def DeleteDeployment(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:
# find deployment operator based on deployment spec
operator = get_deployment_operator(self, deployment_pb)
# executing deployment deletion
response = operator.delete(deployment_pb)
# if delete successful, remove it from active deployment records table
if response.status.status_code == status_pb2.Status.OK:
self.deployment_store.delete(
request.deployment_name, request.namespace
)
return response
# If force delete flag is True, we will remove the record
# from yatai database.
if request.force_delete:
self.deployment_store.delete(
request.deployment_name, request.namespace
)
return DeleteDeploymentResponse(status=Status.OK())
if response.status.status_code == status_pb2.Status.NOT_FOUND:
modified_message = (
'Cloud resources not found, error: {} - it may have been '
'deleted manually. Try delete deployment '
'with "--force" option to ignore this error '
'and force deleting the deployment record'.format(
response.status.error_message
)
)
response.status.error_message = modified_message
return response
else:
return DeleteDeploymentResponse(
status=Status.NOT_FOUND(
'Deployment "{}" in namespace "{}" not found'.format(
request.deployment_name, request.namespace
)
)
)
except BentoMLException as e:
logger.error("RPC ERROR DeleteDeployment: %s", e)
return DeleteDeploymentResponse(status=e.status_proto)
except Exception as e: # pylint: disable=broad-except
logger.error("RPC ERROR DeleteDeployment: %s", e)
return DeleteDeploymentResponse(status=Status.INTERNAL(str(e)))
def ListBento(self, request, context=None):
try:
# TODO: validate request
bento_metadata_pb_list = self.bento_metadata_store.list(
request.bento_name, request.offset, request.limit, request.filter
)
return ListBentoResponse(status=Status.OK(), bentos=bento_metadata_pb_list)
except BentoMLException as e:
logger.error("INTERNAL ERROR: %s", e)
return ListBentoResponse(status=Status.INTERNAL(str(e)))
def ApplyDeployment(self, request, context=None):
try:
# apply default namespace if not set
request.deployment.namespace = (
request.deployment.namespace or self.default_namespace
)
validation_errors = validate_deployment_pb_schema(request.deployment)
if validation_errors:
return ApplyDeploymentResponse(
status=Status.INVALID_ARGUMENT(
'Failed to validate deployment. {errors}'.format(
errors=validation_errors
)
)
)
previous_deployment = self.deployment_store.get(
request.deployment.name, request.deployment.namespace
)
if previous_deployment:
# check deployment platform
if (
previous_deployment.spec.operator
!= request.deployment.spec.operator
):
return ApplyDeploymentResponse(
status=Status.ABORTED(
'Can not change the target deploy platform of existing '
'active deployment. Try delete existing deployment and '
'deploy to new target platform again'
)
)
request.deployment.state.state = DeploymentState.PENDING
else:
request.deployment.created_at.GetCurrentTime()
request.deployment.last_updated_at.GetCurrentTime()
self.deployment_store.insert_or_update(request.deployment)
# find deployment operator based on deployment spec
operator = get_deployment_operator(request.deployment)
# deploying to target platform
response = operator.apply(request.deployment, self, previous_deployment)
# update deployment state
self.deployment_store.insert_or_update(response.deployment)
return response
except BentoMLException as e:
logger.error("INTERNAL ERROR: %s", e)
return ApplyDeploymentResponse(status=Status.INTERNAL(str(e)))
def delete(self, deployment_pb, yatai_service=None):
try:
state = self.describe(deployment_pb, yatai_service).state
if state.state != DeploymentState.RUNNING:
message = ('Failed to delete, no active deployment {name}. '
'The current state is {state}'.format(
name=deployment_pb.name,
state=DeploymentState.State.Name(state.state),
))
return DeleteDeploymentResponse(status=Status.ABORTED(message))
deployment_spec = deployment_pb.spec
aws_config = deployment_spec.aws_lambda_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
