def test_splits_filter( self, mock_pipeline_service_create, mock_pipeline_service_get, mock_dataset_image, mock_model_service_get, mock_model, sync, ): """ Initiate aiplatform with encryption key name. Create and run an AutoML Video Classification training job, verify calls and return value """ aiplatform.init( project=_TEST_PROJECT, encryption_spec_key_name=_TEST_DEFAULT_ENCRYPTION_KEY_NAME, ) job = training_jobs.AutoMLImageTrainingJob( display_name=_TEST_DISPLAY_NAME, base_model=mock_model ) model_from_job = job.run( dataset=mock_dataset_image, model_display_name=_TEST_MODEL_DISPLAY_NAME, training_filter_split=_TEST_FILTER_SPLIT_TRAINING, validation_filter_split=_TEST_FILTER_SPLIT_VALIDATION, test_filter_split=_TEST_FILTER_SPLIT_TEST, budget_milli_node_hours=_TEST_TRAINING_BUDGET_MILLI_NODE_HOURS, disable_early_stopping=_TEST_TRAINING_DISABLE_EARLY_STOPPING, sync=sync, ) if not sync: model_from_job.wait() true_filter_split = gca_training_pipeline.FilterSplit( training_filter=_TEST_FILTER_SPLIT_TRAINING, validation_filter=_TEST_FILTER_SPLIT_VALIDATION, test_filter=_TEST_FILTER_SPLIT_TEST, ) true_managed_model = gca_model.Model( display_name=_TEST_MODEL_DISPLAY_NAME, description=mock_model._gca_resource.description, encryption_spec=_TEST_DEFAULT_ENCRYPTION_SPEC, ) true_input_data_config = gca_training_pipeline.InputDataConfig( filter_split=true_filter_split, dataset_id=mock_dataset_image.name, ) true_training_pipeline = gca_training_pipeline.TrainingPipeline( display_name=_TEST_DISPLAY_NAME, training_task_definition=schema.training_job.definition.automl_image_classification, training_task_inputs=_TEST_TRAINING_TASK_INPUTS_WITH_BASE_MODEL, model_to_upload=true_managed_model, input_data_config=true_input_data_config, encryption_spec=_TEST_DEFAULT_ENCRYPTION_SPEC, ) mock_pipeline_service_create.assert_called_once_with( parent=initializer.global_config.common_location_path(), training_pipeline=true_training_pipeline, )
def test_splits_filter( self, mock_pipeline_service_create, mock_pipeline_service_get, mock_dataset_text, mock_model_service_get, mock_model, sync, ): """ Initiate aiplatform with encryption key name. Create and run an AutoML Video Classification training job, verify calls and return value """ aiplatform.init( project=_TEST_PROJECT, encryption_spec_key_name=_TEST_DEFAULT_ENCRYPTION_KEY_NAME, ) job = training_jobs.AutoMLTextTrainingJob( display_name=_TEST_DISPLAY_NAME, prediction_type=_TEST_PREDICTION_TYPE_CLASSIFICATION, multi_label=_TEST_CLASSIFICATION_MULTILABEL, ) model_from_job = job.run( dataset=mock_dataset_text, model_display_name=_TEST_MODEL_DISPLAY_NAME, training_filter_split=_TEST_FILTER_SPLIT_TRAINING, validation_filter_split=_TEST_FILTER_SPLIT_VALIDATION, test_filter_split=_TEST_FILTER_SPLIT_TEST, sync=sync, create_request_timeout=None, ) if not sync: model_from_job.wait() true_filter_split = gca_training_pipeline.FilterSplit( training_filter=_TEST_FILTER_SPLIT_TRAINING, validation_filter=_TEST_FILTER_SPLIT_VALIDATION, test_filter=_TEST_FILTER_SPLIT_TEST, ) true_managed_model = gca_model.Model( display_name=_TEST_MODEL_DISPLAY_NAME, description=mock_model._gca_resource.description, encryption_spec=_TEST_DEFAULT_ENCRYPTION_SPEC, ) true_input_data_config = gca_training_pipeline.InputDataConfig( filter_split=true_filter_split, dataset_id=mock_dataset_text.name, ) true_training_pipeline = gca_training_pipeline.TrainingPipeline( display_name=_TEST_DISPLAY_NAME, training_task_definition=schema.training_job.definition. automl_text_classification, training_task_inputs=_TEST_TRAINING_TASK_INPUTS_CLASSIFICATION, model_to_upload=true_managed_model, input_data_config=true_input_data_config, encryption_spec=_TEST_DEFAULT_ENCRYPTION_SPEC, ) mock_pipeline_service_create.assert_called_once_with( parent=initializer.global_config.common_location_path(), training_pipeline=true_training_pipeline, timeout=None, )
