def apply(self, deployment_pb, repo, prev_deployment=None): deployment_spec = deployment_pb.spec gcp_config = deployment_spec.gcp_function_operator_config bento_path = repo.get(deployment_spec.bento_name, deployment_spec.bento_version) bento_config = load_bentoml_config(bento_path) with TemporaryServerlessContent( archive_path=bento_path, deployment_name=deployment_pb.name, bento_name=deployment_spec.bento_name, template_type='google-python', ) as output_path: generate_main_py(bento_config['name'], bento_config['apis'], output_path) generate_serverless_configuration_for_gcp_function( service_name=bento_config['name'], apis=bento_config['apis'], output_path=output_path, region=gcp_config.region, stage=deployment_pb.namespace, ) call_serverless_command(["deploy"], output_path) res_deployment_pb = Deployment(state=DeploymentState()) res_deployment_pb.CopyFrom(deployment_pb) state = self.describe(res_deployment_pb, repo).state res_deployment_pb.state.CopyFrom(state) return ApplyDeploymentResponse(status=Status.OK(), deployment=res_deployment_pb)
def _apply(self, deployment_pb, bento_pb, yatai_service, bento_path): if loader._is_remote_path(bento_path): with loader._resolve_remote_bundle_path(bento_path) as local_path: return self._apply(deployment_pb, bento_pb, yatai_service, local_path) deployment_spec = deployment_pb.spec aws_config = deployment_spec.aws_lambda_operator_config bento_service_metadata = bento_pb.bento.bento_service_metadata template = 'aws-python3' if version.parse(bento_service_metadata.env.python_version ) < version.parse('3.0.0'): template = 'aws-python' api_names = ([aws_config.api_name] if aws_config.api_name else [api.name for api in bento_service_metadata.apis]) ensure_deploy_api_name_exists_in_bento( [api.name for api in bento_service_metadata.apis], api_names) with TempDirectory() as serverless_project_dir: init_serverless_project_dir( serverless_project_dir, bento_path, deployment_pb.name, deployment_spec.bento_name, template, ) generate_aws_lambda_handler_py(deployment_spec.bento_name, api_names, serverless_project_dir) generate_aws_lambda_serverless_config( bento_service_metadata.env.python_version, deployment_pb.name, api_names, serverless_project_dir, aws_config.region, # BentoML deployment namespace is mapping to serverless `stage` # concept stage=deployment_pb.namespace, ) logger.info( 'Installing additional packages: serverless-python-requirements' ) install_serverless_plugin("serverless-python-requirements", serverless_project_dir) logger.info('Deploying to AWS Lambda') call_serverless_command(["deploy"], serverless_project_dir) res_deployment_pb = Deployment(state=DeploymentState()) res_deployment_pb.CopyFrom(deployment_pb) state = self.describe(res_deployment_pb, yatai_service).state res_deployment_pb.state.CopyFrom(state) return ApplyDeploymentResponse(status=Status.OK(), deployment=res_deployment_pb)
def apply(self, deployment_pb, repo): # deploy code..... spec = deployment_pb.spec bento_path = repo.get(spec.bento_name, spec.bento_version) # config = load_bentoml_config(bento_path)... res_deployment_pb = Deployment() res_deployment_pb.CopyFrom(deployment_pb) # res_deployment_pb.state = ... return ApplyDeploymentResponse(status=Status.OK(), deployment=res_deployment_pb)
def apply(self, deployment_pb, yatai_service, prev_deployment=None): try: deployment_spec = deployment_pb.spec gcp_config = deployment_spec.gcp_function_operator_config bento_pb = yatai_service.GetBento( GetBentoRequest( bento_name=deployment_spec.bento_name, bento_version=deployment_spec.bento_version, )) if bento_pb.bento.uri.type != BentoUri.LOCAL: raise BentoMLException( 'BentoML currently only support local repository') else: bento_path = bento_pb.bento.uri.uri bento_service_metadata = bento_pb.bento.bento_service_metadata api_names = ([gcp_config.api_name] if gcp_config.api_name else [api.name for api in bento_service_metadata.apis]) ensure_deploy_api_name_exists_in_bento( [api.name for api in bento_service_metadata.apis], api_names) with TempDirectory() as serverless_project_dir: init_serverless_project_dir( serverless_project_dir, bento_path, deployment_pb.name, deployment_spec.bento_name, 'google-python', ) generate_gcp_function_main_py(deployment_spec.bento_name, api_names, serverless_project_dir) generate_gcp_function_serverless_config( deployment_pb.name, api_names, serverless_project_dir, gcp_config.region, # BentoML namespace is mapping to serverless stage. stage=deployment_pb.namespace, ) call_serverless_command(["deploy"], serverless_project_dir) res_deployment_pb = Deployment(state=DeploymentState()) res_deployment_pb.CopyFrom(deployment_pb) state = self.describe(res_deployment_pb, yatai_service).state res_deployment_pb.state.CopyFrom(state) return ApplyDeploymentResponse(status=Status.OK(), deployment=res_deployment_pb) except BentoMLException as error: return ApplyDeploymentResponse( status=exception_to_return_status(error))
