def sample_get_model(): # Create a client client = aiplatform_v1beta1.ModelServiceClient() # Initialize request argument(s) request = aiplatform_v1beta1.GetModelRequest(name="name_value", ) # Make the request response = client.get_model(request=request) # Handle the response print(response)
def sample_import_model_evaluation(): # Create a client client = aiplatform_v1beta1.ModelServiceClient() # Initialize request argument(s) request = aiplatform_v1beta1.ImportModelEvaluationRequest( parent="parent_value", ) # Make the request response = client.import_model_evaluation(request=request) # Handle the response print(response)
def sample_list_model_evaluations(): # Create a client client = aiplatform_v1beta1.ModelServiceClient() # Initialize request argument(s) request = aiplatform_v1beta1.ListModelEvaluationsRequest( parent="parent_value", ) # Make the request page_result = client.list_model_evaluations(request=request) # Handle the response for response in page_result: print(response)
def sample_update_model(): # Create a client client = aiplatform_v1beta1.ModelServiceClient() # Initialize request argument(s) model = aiplatform_v1beta1.Model() model.display_name = "display_name_value" request = aiplatform_v1beta1.UpdateModelRequest(model=model, ) # Make the request response = client.update_model(request=request) # Handle the response print(response)
def sample_delete_model(): # Create a client client = aiplatform_v1beta1.ModelServiceClient() # Initialize request argument(s) request = aiplatform_v1beta1.DeleteModelRequest(name="name_value", ) # Make the request operation = client.delete_model(request=request) print("Waiting for operation to complete...") response = operation.result() # Handle the response print(response)
def sample_upload_model(): # Create a client client = aiplatform_v1beta1.ModelServiceClient() # Initialize request argument(s) model = aiplatform_v1beta1.Model() model.display_name = "display_name_value" request = aiplatform_v1beta1.UploadModelRequest( parent="parent_value", model=model, ) # Make the request operation = client.upload_model(request=request) print("Waiting for operation to complete...") response = operation.result() # Handle the response print(response)
def export_model_tabular_classification_sample( project: str, model_id: str, gcs_destination_output_uri_prefix: str, location: str = "us-central1", api_endpoint: str = "us-central1-aiplatform.googleapis.com", timeout: int = 300, ): # The AI Platform services require regional API endpoints. client_options = {"api_endpoint": api_endpoint} # Initialize client that will be used to create and send requests. # This client only needs to be created once, and can be reused for multiple requests. client = aiplatform_v1beta1.ModelServiceClient(client_options=client_options) gcs_destination = {"output_uri_prefix": gcs_destination_output_uri_prefix} output_config = { "artifact_destination": gcs_destination, "export_format_id": "tf-saved-model", } name = client.model_path(project=project, location=location, model=model_id) response = client.export_model(name=name, output_config=output_config) print("Long running operation:", response.operation.name) print("output_info:", response.metadata.output_info) export_model_response = response.result(timeout=timeout) print("export_model_response:", export_model_response)
def upload_model_explain_tabular_managed_container_sample( project: str, display_name: str, container_spec_image_uri: str, artifact_uri: str, input_tensor_name: str, output_tensor_name: str, feature_names: list, location: str = "us-central1", api_endpoint: str = "us-central1-aiplatform.googleapis.com", timeout: int = 300, ): # The AI Platform services require regional API endpoints. client_options = {"api_endpoint": api_endpoint} # Initialize client that will be used to create and send requests. # This client only needs to be created once, and can be reused for multiple requests. client = aiplatform_v1beta1.ModelServiceClient( client_options=client_options) # Container specification for deploying the model container_spec = { "image_uri": container_spec_image_uri, "command": [], "args": [] } # The explainabilty method and corresponding parameters parameters = aiplatform_v1beta1.ExplanationParameters( {"xrai_attribution": { "step_count": 1 }}) # The input tensor for feature attribution to the output # For single input model, y = f(x), this will be the serving input layer. input_metadata = aiplatform_v1beta1.ExplanationMetadata.InputMetadata({ "input_tensor_name": input_tensor_name, # Input is tabular data "modality": "numeric", # Assign feature names to the inputs for explanation "encoding": "BAG_OF_FEATURES", "index_feature_mapping": feature_names, }) # The output tensor to explain # For single output model, y = f(x), this will be the serving output layer. output_metadata = aiplatform_v1beta1.ExplanationMetadata.OutputMetadata( {"output_tensor_name": output_tensor_name}) # Assemble the explanation metadata metadata = aiplatform_v1beta1.ExplanationMetadata( inputs={"features": input_metadata}, outputs={"prediction": output_metadata}) # Assemble the explanation specification explanation_spec = aiplatform_v1beta1.ExplanationSpec( parameters=parameters, metadata=metadata) model = aiplatform_v1beta1.Model( display_name=display_name, # The Cloud Storage location of the custom model artifact_uri=artifact_uri, explanation_spec=explanation_spec, container_spec=container_spec, ) parent = f"projects/{project}/locations/{location}" response = client.upload_model(parent=parent, model=model) print("Long running operation:", response.operation.name) upload_model_response = response.result(timeout=timeout) print("upload_model_response:", upload_model_response)