def DescribeDeployment(self, request, context=None):
try:
request.namespace = request.namespace or self.default_namespace
deployment_pb = self.deployment_store.get(
request.deployment_name, request.namespace
)
if deployment_pb:
operator = get_deployment_operator(deployment_pb)
response = operator.describe(deployment_pb, self)
if response.status.status_code == status_pb2.Status.OK:
with self.deployment_store.update_deployment(
request.deployment_name, request.namespace
) as deployment:
deployment.state = ProtoMessageToDict(response.state)
return response
else:
return DescribeDeploymentResponse(
status=Status.NOT_FOUND(
'Deployment "{}" in namespace "{}" not found'.format(
request.deployment_name, request.namespace
)
)
)
except BentoMLException as e:
logger.error("INTERNAL ERROR: %s", e)
return DescribeDeploymentResponse(Status.INTERNAL(str(e)))
def describe(self, deployment_pb):
try:
deployment_spec = deployment_pb.spec
sagemaker_config = deployment_spec.sagemaker_operator_config
sagemaker_client = boto3.client('sagemaker', sagemaker_config.region)
_, _, sagemaker_endpoint_name = _get_sagemaker_resource_names(deployment_pb)
try:
endpoint_status_response = sagemaker_client.describe_endpoint(
EndpointName=sagemaker_endpoint_name
)
except ClientError as e:
raise _aws_client_error_to_bentoml_exception(
e,
f"Failed to fetch current status of sagemaker endpoint "
f"'{sagemaker_endpoint_name}'",
)
logger.debug("AWS describe endpoint response: %s", endpoint_status_response)
endpoint_status = endpoint_status_response["EndpointStatus"]
service_state = ENDPOINT_STATUS_TO_STATE[endpoint_status]
deployment_state = DeploymentState(
state=service_state,
info_json=json.dumps(endpoint_status_response, default=str),
)
deployment_state.timestamp.GetCurrentTime()
return DescribeDeploymentResponse(
state=deployment_state, status=Status.OK()
)
except BentoMLException as error:
return DescribeDeploymentResponse(status=error.status_proto)
def describe(self, deployment_pb, repo=None):
deployment_spec = deployment_pb.spec
sagemaker_config = deployment_spec.sagemaker_operator_config
if sagemaker_config is None:
raise BentoMLDeploymentException(
'Sagemaker configuration is missing.')
sagemaker_client = boto3.client('sagemaker', sagemaker_config.region)
endpoint_name = generate_aws_compatible_string(
deployment_pb.namespace + '-' + deployment_spec.bento_name)
try:
endpoint_status_response = sagemaker_client.describe_endpoint(
EndpointName=endpoint_name)
except ClientError as e:
status = _parse_aws_client_exception_or_raise(e)
status.error_message = (
'Failed to describe SageMaker deployment: %s',
status.error_message,
)
return DescribeDeploymentResponse(status=status)
logger.debug("AWS describe endpoint response: %s",
endpoint_status_response)
endpoint_status = endpoint_status_response["EndpointStatus"]
service_state = ENDPOINT_STATUS_TO_STATE[endpoint_status]
deployment_state = DeploymentState(
state=service_state,
info_json=json.dumps(endpoint_status_response, default=str),
)
return DescribeDeploymentResponse(state=deployment_state,
status=Status.OK())
def describe(self, deployment_pb, yatai_service=None):
try:
deployment_spec = deployment_pb.spec
aws_config = deployment_spec.aws_lambda_operator_config
info_json = {'endpoints': []}
bento_pb = 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 = (
[aws_config.api_name]
if aws_config.api_name
else [api.name for api in bento_service_metadata.apis]
)
try:
cloud_formation_stack_result = boto3.client(
'cloudformation'
).describe_stacks(
StackName='{name}-{ns}'.format(
ns=deployment_pb.namespace, name=deployment_pb.name
)
)
outputs = cloud_formation_stack_result.get('Stacks')[0]['Outputs']
except Exception as error:
state = DeploymentState(
state=DeploymentState.ERROR, error_message=str(error)
)
state.timestamp.GetCurrentTime()
return DescribeDeploymentResponse(
status=Status.INTERNAL(str(error)), state=state
)
base_url = ''
for output in outputs:
if output['OutputKey'] == 'ServiceEndpoint':
base_url = output['OutputValue']
break
if base_url:
info_json['endpoints'] = [
base_url + '/' + api_name for api_name in api_names
]
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=exception_to_return_status(error))
def describe(self, deployment_pb, repo=None):
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)
with TemporaryServerlessConfig(
archive_path=bento_path,
deployment_name=deployment_pb.name,
region=aws_config.region,
stage=deployment_pb.namespace,
provider_name='aws',
functions=generate_aws_handler_functions_config(
bento_config['apis']),
) as tempdir:
try:
response = call_serverless_command(["serverless", "info"],
tempdir)
info_json = parse_serverless_info_response_to_json_string(
response)
state = DeploymentState(state=DeploymentState.RUNNING,
info_json=info_json)
except BentoMLException as e:
state = DeploymentState(state=DeploymentState.ERROR,
error_message=str(e))
return DescribeDeploymentResponse(status=Status.OK(), state=state)
def create_yatai_service_mock():
yatai_service_mock = Mock()
yatai_service_mock.ApplyDeployment.return_value = ApplyDeploymentResponse()
yatai_service_mock.DeleteDeployment.return_value = DeleteDeploymentResponse()
yatai_service_mock.DescribeDeployment.return_value = DescribeDeploymentResponse()
yatai_service_mock.GetDeployment.return_value = GetDeploymentResponse(
status=Status.NOT_FOUND()
)
yatai_service_mock.ListDeployments.return_value = ListDeploymentsResponse()
return yatai_service_mock
def describe(self, deployment_pb, yatai_service=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,
))
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])
with TempDirectory() as 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,
)
try:
response = call_serverless_command(["info"],
serverless_project_dir)
info_json = parse_serverless_info_response_to_json_string(
response)
state = DeploymentState(state=DeploymentState.RUNNING,
info_json=info_json)
state.timestamp.GetCurrentTime()
except BentoMLException as e:
state = DeploymentState(state=DeploymentState.ERROR,
error_message=str(e))
state.timestamp.GetCurrentTime()
return DescribeDeploymentResponse(status=Status.OK(), state=state)
except BentoMLException as error:
return DescribeDeploymentResponse(
status=exception_to_return_status(error))
def describe(self, deployment_pb):
try:
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')
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)