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