# We are not validating api_name, because for delete, you don't
# need them.
api_names = ([aws_config.api_name] if aws_config.api_name else
[api.name for api in bento_service_metadata.apis])
with TempDirectory() as serverless_project_dir:
generate_aws_lambda_serverless_config(
bento_service_metadata.env.python_version,
deployment_pb.name,
api_names,
serverless_project_dir,
aws_config.region,
# BentoML deployment namespace is mapping to serverless `stage`
# concept
stage=deployment_pb.namespace,
)
response = call_serverless_command(['remove'],
serverless_project_dir)
stack_name = '{name}-{namespace}'.format(
name=deployment_pb.name, namespace=deployment_pb.namespace)
if "Serverless: Stack removal finished..." in response:
status = Status.OK()
elif "Stack '{}' does not exist".format(
stack_name) in response:
status = Status.NOT_FOUND(
'Deployment {} not found'.format(stack_name))
else:
status = Status.ABORTED()
return DeleteDeploymentResponse(status=status)
except BentoMLException as error:
return DeleteDeploymentResponse(
status=exception_to_return_status(error))
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 AddBento(self, request, context=None):
try:
# TODO: validate request
new_bento_uri = self.repo.add(request.bento_name, request.bento_version)
self.bento_metadata_store.add(
bento_name=request.bento_name,
bento_version=request.bento_version,
uri=new_bento_uri.uri,
uri_type=new_bento_uri.type,
)
return AddBentoResponse(status=Status.OK(), uri=new_bento_uri)
except BentoMLException as e:
logger.error("INTERNAL ERROR: %s", e)
return AddBentoResponse(status=Status.INTERNAL(str(e)))
def GetDeployment(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:
return GetDeploymentResponse(status=Status.OK(),
deployment=deployment_pb)
else:
return GetDeploymentResponse(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 GetDeploymentResponse(status=Status.INTERNAL(str(e)))
def GetBento(self, request, context=None):
try:
# TODO: validate request
bento_metadata_pb = self.bento_metadata_store.get(
request.bento_name, request.bento_version)
if bento_metadata_pb:
return GetBentoResponse(status=Status.OK(),
bento=bento_metadata_pb)
else:
return GetBentoResponse(status=Status.NOT_FOUND(
"Bento `{}:{}` is not found".format(
request.bento_name, request.bento_version)))
except BentoMLException as e:
logger.error("INTERNAL ERROR: %s", e)
return GetBentoResponse(status=Status.INTERNAL(str(e)))
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):
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 _add(self, deployment_pb, bento_pb, bento_path):
try:
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
aws_ec2_deployment_config = deployment_spec.aws_ec2_operator_config
user_id = get_aws_user_id()
artifact_s3_bucket_name = generate_aws_compatible_string(
"btml-{user_id}-{namespace}".format(
user_id=user_id,
namespace=deployment_pb.namespace,
))
create_s3_bucket_if_not_exists(artifact_s3_bucket_name,
aws_ec2_deployment_config.region)
self.deploy_service(
deployment_pb,
deployment_spec,
bento_path,
aws_ec2_deployment_config,
artifact_s3_bucket_name,
aws_ec2_deployment_config.region,
)
except BentoMLException as error:
if artifact_s3_bucket_name and aws_ec2_deployment_config.region:
cleanup_s3_bucket_if_exist(artifact_s3_bucket_name,
aws_ec2_deployment_config.region)
raise error
return ApplyDeploymentResponse(status=Status.OK(),
deployment=deployment_pb)
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(self, deployment_pb)
response = operator.describe(deployment_pb)
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("RPC ERROR DescribeDeployment: %s", e)
return DeleteDeploymentResponse(status=e.status_proto)
def delete(self, deployment_pb):
try:
deployment_spec = deployment_pb.spec
sagemaker_config = deployment_spec.sagemaker_operator_config
sagemaker_config.region = (sagemaker_config.region
or get_default_aws_region())
if not sagemaker_config.region:
raise InvalidArgument('AWS region is missing')
sagemaker_client = boto3.client('sagemaker',
sagemaker_config.region)
_, _, sagemaker_endpoint_name = _get_sagemaker_resource_names(
deployment_pb)
try:
delete_endpoint_response = sagemaker_client.delete_endpoint(
EndpointName=sagemaker_endpoint_name)
logger.debug("AWS delete endpoint response: %s",