def test_run_call_pipeline_service_create( self, mock_pipeline_service_create, mock_pipeline_service_get, mock_dataset_image, mock_model_service_get, mock_model, sync, ): """Create and run an AutoML ICN training job, verify calls and return value""" aiplatform.init( project=_TEST_PROJECT, encryption_spec_key_name=_TEST_DEFAULT_ENCRYPTION_KEY_NAME, ) job = training_jobs.AutoMLImageTrainingJob( display_name=_TEST_DISPLAY_NAME, base_model=mock_model, labels=_TEST_LABELS, ) model_from_job = job.run( dataset=mock_dataset_image, model_display_name=_TEST_MODEL_DISPLAY_NAME, model_labels=_TEST_MODEL_LABELS, training_filter_split=_TEST_FILTER_SPLIT_TRAINING, validation_filter_split=_TEST_FILTER_SPLIT_VALIDATION, test_filter_split=_TEST_FILTER_SPLIT_TEST, budget_milli_node_hours=_TEST_TRAINING_BUDGET_MILLI_NODE_HOURS, disable_early_stopping=_TEST_TRAINING_DISABLE_EARLY_STOPPING, sync=sync, create_request_timeout=None, ) if not sync: model_from_job.wait() true_filter_split = gca_training_pipeline.FilterSplit( training_filter=_TEST_FILTER_SPLIT_TRAINING, validation_filter=_TEST_FILTER_SPLIT_VALIDATION, test_filter=_TEST_FILTER_SPLIT_TEST, ) true_managed_model = gca_model.Model( display_name=_TEST_MODEL_DISPLAY_NAME, labels=mock_model._gca_resource.labels, description=mock_model._gca_resource.description, encryption_spec=_TEST_DEFAULT_ENCRYPTION_SPEC, ) true_input_data_config = gca_training_pipeline.InputDataConfig( filter_split=true_filter_split, dataset_id=mock_dataset_image.name, ) true_training_pipeline = gca_training_pipeline.TrainingPipeline( display_name=_TEST_DISPLAY_NAME, labels=_TEST_LABELS, training_task_definition=schema.training_job.definition. automl_image_classification, training_task_inputs=_TEST_TRAINING_TASK_INPUTS_WITH_BASE_MODEL, model_to_upload=true_managed_model, input_data_config=true_input_data_config, encryption_spec=_TEST_DEFAULT_ENCRYPTION_SPEC, ) mock_pipeline_service_create.assert_called_once_with( parent=initializer.global_config.common_location_path(), training_pipeline=true_training_pipeline, timeout=None, ) mock_model_service_get.assert_called_once_with( name=_TEST_MODEL_NAME, retry=base._DEFAULT_RETRY) assert job._gca_resource is mock_pipeline_service_get.return_value assert model_from_job._gca_resource is mock_model_service_get.return_value assert job.get_model( )._gca_resource is mock_model_service_get.return_value assert not job.has_failed assert job.state == gca_pipeline_state.PipelineState.PIPELINE_STATE_SUCCEEDED
def test_run_call_pipeline_service_create_sentiment( self, mock_pipeline_service_create, mock_pipeline_service_get, mock_dataset_text, mock_model_service_get, sync, ): """Create and run an AutoML Text Sentiment training job, verify calls and return value""" aiplatform.init(project=_TEST_PROJECT) job = training_jobs.AutoMLTextTrainingJob( display_name=_TEST_DISPLAY_NAME, labels=_TEST_LABELS, prediction_type=_TEST_PREDICTION_TYPE_SENTIMENT, sentiment_max=10, ) model_from_job = job.run( dataset=mock_dataset_text, model_display_name=_TEST_MODEL_DISPLAY_NAME, model_labels=_TEST_MODEL_LABELS, training_filter_split=_TEST_FILTER_SPLIT_TRAINING, validation_filter_split=_TEST_FILTER_SPLIT_VALIDATION, test_filter_split=_TEST_FILTER_SPLIT_TEST, sync=sync, create_request_timeout=None, ) if