__init__.py

# Copyright 2019 Atalaya Tech, Inc.

# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at

# http://www.apache.org/licenses/LICENSE-2.0

# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import json
import os
import logging
import shutil
import uuid
from pathlib import Path

from botocore.exceptions import ClientError

import boto3
from ruamel.yaml import YAML

from bentoml.bundler import loader
from bentoml.deployment.aws_lambda.utils import (
    ensure_sam_available_or_raise,
    init_sam_project,
    lambda_deploy,
    lambda_package,
    validate_lambda_template,
    reduce_bundle_size_and_upload_extra_resources_to_s3,
    total_file_or_directory_size,
    LAMBDA_FUNCTION_LIMIT,
    LAMBDA_FUNCTION_MAX_LIMIT,
    FAILED_CLOUDFORMATION_STACK_STATUS,
)
from bentoml.deployment.operator import DeploymentOperatorBase
from bentoml.deployment.utils import (
    ensure_docker_available_or_raise,
    generate_aws_compatible_string,
    raise_if_api_names_not_found_in_bento_service_metadata,
)
from bentoml.exceptions import BentoMLException
from bentoml.proto.deployment_pb2 import (
    ApplyDeploymentResponse,
    DeploymentState,
    DescribeDeploymentResponse,
    DeleteDeploymentResponse,
)
from bentoml.proto.repository_pb2 import GetBentoRequest, BentoUri
from bentoml.utils.s3 import create_s3_bucket_if_not_exists
from bentoml.utils.tempdir import TempDirectory
from bentoml.yatai.status import Status


logger = logging.getLogger(__name__)


def _create_aws_lambda_cloudformation_template_file(
    project_dir,
    namespace,
    deployment_name,
    deployment_path_prefix,
    api_names,
    bento_service_name,
    s3_bucket_name,
    py_runtime,
    memory_size,
    timeout,
):
    template_file_path = os.path.join(project_dir, 'template.yaml')
    yaml = YAML()
    sam_config = {
        'AWSTemplateFormatVersion': '2010-09-09',
        'Transform': 'AWS::Serverless-2016-10-31',
        'Globals': {
            'Function': {'Timeout': timeout, 'Runtime': py_runtime},
            'Api': {
                'BinaryMediaTypes': ['image~1*'],
                'Cors': {'AllowOrigin': "'*'"},
                'Auth': {
                    'ApiKeyRequired': False,
                    'DefaultAuthorizer': 'NONE',
                    'AddDefaultAuthorizerToCorsPreflight': False,
                },
            },
        },
        'Resources': {},
        'Outputs': {
            'S3Bucket': {
                'Value': s3_bucket_name,
                'Description': 'S3 Bucket for saving artifacts and lambda bundle',
            }
        },
    }
    for api_name in api_names:
        sam_config['Resources'][api_name] = {
            'Type': 'AWS::Serverless::Function',
            'Properties': {
                'Runtime': py_runtime,
                'CodeUri': deployment_name + '/',
                'Handler': 'app.{}'.format(api_name),
                'FunctionName': f'{namespace}-{deployment_name}-{api_name}',
                'Timeout': timeout,
                'MemorySize': memory_size,
                'Events': {
                    'Api': {
                        'Type': 'Api',
                        'Properties': {
                            'Path': '/{}'.format(api_name),
                            'Method': 'post',
                        },
                    }
                },
                'Policies': [{'S3ReadPolicy': {'BucketName': s3_bucket_name}}],
                'Environment': {
                    'Variables': {
                        'BENTOML_BENTO_SERVICE_NAME': bento_service_name,
                        'BENTOML_API_NAME': api_name,
                        'BENTOML_S3_BUCKET': s3_bucket_name,
                        'BENTOML_DEPLOYMENT_PATH_PREFIX': deployment_path_prefix,
                    }
                },
            },
        }

    yaml.dump(sam_config, Path(template_file_path))

    # We add Outputs section separately, because the value should not
    # have "'" around !Sub
    with open(template_file_path, 'a') as f:
        f.write(
            """\
  EndpointUrl:
    Value: !Sub "https://${ServerlessRestApi}.execute-api.${AWS::Region}.\
amazonaws.com/Prod"
    Description: URL for endpoint
"""
        )
    return template_file_path


def _cleanup_s3_bucket_if_exist(bucket_name, region):
    s3_client = boto3.client('s3', region)
    s3 = boto3.resource('s3')
    try:
        logger.debug('Removing all objects inside bucket %s', bucket_name)
        s3.Bucket(bucket_name).objects.all().delete()
        logger.debug('Deleting bucket %s', bucket_name)
        s3_client.delete_bucket(Bucket=bucket_name)
    except ClientError as e:
        if e.response and e.response['Error']['Code'] == 'NoSuchBucket':
            # If there is no bucket, we just let it silently fail, dont have to do
            # any thing
            return
        else:
            raise e