delete_endpoint_response)
except ClientError as e:
raise _aws_client_error_to_bentoml_exception(e)
_try_clean_up_sagemaker_deployment_resource(deployment_pb)
return DeleteDeploymentResponse(status=Status.OK())
except BentoMLException as error:
return DeleteDeploymentResponse(status=error.status_proto)
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 _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_lambda_deployment_config = (
updated_deployment_spec.aws_lambda_operator_config)
updated_bento_service_metadata = bento_pb.bento.bento_service_metadata
describe_result = self.describe(deployment_pb)
if describe_result.status.status_code != status_pb2.Status.OK:
error_code, error_message = status_pb_to_error_code_and_message(
describe_result.status)
raise YataiDeploymentException(
f'Failed fetching Lambda deployment current status - '
f'{error_code}:{error_message}')
latest_deployment_state = json.loads(describe_result.state.info_json)
if 's3_bucket' in latest_deployment_state:
lambda_s3_bucket = latest_deployment_state['s3_bucket']
else:
raise BentoMLException(
'S3 Bucket is missing in the AWS Lambda deployment, please make sure '
'it exists and try again')
_deploy_lambda_function(
deployment_pb=deployment_pb,
bento_service_metadata=updated_bento_service_metadata,
deployment_spec=updated_deployment_spec,
lambda_s3_bucket=lambda_s3_bucket,
lambda_deployment_config=updated_lambda_deployment_config,
bento_path=bento_path,
)
return ApplyDeploymentResponse(deployment=deployment_pb,
status=Status.OK())
def ListDeployments(self, request, context=None):
try:
namespace = request.namespace or self.default_namespace
deployment_pb_list = self.deployment_store.list(
namespace=namespace,
filter_str=request.filter,
labels=request.labels,
offset=request.offset,
limit=request.limit,
)
return ListDeploymentsResponse(status=Status.OK(),
deployments=deployment_pb_list)
except BentoMLException as e:
logger.error("INTERNAL ERROR: %s", e)
return ListDeploymentsResponse(status=Status.INTERNAL(str(e)))
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 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 delete(self, deployment_pb):
try:
deployment_spec = deployment_pb.spec
ec2_deployment_config = deployment_spec.aws_ec2_operator_config
ec2_deployment_config.region = (ec2_deployment_config.region
or get_default_aws_region())
if not ec2_deployment_config.region:
raise InvalidArgument("AWS region is missing")
_, deployment_stack_name, repository_name, _ = generate_ec2_resource_names(
deployment_pb.namespace, deployment_pb.name)
# delete stack
delete_cloudformation_stack(deployment_stack_name,
ec2_deployment_config.region)
# delete repo from ecr
delete_ecr_repository(repository_name,
ec2_deployment_config.region)
# remove bucket
if deployment_pb.state.info_json:
deployment_info_json = json.loads(
deployment_pb.state.info_json)
bucket_name = deployment_info_json.get('S3Bucket')
if bucket_name:
cleanup_s3_bucket_if_exist(bucket_name,
ec2_deployment_config.region)
return DeleteDeploymentResponse(status=Status.OK())
except BentoMLException as error:
return DeleteDeploymentResponse(status=error.status_proto)
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)
try:
_deploy_azure_functions(
deployment_spec=deployment_pb.spec,
deployment_name=deployment_pb.name,
namespace=deployment_pb.namespace,
bento_pb=bento_pb,
bento_path=bento_path,
)
return ApplyDeploymentResponse(status=Status.OK(),
deployment=deployment_pb)
except AzureServiceError as error:
resource_group_name, _, _, _, _, = _generate_azure_resource_names(
deployment_pb.namespace, deployment_pb.name)
logger.debug(
'Failed to create Azure Functions. Cleaning up Azure resources'
)
try:
_call_az_cli(
command=[
'az',
'group',
'delete',
'-y',
'--name',
resource_group_name,
],
message='delete Azure resource group',
)
except AzureServiceError:
pass
raise error
def apply(self, deployment_pb, repo, prev_deployment=None):
deployment_spec = deployment_pb.spec
gcp_config = deployment_spec.gcp_function_operator_config
bento_path = repo.get(deployment_spec.bento_name,
deployment_spec.bento_version)
bento_config = load_bentoml_config(bento_path)
with TemporaryServerlessContent(
archive_path=bento_path,
deployment_name=deployment_pb.name,