def apply(self, deployment_pb, repo, prev_deployment=None): ensure_docker_available_or_raise() deployment_spec = deployment_pb.spec aws_config = deployment_spec.aws_lambda_operator_config bento_path = repo.get(deployment_spec.bento_name, deployment_spec.bento_version) bento_config = load_bentoml_config(bento_path) template = 'aws-python3' minimum_python_version = version.parse('3.0.0') bento_python_version = version.parse( bento_config['env']['python_version']) if bento_python_version < minimum_python_version: template = 'aws-python' with TemporaryServerlessContent( archive_path=bento_path, deployment_name=deployment_pb.name, bento_name=deployment_spec.bento_name, template_type=template, ) as output_path: generate_handler_py(deployment_spec.bento_name, bento_config['apis'], output_path) generate_serverless_configuration_for_aws_lambda( service_name=deployment_pb.name, apis=bento_config['apis'], output_path=output_path, region=aws_config.region, stage=deployment_pb.namespace, ) logger.info( 'Installing additional packages: serverless-python-requirements, ' 'serverless-apigw-binary') install_serverless_plugin("serverless-python-requirements", output_path) install_serverless_plugin("serverless-apigw-binary", output_path) logger.info('Deploying to AWS Lambda') call_serverless_command(["deploy"], output_path) res_deployment_pb = Deployment(state=DeploymentState()) res_deployment_pb.CopyFrom(deployment_pb) state = self.describe(res_deployment_pb, repo).state res_deployment_pb.state.CopyFrom(state) return ApplyDeploymentResponse(status=Status.OK(), deployment=res_deployment_pb)
def apply(self, deployment_pb, repo, prev_deployment=None): deployment_spec = deployment_pb.spec sagemaker_config = deployment_spec.sagemaker_operator_config if sagemaker_config is None: raise BentoMLDeploymentException('Sagemaker configuration is missing.') archive_path = repo.get( deployment_spec.bento_name, deployment_spec.bento_version ) # config = load_bentoml_config(bento_path)... sagemaker_client = boto3.client('sagemaker', sagemaker_config.region) with TemporarySageMakerContent( archive_path, deployment_spec.bento_name, deployment_spec.bento_version ) as temp_path: ecr_image_path = create_push_image_to_ecr( deployment_spec.bento_name, deployment_spec.bento_version, temp_path ) execution_role_arn = get_arn_role_from_current_user() model_name = create_sagemaker_model_name( deployment_spec.bento_name, deployment_spec.bento_version ) sagemaker_model_info = { "ModelName": model_name, "PrimaryContainer": { "ContainerHostname": model_name, "Image": ecr_image_path, "Environment": { "API_NAME": sagemaker_config.api_name, "BENTO_SERVER_TIMEOUT": config().get( 'apiserver', 'default_timeout' ), "BENTO_SERVER_WORKERS": config().get( 'apiserver', 'default_gunicorn_workers_count' ), }, }, "ExecutionRoleArn": execution_role_arn, } logger.info("Creating sagemaker model %s", model_name) create_model_response = sagemaker_client.create_model(**sagemaker_model_info) logger.debug("AWS create model response: %s", create_model_response) production_variants = [ { "VariantName": generate_aws_compatible_string( deployment_spec.bento_name ), "ModelName": model_name, "InitialInstanceCount": sagemaker_config.instance_count, "InstanceType": sagemaker_config.instance_type, } ] endpoint_config_name = create_sagemaker_endpoint_config_name( deployment_spec.bento_name, deployment_spec.bento_version ) logger.info( "Creating Sagemaker endpoint %s configuration", endpoint_config_name ) create_endpoint_config_response = sagemaker_client.create_endpoint_config( EndpointConfigName=endpoint_config_name, ProductionVariants=production_variants, ) logger.debug( "AWS create endpoint config response: %s", create_endpoint_config_response ) endpoint_name = generate_aws_compatible_string( deployment_pb.namespace + '-' + deployment_spec.bento_name ) if prev_deployment: logger.info("Updating sagemaker endpoint %s", endpoint_name) update_endpoint_response = sagemaker_client.update_endpoint( EndpointName=endpoint_name, EndpointConfigName=endpoint_config_name ) logger.debug("AWS update endpoint response: %s", update_endpoint_response) else: logger.info("Creating sagemaker endpoint %s", endpoint_name) create_endpoint_response = sagemaker_client.create_endpoint( EndpointName=endpoint_name, EndpointConfigName=endpoint_config_name ) logger.debug("AWS create endpoint response: %s", create_endpoint_response) res_deployment_pb = Deployment(state=DeploymentState()) res_deployment_pb.CopyFrom(deployment_pb) return ApplyDeploymentResponse(status=Status.OK(), deployment=res_deployment_pb)
def apply(self, deployment_pb, yatai_service, prev_deployment=None): try: ensure_docker_available_or_raise() deployment_spec = deployment_pb.spec sagemaker_config = deployment_spec.sagemaker_operator_config if sagemaker_config is None: raise BentoMLDeploymentException('Sagemaker configuration is missing.') bento_pb = yatai_service.GetBento( GetBentoRequest( bento_name=deployment_spec.bento_name, bento_version=deployment_spec.bento_version, ) ) if bento_pb.bento.uri.type != BentoUri.LOCAL: raise BentoMLException( 'BentoML currently only support local repository' ) else: bento_path = bento_pb.bento.uri.uri ensure_deploy_api_name_exists_in_bento( [api.name for api in bento_pb.bento.bento_service_metadata.apis], [sagemaker_config.api_name], ) sagemaker_client = boto3.client('sagemaker', sagemaker_config.region) with TempDirectory() as temp_dir: sagemaker_project_dir = os.path.jon( temp_dir, deployment_spec.bento_name ) init_sagemaker_project(sagemaker_project_dir, bento_path) ecr_image_path = create_push_docker_image_to_ecr( deployment_spec.bento_name, deployment_spec.bento_version, sagemaker_project_dir, ) execution_role_arn = get_arn_role_from_current_aws_user() model_name = create_sagemaker_model_name( deployment_spec.bento_name, deployment_spec.bento_version ) sagemaker_model_info = { "ModelName": model_name, "PrimaryContainer": { "ContainerHostname": model_name, "Image": ecr_image_path, "Environment": { "API_NAME": sagemaker_config.api_name, "BENTO_SERVER_TIMEOUT": config().get( 'apiserver', 'default_timeout' ), "BENTO_SERVER_WORKERS": config().get( 'apiserver', 'default_gunicorn_workers_count' ), }, }, "ExecutionRoleArn": execution_role_arn, } logger.info("Creating sagemaker model %s", model_name) try: create_model_response = sagemaker_client.create_model( **sagemaker_model_info ) logger.debug("AWS create model response: %s", create_model_response) except ClientError as e: status = _parse_aws_client_exception_or_raise(e) status.error_message = ( 'Failed to create model for SageMaker Deployment: %s', status.error_message, ) return ApplyDeploymentResponse(status=status, deployment=deployment_pb) production_variants = [ { "VariantName": generate_aws_compatible_string( deployment_spec.bento_name ), "ModelName": model_name, "InitialInstanceCount": sagemaker_config.instance_count, "InstanceType": sagemaker_config.instance_type, } ] endpoint_config_name = create_sagemaker_endpoint_config_name( deployment_spec.bento_name, deployment_spec.bento_version ) logger.info( "Creating Sagemaker endpoint %s configuration", endpoint_config_name ) try: create_config_response = sagemaker_client.create_endpoint_config( EndpointConfigName=endpoint_config_name, ProductionVariants=production_variants, ) logger.debug( "AWS create endpoint config response: %s", create_config_response ) except ClientError as e: # create endpoint failed, will remove previously created model cleanup_model_error = _cleanup_sagemaker_model( sagemaker_client, deployment_spec.bento_name, deployment_spec.bento_version, ) if cleanup_model_error: cleanup_model_error.error_message = ( 'Failed to clean up model after unsuccessfully ' 'create endpoint config: %s', cleanup_model_error.error_message, ) return ApplyDeploymentResponse( status=cleanup_model_error, deployment=deployment_pb ) status = _parse_aws_client_exception_or_raise(e) status.error_message = ( 'Failed to create endpoint config for SageMaker deployment: %s', status.error_message, ) return