class AwsLambdaDeploymentOperator(DeploymentOperatorBase):
    def add(self, deployment_pb):
        try:
            ensure_sam_available_or_raise()
            ensure_docker_available_or_raise()
            deployment_spec = deployment_pb.spec

            bento_pb = self.yatai_service.GetBento(
                GetBentoRequest(
                    bento_name=deployment_spec.bento_name,
                    bento_version=deployment_spec.bento_version,
                )
            )
            if bento_pb.bento.uri.type not in (BentoUri.LOCAL, BentoUri.S3):
                raise BentoMLException(
                    'BentoML currently not support {} repository'.format(
                        BentoUri.StorageType.Name(bento_pb.bento.uri.type)
                    )
                )

            return self._add(deployment_pb, bento_pb, bento_pb.bento.uri.uri)
        except BentoMLException as error:
            deployment_pb.state.state = DeploymentState.ERROR
            deployment_pb.state.error_message = f'Error: {str(error)}'
            return ApplyDeploymentResponse(
                status=error.status_proto, deployment=deployment_pb
            )

    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

    def update(self, deployment_pb):
        raise NotImplementedError(
            "Updating AWS Lambda deployment is not supported in current version of "
            "BentoML"
        )

    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

            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 describe(self, deployment_pb):
        try:
            deployment_spec = deployment_pb.spec
            lambda_deployment_config = deployment_spec.aws_lambda_operator_config

            bento_pb = self.yatai_service.GetBento(
                GetBentoRequest(
                    bento_name=deployment_spec.bento_name,
                    bento_version=deployment_spec.bento_version,
                )
            )
            bento_service_metadata = bento_pb.bento.bento_service_metadata
            api_names = (
                [lambda_deployment_config.api_name]
                if lambda_deployment_config.api_name
                else [api.name for api in bento_service_metadata.apis]
            )

            try:
                cf_client = boto3.client(
                    'cloudformation', lambda_deployment_config.region
                )
                cloud_formation_stack_result = cf_client.describe_stacks(
                    StackName='{ns}-{name}'.format(
                        ns=deployment_pb.namespace, name=deployment_pb.name
                    )
                )
                stack_result = cloud_formation_stack_result.get('Stacks')[0]
                # https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/\
                # using-cfn-describing-stacks.html
                success_status = ['CREATE_COMPLETE', 'UPDATE_COMPLETE']
                if stack_result['StackStatus'] in success_status:
                    if stack_result.get('Outputs'):
                        outputs = stack_result['Outputs']
                    else:
                        return DescribeDeploymentResponse(
                            status=Status.ABORTED('"Outputs" field is not present'),
                            state=DeploymentState(
                                state=DeploymentState.ERROR,
                                error_message='"Outputs" field is not present',
                            ),
                        )
                elif stack_result['StackStatus'] in FAILED_CLOUDFORMATION_STACK_STATUS:
                    state = DeploymentState(state=DeploymentState.FAILED)
                    state.timestamp.GetCurrentTime()
                    return DescribeDeploymentResponse(status=Status.OK(), state=state)
                else:
                    state = DeploymentState(state=DeploymentState.PENDING)
                    state.timestamp.GetCurrentTime()
                    return DescribeDeploymentResponse(status=Status.OK(), state=state)
            except Exception as error:  # pylint: disable=broad-except
                state = DeploymentState(
                    state=DeploymentState.ERROR, error_message=str(error)
                )
                state.timestamp.GetCurrentTime()
                return DescribeDeploymentResponse(
                    status=Status.INTERNAL(str(error)), state=state
                )
            outputs = {o['OutputKey']: o['OutputValue'] for o in outputs}
            info_json = {}

            if 'EndpointUrl' in outputs:
                info_json['endpoints'] = [
                    outputs['EndpointUrl'] + '/' + api_name for api_name in api_names
                ]
            if 'S3Bucket' in outputs:
                info_json['s3_bucket'] = outputs['S3Bucket']

            state = DeploymentState(
                state=DeploymentState.RUNNING, info_json=json.dumps(info_json)
            )
            state.timestamp.GetCurrentTime()
            return DescribeDeploymentResponse(status=Status.OK(), state=state)
        except BentoMLException as error:
            return DescribeDeploymentResponse(status=error.status_proto)