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
sagemaker_config = deployment_spec.sagemaker_operator_config
raise_if_api_names_not_found_in_bento_service_metadata(
bento_pb.bento.bento_service_metadata, [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,
bento_pb.bento.bento_service_metadata.env.docker_base_image,
)
ecr_image_path = create_and_push_docker_image_to_ecr(
sagemaker_config.region,
deployment_spec.bento_name,
deployment_spec.bento_version,
sagemaker_project_dir,
)
try:
(
sagemaker_model_name,
sagemaker_endpoint_config_name,
sagemaker_endpoint_name,
) = _get_sagemaker_resource_names(deployment_pb)
_create_sagemaker_model(sagemaker_client, sagemaker_model_name,
ecr_image_path, sagemaker_config)
_create_sagemaker_endpoint_config(
sagemaker_client,
sagemaker_model_name,
sagemaker_endpoint_config_name,
sagemaker_config,
)
_create_sagemaker_endpoint(
sagemaker_client,
sagemaker_endpoint_name,
sagemaker_endpoint_config_name,
)
except AWSServiceError as e:
delete_sagemaker_deployment_resources_if_exist(deployment_pb)
raise e
return ApplyDeploymentResponse(status=Status.OK(),
deployment=deployment_pb)
def _update(self, deployment_pb, current_deployment, bento_pb, bento_path):
if loader._is_remote_path(bento_path):
with loader._resolve_remote_bundle_path(bento_path) as local_path:
return self._update(deployment_pb, current_deployment,
bento_pb, local_path)
updated_deployment_spec = deployment_pb.spec
updated_sagemaker_config = updated_deployment_spec.sagemaker_operator_config
sagemaker_client = boto3.client(
"sagemaker", updated_sagemaker_config.region
or get_default_aws_region())
try:
raise_if_api_names_not_found_in_bento_service_metadata(
bento_pb.bento.bento_service_metadata,
[updated_sagemaker_config.api_name],
)
describe_latest_deployment_state = self.describe(deployment_pb)
current_deployment_spec = current_deployment.spec
current_sagemaker_config = current_deployment_spec.sagemaker_operator_config
latest_deployment_state = json.loads(
describe_latest_deployment_state.state.info_json)
current_ecr_image_tag = latest_deployment_state[
"ProductionVariants"][0]["DeployedImages"][0]["SpecifiedImage"]
if (updated_deployment_spec.bento_name !=
current_deployment_spec.bento_name
or updated_deployment_spec.bento_version !=
current_deployment_spec.bento_version):
logger.debug(
"BentoService tag is different from current deployment, "
"creating new docker image and push to ECR")
with TempDirectory() as temp_dir:
sagemaker_project_dir = os.path.join(
temp_dir, updated_deployment_spec.bento_name)
_init_sagemaker_project(
sagemaker_project_dir,
bento_path,
bento_pb.bento.bento_service_metadata.env.
docker_base_image,
)
ecr_image_path = create_and_push_docker_image_to_ecr(
updated_sagemaker_config.region,
updated_deployment_spec.bento_name,
updated_deployment_spec.bento_version,
sagemaker_project_dir,
)
else:
logger.debug("Using existing ECR image for Sagemaker model")
ecr_image_path = current_ecr_image_tag
(
updated_sagemaker_model_name,
updated_sagemaker_endpoint_config_name,
sagemaker_endpoint_name,
) = _get_sagemaker_resource_names(deployment_pb)
(
current_sagemaker_model_name,
current_sagemaker_endpoint_config_name,
_,
) = _get_sagemaker_resource_names(current_deployment)
if (updated_sagemaker_config.api_name !=
current_sagemaker_config.api_name
or updated_sagemaker_config.
num_of_gunicorn_workers_per_instance !=
current_sagemaker_config.
num_of_gunicorn_workers_per_instance
or ecr_image_path != current_ecr_image_tag):
logger.debug(
"Sagemaker model requires update. Delete current sagemaker model %s"
"and creating new model %s",
current_sagemaker_model_name,
updated_sagemaker_model_name,
)
_delete_sagemaker_model_if_exist(sagemaker_client,
current_sagemaker_model_name)
_create_sagemaker_model(
sagemaker_client,
updated_sagemaker_model_name,
ecr_image_path,
updated_sagemaker_config,
)
# When bento service tag is not changed, we need to delete the current
# endpoint configuration in order to create new one to avoid name collation
if (current_sagemaker_endpoint_config_name ==
updated_sagemaker_endpoint_config_name):
logger.debug(
"Current sagemaker config name %s is same as updated one, "
"delete it before create new endpoint config",
current_sagemaker_endpoint_config_name,
)
_delete_sagemaker_endpoint_config_if_exist(
sagemaker_client, current_sagemaker_endpoint_config_name)
logger.debug(
"Create new endpoint configuration %s",
updated_sagemaker_endpoint_config_name,
)
_create_sagemaker_endpoint_config(
sagemaker_client,
updated_sagemaker_model_name,
updated_sagemaker_endpoint_config_name,
updated_sagemaker_config,
)
logger.debug(
"Updating endpoint to new endpoint configuration %s",
updated_sagemaker_endpoint_config_name,
)
_update_sagemaker_endpoint(
sagemaker_client,
sagemaker_endpoint_name,
updated_sagemaker_endpoint_config_name,
)
if not (current_sagemaker_endpoint_config_name
== updated_sagemaker_endpoint_config_name):
logger.debug(
'Delete old sagemaker endpoint config %s',
current_sagemaker_endpoint_config_name,
)
_delete_sagemaker_endpoint_config_if_exist(
sagemaker_client, current_sagemaker_endpoint_config_name)
except AWSServiceError as e:
delete_sagemaker_deployment_resources_if_exist(deployment_pb)
raise e
return ApplyDeploymentResponse(status=Status.OK(),
deployment=deployment_pb)
def _deploy_lambda_function(
deployment_pb,
bento_service_metadata,
deployment_spec,
lambda_s3_bucket,
lambda_deployment_config,
bento_path,
):
deployment_path_prefix = os.path.join(deployment_pb.namespace,
deployment_pb.name)
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(
'AWS Lambda Deployment only supports BentoML services'
'built with Python 3.6.x. To fix this, repack your'
'service with the right Python version'
'(hint: pyenv/anaconda) and try again')
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)
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_sam_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,
)