ApplyDeploymentResponse(status=status, deployment=deployment_pb) endpoint_name = generate_aws_compatible_string( deployment_pb.namespace + '-' + deployment_spec.bento_name ) try: if prev_deployment: logger.debug("Updating sagemaker endpoint %s", endpoint_name) update_endpoint_response = sagemaker_client.update_endpoint( EndpointName=endpoint_name, EndpointConfigName=endpoint_config_name, ) logger.debug( "AWS update endpoint response: %s", update_endpoint_response ) else: logger.debug("Creating sagemaker endpoint %s", endpoint_name) create_endpoint_response = sagemaker_client.create_endpoint( EndpointName=endpoint_name, EndpointConfigName=endpoint_config_name, ) logger.debug( "AWS create endpoint response: %s", create_endpoint_response ) except ClientError as e: # create/update endpoint failed, will remove previously created config # and then remove the model cleanup_endpoint_config_error = _cleanup_sagemaker_endpoint_config( client=sagemaker_client, name=deployment_spec.bento_name, version=deployment_spec.bento_version, ) if cleanup_endpoint_config_error: cleanup_endpoint_config_error.error_message = ( 'Failed to clean up endpoint config after unsuccessfully ' 'apply SageMaker deployment: %s', cleanup_endpoint_config_error.error_message, ) return ApplyDeploymentResponse( status=cleanup_endpoint_config_error, deployment=deployment_pb ) cleanup_model_error = _cleanup_sagemaker_model( client=sagemaker_client, name=deployment_spec.bento_name, version=deployment_spec.bento_version, ) if cleanup_model_error: cleanup_model_error.error_message = ( 'Failed to clean up model after unsuccessfully ' 'apply SageMaker deployment: %s', cleanup_model_error.error_message, ) return ApplyDeploymentResponse( status=cleanup_model_error, deployment=deployment_pb ) status = _parse_aws_client_exception_or_raise(e) status.error_message = ( 'Failed to apply SageMaker deployment: %s', status.error_message, ) return ApplyDeploymentResponse(status=status, deployment=deployment_pb) res_deployment_pb = Deployment(state=DeploymentState()) res_deployment_pb.CopyFrom(deployment_pb) return ApplyDeploymentResponse( status=Status.OK(), deployment=res_deployment_pb ) except BentoMLException as error: return ApplyDeploymentResponse(status=exception_to_return_status(error))
def _apply( self, deployment_pb, bento_pb, yatai_service, bento_path, prev_deployment=None ): if loader._is_remote_path(bento_path): with loader._resolve_remote_bundle_path(bento_path) as local_path: return self._apply( deployment_pb, bento_pb, yatai_service, local_path, prev_deployment ) deployment_spec = deployment_pb.spec sagemaker_config = deployment_spec.sagemaker_operator_config ensure_deploy_api_name_exists_in_bento( [api.name for api in bento_pb.bento.bento_service_metadata.apis], [sagemaker_config.api_name], ) sagemaker_client = boto3.client('sagemaker', sagemaker_config.region) with TempDirectory() as temp_dir: sagemaker_project_dir = os.path.join(temp_dir, deployment_spec.bento_name) init_sagemaker_project(sagemaker_project_dir, bento_path) ecr_image_path = create_push_docker_image_to_ecr( sagemaker_config.region, deployment_spec.bento_name, deployment_spec.bento_version, sagemaker_project_dir, ) try: model_name = _create_sagemaker_model( sagemaker_client, deployment_spec.bento_name, deployment_spec.bento_version, ecr_image_path, sagemaker_config.api_name, ) except ClientError as e: status = _parse_aws_client_exception(e) status.error_message = ( 'Failed to create model for SageMaker' ' Deployment: {}'.format(status.error_message) ) return ApplyDeploymentResponse(status=status, deployment=deployment_pb) try: endpoint_config_name = _create_sagemaker_endpoint_config( sagemaker_client, model_name, deployment_spec.bento_name, deployment_spec.bento_version, sagemaker_config, ) except ClientError as e: # create endpoint failed, will remove previously created model cleanup_model_error = _cleanup_sagemaker_model( sagemaker_client, deployment_spec.bento_name, deployment_spec.bento_version, ) if cleanup_model_error: cleanup_model_error.error_message = ( 'Failed to clean up model after unsuccessfully ' 'create endpoint config: %s', cleanup_model_error.error_message, ) return ApplyDeploymentResponse( status=cleanup_model_error, deployment=deployment_pb ) status = _parse_aws_client_exception(e) status.error_message = ( 'Failed to create endpoint config for