def delete(self, deployment_pb): try: logger.debug('Deleting AWS Lambda deployment') deployment_spec = deployment_pb.spec lambda_deployment_config = deployment_spec.aws_lambda_operator_config lambda_deployment_config.region = (lambda_deployment_config.region or get_default_aws_region()) if not lambda_deployment_config.region: raise InvalidArgument('AWS region is missing') cf_client = boto3.client('cloudformation', lambda_deployment_config.region) stack_name = generate_aws_compatible_string( deployment_pb.namespace, deployment_pb.name) if deployment_pb.state.info_json: deployment_info_json = json.loads( deployment_pb.state.info_json) bucket_name = deployment_info_json.get('s3_bucket') if bucket_name: _cleanup_s3_bucket_if_exist( bucket_name, lambda_deployment_config.region) logger.debug( 'Deleting AWS CloudFormation: %s that includes Lambda function ' 'and related resources', stack_name, ) cf_client.delete_stack(StackName=stack_name) return DeleteDeploymentResponse(status=Status.OK()) except BentoMLException as error: return DeleteDeploymentResponse(status=error.status_proto)
def _get_sagemaker_resource_names(deployment_pb): sagemaker_model_name = generate_aws_compatible_string( (deployment_pb.namespace, 10), (deployment_pb.name, 12), (deployment_pb.spec.bento_name, 20), (deployment_pb.spec.bento_version, 18), ) sagemaker_endpoint_config_name = generate_aws_compatible_string( (deployment_pb.namespace, 10), (deployment_pb.name, 12), (deployment_pb.spec.bento_name, 20), (deployment_pb.spec.bento_version, 18), ) sagemaker_endpoint_name = generate_aws_compatible_string( deployment_pb.namespace, deployment_pb.name) return sagemaker_model_name, sagemaker_endpoint_config_name, sagemaker_endpoint_name
def _add(self, deployment_pb, bento_pb, bento_path): if loader._is_remote_path(bento_path): with loader._resolve_remote_bundle_path(bento_path) as local_path: return self._add(deployment_pb, bento_pb, local_path) deployment_spec = deployment_pb.spec lambda_deployment_config = deployment_spec.aws_lambda_operator_config bento_service_metadata = bento_pb.bento.bento_service_metadata lambda_s3_bucket = generate_aws_compatible_string( 'btml-{namespace}-{name}-{random_string}'.format( namespace=deployment_pb.namespace, name=deployment_pb.name, random_string=uuid.uuid4().hex[:6].lower(), ) ) try: create_s3_bucket_if_not_exists( lambda_s3_bucket, lambda_deployment_config.region ) _deploy_lambda_function( deployment_pb=deployment_pb, bento_service_metadata=bento_service_metadata, deployment_spec=deployment_spec, lambda_s3_bucket=lambda_s3_bucket, lambda_deployment_config=lambda_deployment_config, bento_path=bento_path, ) return ApplyDeploymentResponse(status=Status.OK(), deployment=deployment_pb) except BentoMLException as error: if lambda_s3_bucket and lambda_deployment_config: _cleanup_s3_bucket_if_exist( lambda_s3_bucket, lambda_deployment_config.region ) raise error
def _add(self, deployment_pb, bento_pb, bento_path): if loader._is_remote_path(bento_path): with loader._resolve_remote_bundle_path(bento_path) as local_path: return self._add(deployment_pb, bento_pb, local_path) deployment_spec = deployment_pb.spec lambda_deployment_config = deployment_spec.aws_lambda_operator_config bento_service_metadata = bento_pb.bento.bento_service_metadata lambda_s3_bucket = generate_aws_compatible_string( 'btml-{namespace}-{name}-{random_string}'.format( namespace=deployment_pb.namespace, name=deployment_pb.name, random_string=uuid.uuid4().hex[:6].lower(), )) try: py_major, py_minor, _ = bento_service_metadata.env.python_version.split( '.') if py_major != '3': raise BentoMLException( 'Python 2 is not supported for Lambda Deployment') python_runtime = 'python{}.