not sync: model_from_job.wait() true_filter_split = gca_training_pipeline.FilterSplit( training_filter=_TEST_FILTER_SPLIT_TRAINING, validation_filter=_TEST_FILTER_SPLIT_VALIDATION, test_filter=_TEST_FILTER_SPLIT_TEST, ) true_managed_model = gca_model.Model( display_name=_TEST_MODEL_DISPLAY_NAME, labels=_TEST_MODEL_LABELS) true_input_data_config = gca_training_pipeline.InputDataConfig( filter_split=true_filter_split, dataset_id=mock_dataset_text.name, ) true_training_pipeline = gca_training_pipeline.TrainingPipeline( display_name=_TEST_DISPLAY_NAME, labels=_TEST_LABELS, training_task_definition=schema.training_job.definition. automl_text_sentiment, training_task_inputs=_TEST_TRAINING_TASK_INPUTS_SENTIMENT, model_to_upload=true_managed_model, input_data_config=true_input_data_config, ) mock_pipeline_service_create.assert_called_once_with( parent=initializer.global_config.common_location_path(), training_pipeline=true_training_pipeline, timeout=None, ) mock_model_service_get.assert_called_once_with( name=_TEST_MODEL_NAME, retry=base._DEFAULT_RETRY) assert job._gca_resource is mock_pipeline_service_get.return_value assert model_from_job._gca_resource is mock_model_service_get.return_value assert job.get_model( )._gca_resource is mock_model_service_get.return_value assert not job.has_failed assert job.state == gca_pipeline_state.PipelineState.PIPELINE_STATE_SUCCEEDED
def test_run_call_pipeline_service_create_with_timeout( self, mock_pipeline_service_create, mock_pipeline_service_get, mock_dataset_video, mock_model_service_get, mock_model, sync, ): """Create and run an AutoML ICN training job, verify calls and return value""" aiplatform.init(project=_TEST_PROJECT) job = training_jobs.AutoMLVideoTrainingJob( display_name=_TEST_DISPLAY_NAME, labels=_TEST_LABELS, prediction_type=_TEST_PREDICTION_TYPE_VCN, model_type=_TEST_MODEL_TYPE_CLOUD, training_encryption_spec_key_name= _TEST_PIPELINE_ENCRYPTION_KEY_NAME, model_encryption_spec_key_name=_TEST_MODEL_ENCRYPTION_KEY_NAME, ) model_from_job = job.run( dataset=mock_dataset_video, model_display_name=_TEST_MODEL_DISPLAY_NAME, model_labels=_TEST_MODEL_LABELS, training_filter_split=_TEST_FILTER_SPLIT_TRAINING, test_filter_split=_TEST_FILTER_SPLIT_TEST, sync=sync, create_request_timeout=180.0, ) if not sync: model_from_job.wait() true_filter_split = gca_training_pipeline.FilterSplit( training_filter=_TEST_FILTER_SPLIT_TRAINING, validation_filter=_TEST_FILTER_SPLIT_VALIDATION, test_filter=_TEST_FILTER_SPLIT_TEST, ) true_managed_model = gca_model.Model( display_name=_TEST_MODEL_DISPLAY_NAME, labels=_TEST_MODEL_LABELS, description=mock_model._gca_resource.description, encryption_spec=_TEST_MODEL_ENCRYPTION_SPEC, ) true_input_data_config = gca_training_pipeline.InputDataConfig( filter_split=true_filter_split, dataset_id=mock_dataset_video.name, ) true_training_pipeline = gca_training_pipeline.TrainingPipeline( display_name=_TEST_DISPLAY_NAME, labels=_TEST_LABELS, training_task_definition=schema.training_job.definition. automl_video_classification, training_task_inputs=_TEST_TRAINING_TASK_INPUTS, model_to_upload=true_managed_model, input_data_config=true_input_data_config, encryption_spec=_TEST_PIPELINE_ENCRYPTION_SPEC, ) mock_pipeline_service_create.assert_called_once_with( parent=initializer.global_config.common_location_path(), training_pipeline=true_training_pipeline, timeout=180.0, )