SageMaker deployment: %s', status.error_message, ) return ApplyDeploymentResponse(status=status, deployment=deployment_pb) endpoint_name = generate_aws_compatible_string( deployment_pb.namespace + '-' + deployment_spec.bento_name ) try: if prev_deployment: logger.debug("Updating sagemaker endpoint %s", endpoint_name) update_endpoint_response = sagemaker_client.update_endpoint( EndpointName=endpoint_name, EndpointConfigName=endpoint_config_name ) logger.debug( "AWS update endpoint response: %s", update_endpoint_response ) else: logger.debug("Creating sagemaker endpoint %s", endpoint_name) create_endpoint_response = sagemaker_client.create_endpoint( EndpointName=endpoint_name, EndpointConfigName=endpoint_config_name ) logger.debug( "AWS create endpoint response: %s", create_endpoint_response ) except ClientError as e: # create/update endpoint failed, will remove previously created config # and then remove the model cleanup_endpoint_config_error = _cleanup_sagemaker_endpoint_config( client=sagemaker_client, name=deployment_spec.bento_name, version=deployment_spec.bento_version, ) if cleanup_endpoint_config_error: cleanup_endpoint_config_error.error_message = ( 'Failed to clean up endpoint config after unsuccessfully ' 'apply SageMaker deployment: %s', cleanup_endpoint_config_error.error_message, ) return ApplyDeploymentResponse( status=cleanup_endpoint_config_error, deployment=deployment_pb ) cleanup_model_error = _cleanup_sagemaker_model( client=sagemaker_client, name=deployment_spec.bento_name, version=deployment_spec.bento_version, ) if cleanup_model_error: cleanup_model_error.error_message = ( 'Failed to clean up model after unsuccessfully apply ' 'SageMaker deployment: {}'.format(cleanup_model_error.error_message) ) return ApplyDeploymentResponse( status=cleanup_model_error, deployment=deployment_pb ) status = _parse_aws_client_exception(e) status.error_message = 'Failed to apply SageMaker ' 'deployment: {}'.format( status.error_message ) return ApplyDeploymentResponse(status=status, deployment=deployment_pb) res_deployment_pb = Deployment(state=DeploymentState()) res_deployment_pb.CopyFrom(deployment_pb) return ApplyDeploymentResponse(status=Status.OK(), deployment=res_deployment_pb)
def apply(self, deployment_pb, yatai_service, prev_deployment=None): try: ensure_docker_available_or_raise() deployment_spec = deployment_pb.spec aws_config = deployment_spec.aws_lambda_operator_config bento_pb = yatai_service.GetBento( GetBentoRequest( bento_name=deployment_spec.bento_name, bento_version=deployment_spec.bento_version, ) ) if bento_pb.bento.uri.type != BentoUri.LOCAL: raise BentoMLException( 'BentoML currently only support local repository' ) else: bento_path = bento_pb.bento.uri.uri bento_service_metadata = bento_pb.bento.bento_service_metadata template = 'aws-python3' if version.parse(bento_service_metadata.env.python_version) < version.parse( '3.0.0' ): template = 'aws-python' api_names = ( [aws_config.api_name] if aws_config.api_name else [api.name for api in bento_service_metadata.apis] ) ensure_deploy_api_name_exists_in_bento( [api.name for api in bento_service_metadata.apis], api_names ) with TempDirectory() as serverless_project_dir: init_serverless_project_dir( serverless_project_dir, bento_path, deployment_pb.name, deployment_spec.bento_name, template, ) generate_aws_lambda_handler_py( deployment_spec.bento_name, api_names, serverless_project_dir ) generate_aws_lambda_serverless_config( bento_service_metadata.env.python_version, deployment_pb.name, api_names, serverless_project_dir, aws_config.region, # BentoML deployment namespace is mapping to serverless `stage` # concept stage=deployment_pb.namespace, ) logger.info( 'Installing additional packages: serverless-python-requirements' ) install_serverless_plugin( "serverless-python-requirements", serverless_project_dir ) logger.info('Deploying to AWS Lambda') call_serverless_command(["deploy"], serverless_project_dir) res_deployment_pb = Deployment(state=DeploymentState()) res_deployment_pb.CopyFrom(deployment_pb) state = self.describe(res_deployment_pb, yatai_service).state res_deployment_pb.state.CopyFrom(state) return ApplyDeploymentResponse( status=Status.OK(), deployment=res_deployment_pb ) except BentoMLException as error: return ApplyDeploymentResponse(status=exception_to_return_status(error))