get_param("NameContains"), status_equals=status_equals, ) return 200, {}, json.dumps(response) @amzn_request_id def list_transform_jobs(self) -> TYPE_RESPONSE: max_results_range = range(1, 101) allowed_sort_by = ["Name", "CreationTime", "Status"] allowed_sort_order = ["Ascending", "Descending"] allowed_status_equals = [ "Completed", "Stopped", "InProgress", "Stopping", "Failed", ] max_results = self._get_int_param("MaxResults") sort_by = self._get_param("SortBy", "CreationTime") sort_order = self._get_param("SortOrder", "Ascending") status_equals = self._get_param("StatusEquals") next_token = self._get_param("NextToken") errors = [] if max_results and max_results not in max_results_range: errors.append( f"Value '{max_results}' at 'maxResults' failed to satisfy constraint: Member must have value less than or equal to {max_results_range[-1]}" ) if sort_by not in allowed_sort_by: errors.append(format_enum_error(sort_by, "sortBy", allowed_sort_by)) if sort_order not in allowed_sort_order: errors.append( format_enum_error(sort_order, "sortOrder", allowed_sort_order) ) if status_equals and status_equals not in allowed_status_equals: errors.append( format_enum_error(status_equals, "statusEquals", allowed_status_equals) ) if errors != []: raise AWSValidationException( f"{len(errors)} validation errors detected: {';'.join(errors)}" ) response = self.sagemaker_backend.list_transform_jobs( next_token=next_token, max_results=max_results, creation_time_after=self._get_param("CreationTimeAfter"), creation_time_before=self._get_param("CreationTimeBefore"), last_modified_time_after=self._get_param("LastModifiedTimeAfter"), last_modified_time_before=self._get_param("LastModifiedTimeBefore"), name_contains=self._get_param("NameContains"), status_equals=status_equals, ) return 200, {}, json.dumps(response) @amzn_request_id def list_training_jobs(self) -> TYPE_RESPONSE: max_results_range = range(1, 101) allowed_sort_by = ["Name", "CreationTime", "Status"] allowed_sort_order = ["Ascending", "Descending"] allowed_status_equals = [ "Completed", "Stopped", "InProgress", "Stopping", "Failed", ] max_results = self._get_int_param("MaxResults") sort_by = self._get_param("SortBy", "CreationTime") sort_order = self._get_param("SortOrder", "Ascending") status_equals = self._get_param("StatusEquals") next_token = self._get_param("NextToken") errors = [] if max_results and max_results not in max_results_range: errors.append( f"Value '{max_results}' at 'maxResults' failed to satisfy constraint: Member must have value less than or equal to {max_results_range[-1]}" ) if sort_by not in allowed_sort_by: errors.append(format_enum_error(sort_by, "sortBy", allowed_sort_by)) if sort_order not in allowed_sort_order: errors.append( format_enum_error(sort_order, "sortOrder", allowed_sort_order) ) if status_equals and status_equals not in allowed_status_equals: errors.append( format_enum_error(status_equals, "statusEquals", allowed_status_equals) ) if errors != []: raise AWSValidationException( f"{len(errors)} validation errors detected: {';'.join(errors)}" ) response = self.sagemaker_backend.list_training_jobs( next_token=next_token, max_results=max_results, creation_time_after=self._get_param("CreationTimeAfter"), creation_time_before=self._get_param("CreationTimeBefore"), last_modified_time_after=self._get_param("LastModifiedTimeAfter"), last_modified_time_before=self._get_param("LastModifiedTimeBefore"), name_contains=self._get_param("NameContains"), status_equals=status_equals, ) return 200, {}, json.dumps(response) def update_endpoint_weights_and_capacities(self) -> TYPE_RESPONSE: endpoint_name = self._get_param("EndpointName") desired_weights_and_capacities = self._get_param("DesiredWeightsAndCapacities") endpoint_arn = self.sagemaker_backend.update_endpoint_weights_and_capacities( endpoint_name=endpoint_name, desired_weights_and_capacities=desired_weights_and_capacities, ) return 200, {}, json.dumps({"EndpointArn": endpoint_arn}) def list_model_packages(self) -> str: creation_time_after = self._get_param("CreationTimeAfter") creation_time_before = self._get_param("CreationTimeBefore") max_results = self._get_param("MaxResults") name_contains = self._get_param("NameContains") model_approval_status = self._get_param("ModelApprovalStatus") model_package_group_name = self._get_param("ModelPackageGroupName") model_package_type = self._get_param("ModelPackageType", "Unversioned") next_token = self._get_param("NextToken") sort_by = self._get_param("SortBy") sort_order = self._get_param("SortOrder") ( model_package_summary_list, next_token, ) = self.sagemaker_backend.list_model_packages( creation_time_after=creation_time_after, creation_time_before=creation_time_before, max_results=max_results, name_contains=name_contains, model_approval_status=model_approval_status, model_package_group_name=model_package_group_name, model_package_type=model_package_type, next_token=next_token, sort_by=sort_by, sort_order=sort_order, ) model_package_summary_list_response_object = [ x.gen_response_object() for x in model_package_summary_list ] return json.dumps( dict( ModelPackageSummaryList=model_package_summary_list_response_object, NextToken=next_token, ) ) def describe_model_package(self) -> str: model_package_name = self._get_param("ModelPackageName") model_package = self.sagemaker_backend.describe_model_package( model_package_name=model_package_name, ) return json.dumps( model_package.gen_response_object(), ) def create_model_package(self) -> str: model_package_name = self._get_param("ModelPackageName") model_package_group_name = self._get_param("ModelPackageGroupName") model_package_description = self._get_param("ModelPackageDescription") inference_specification = self._get_param("InferenceSpecification") validation_specification = self._get_param("ValidationSpecification") source_algorithm_specification = self._get_param("SourceAlgorithmSpecification") certify_for_marketplace = self._get_param("CertifyForMarketplace") tags = self._get_param("Tags") model_approval_status = self._get_param("ModelApprovalStatus") metadata_properties = self._get_param("MetadataProperties") model_metrics = self._get_param("ModelMetrics") client_token = self._get_param("ClientToken") customer_metadata_properties = self._get_param("CustomerMetadataProperties") drift_check_baselines = self._get_param("DriftCheckBaselines") domain = self._get_param("Domain") task = self._get_param("Task") sample_payload_url = self._get_param("SamplePayloadUrl") additional_inference_specifications = self._get_param( "AdditionalInferenceSpecifications" ) model_package_arn = self.sagemaker_backend.create_model_package( model_package_name=model_package_name, model_package_group_name=model_package_group_name, model_package_description=model_package_description, inference_specification=inference_specification, validation_specification=validation_specification, source_algorithm_specification=source_algorithm_specification, certify_for_marketplace=certify_for_marketplace, tags=tags, model_approval_status=model_approval_status, metadata_properties=metadata_properties, model_metrics=model_metrics, customer_metadata_properties=customer_metadata_properties, drift_check_baselines=drift_check_baselines, domain=domain, task=task, sample_payload_url=sample_payload_url, additional_inference_specifications=additional_inference_specifications, client_token=client_token, ) return json.dumps(dict(ModelPackageArn=model_package_arn)) def create_model_package_group(self) -> str: model_package_group_name = self._get_param("ModelPackageGroupName") model_package_group_description = self._get_param( "ModelPackageGroupDescription" ) tags = self._get_param("Tags") model_package_group_arn = self.sagemaker_backend.create_model_package_group( model_package_group_name=model_package_group_name, model_package_group_description=model_package_group_description, tags=tags, ) return json.dumps(dict(ModelPackageGroupArn=model_package_group_arn))