{}'.format(py_major, py_minor) artifact_types = [ item.artifact_type for item in bento_service_metadata.artifacts ] if any(i in ['TensorflowSavedModelArtifact', 'KerasModelArtifact'] for i in artifact_types) and (py_major, py_minor) != ('3', '6'): raise BentoMLException( 'For Tensorflow and Keras model, only python3.6 is ' 'supported for AWS Lambda deployment') api_names = ([lambda_deployment_config.api_name] if lambda_deployment_config.api_name else [api.name for api in bento_service_metadata.apis]) raise_if_api_names_not_found_in_bento_service_metadata( bento_service_metadata, api_names) create_s3_bucket_if_not_exists(lambda_s3_bucket, lambda_deployment_config.region) deployment_path_prefix = os.path.join(deployment_pb.namespace, deployment_pb.name) with TempDirectory() as lambda_project_dir: logger.debug( 'Generating cloudformation template.yaml for lambda project at %s', lambda_project_dir, ) template_file_path = _create_aws_lambda_cloudformation_template_file( project_dir=lambda_project_dir, namespace=deployment_pb.namespace, deployment_name=deployment_pb.name, deployment_path_prefix=deployment_path_prefix, api_names=api_names, bento_service_name=deployment_spec.bento_name, s3_bucket_name=lambda_s3_bucket, py_runtime=python_runtime, memory_size=lambda_deployment_config.memory_size, timeout=lambda_deployment_config.timeout, ) logger.debug('Validating generated template.yaml') validate_lambda_template( template_file_path, lambda_deployment_config.region, lambda_project_dir, ) logger.debug( 'Initializing lambda project in directory: %s ...', lambda_project_dir, ) init_sam_project( lambda_project_dir, bento_path, deployment_pb.name, deployment_spec.bento_name, api_names, aws_region=lambda_deployment_config.region, ) for api_name in api_names: build_directory = os.path.join(lambda_project_dir, '.aws-sam', 'build', api_name) logger.debug( 'Checking is function "%s" bundle under lambda size ' 'limit', api_name, ) # Since we only use s3 get object in lambda function, and # lambda function pack their own boto3/botocore modules, # we will just delete those modules from function bundle # directory delete_list = ['boto3', 'botocore'] for name in delete_list: logger.debug('Remove module "%s" from build directory', name) shutil.rmtree(os.path.join(build_directory, name)) total_build_dir_size = total_file_or_directory_size( build_directory) if total_build_dir_size > LAMBDA_FUNCTION_MAX_LIMIT: raise BentoMLException( 'Build function size is over 700MB, max size ' 'capable for AWS Lambda function') if total_build_dir_size >= LAMBDA_FUNCTION_LIMIT: logger.debug( 'Function %s is over lambda size limit, attempting ' 'reduce it', api_name, ) reduce_bundle_size_and_upload_extra_resources_to_s3( build_directory=build_directory, region=lambda_deployment_config.region, s3_bucket=lambda_s3_bucket, deployment_prefix=deployment_path_prefix, function_name=api_name, lambda_project_dir=lambda_project_dir, ) else: logger.debug( 'Function bundle is within Lambda limit, removing ' 'download_extra_resources.py file from function bundle' ) os.remove( os.path.join(build_directory, 'download_extra_resources.py')) logger.info('Packaging AWS Lambda project at %s ...', lambda_project_dir) lambda_package( lambda_project_dir, lambda_deployment_config.region, lambda_s3_bucket, deployment_path_prefix, ) logger.info('Deploying lambda project') stack_name = generate_aws_compatible_string( deployment_pb.namespace + '-' + deployment_pb.name) lambda_deploy( lambda_project_dir, lambda_deployment_config.region, stack_name=stack_name, ) deployment_pb.state.state = DeploymentState.PENDING return ApplyDeploymentResponse(status=Status.OK(), deployment=deployment_pb) except BentoMLException as error: if lambda_s3_bucket and lambda_deployment_config: _cleanup_s3_bucket_if_exist(lambda_s3_bucket, lambda_deployment_config.region) raise error