bento_name=deployment_spec.bento_name,
template_type='google-python',
) as output_path:
generate_main_py(bento_config['name'], bento_config['apis'],
output_path)
generate_serverless_configuration_for_gcp_function(
service_name=bento_config['name'],
apis=bento_config['apis'],
output_path=output_path,
region=gcp_config.region,
stage=deployment_pb.namespace,
)
call_serverless_command(["deploy"], output_path)
res_deployment_pb = Deployment(state=DeploymentState())
res_deployment_pb.CopyFrom(deployment_pb)
state = self.describe(res_deployment_pb, repo).state
res_deployment_pb.state.CopyFrom(state)
return ApplyDeploymentResponse(status=Status.OK(),
deployment=res_deployment_pb)
def ApplyDeployment(self, request, context=None):
try:
# apply default namespace if not set
request.deployment.namespace = (request.deployment.namespace
or self.default_namespace)
# TODO: validate deployment config
# create or update deployment spec record
self.deployment_store.insert_or_update(request.deployment)
# find deployment operator based on deployment spec
operator = get_deployment_operator(request.deployment)
# deploying to target platform
response = operator.apply(request.deployment, self.repo)
# update deployment state
self.deployment_store.insert_or_update(response.deployment)
return response
except BentoMLException as e:
logger.error("INTERNAL ERROR: %s", e)
return ApplyDeploymentResponse(Status.INTERNAL(e))
def delete(self, deployment_pb, yatai_service=None):
try:
state = self.describe(deployment_pb, yatai_service).state
if state.state != DeploymentState.RUNNING:
message = ('Failed to delete, no active deployment {name}. '
'The current state is {state}'.format(
name=deployment_pb.name,
state=DeploymentState.State.Name(state.state),
))
return DeleteDeploymentResponse(status=Status.ABORTED(message))
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(['remove'],
serverless_project_dir)
if "Serverless: Stack removal finished..." in response:
status = Status.OK()
else:
status = Status.ABORTED()
except BentoMLException as e:
status = Status.INTERNAL(str(e))
return DeleteDeploymentResponse(status=status)
except BentoMLException as error:
return DeleteDeploymentResponse(
status=exception_to_return_status(error))
def describe(self, deployment_pb):
try:
(
resource_group_name,
_,
_,
function_name,
_,
) = _generate_azure_resource_names(deployment_pb.namespace,
deployment_pb.name)
show_function_result = _call_az_cli(
command=[
'az',
'functionapp',
'show',
'--name',
function_name,
'--resource-group',
resource_group_name,
],
message='show Azure functionapp detail',
)
keys = [
'defaultHostName',
'enabledHostNames',
'hostNames',
'id',
'kind',
'lastModifiedTimeUtc',
'location',
'name',
'repositorySiteName',
'reserved',
'resourceGroup',
'state',
'type',
'usageState',
]
# Need find more documentation on the status of functionapp. For now, any
# other status is error.
if show_function_result['state'] == 'Running':
state = DeploymentState.RUNNING
else:
state = DeploymentState.ERROR
info_json = {
k: v
for k, v in show_function_result.items() if k in keys
}
deployment_state = DeploymentState(
info_json=json.dumps(info_json, default=str),
state=state,
)
deployment_state.timestamp.GetCurrentTime()
return DescribeDeploymentResponse(state=deployment_state,
status=Status.OK())
except BentoMLException as error:
return DescribeDeploymentResponse(status=error.status_proto)
def ListBento(self, request, context=None):
try:
# TODO: validate request
bento_metadata_pb_list = self.bento_metadata_store.list(
bento_name=request.bento_name,
offset=request.offset,
limit=request.limit,
order_by=request.order_by,
ascending_order=request.ascending_order,
)
return ListBentoResponse(status=Status.OK(), bentos=bento_metadata_pb_list)
except BentoMLException as e:
logger.error("RPC ERROR ListBento: %s", e)
return ListBentoResponse(status=e.status_proto)
except Exception as e: # pylint: disable=broad-except
logger.error("RPC ERROR ListBento: %s", e)
return ListBentoResponse(status=Status.INTERNAL())
def DangerouslyDeleteBento(self, request, context=None):
try:
# TODO: validate request
self.bento_metadata_store.dangerously_delete(
request.bento_name, request.bento_version
)
except BentoMLException as e:
logger.error("INTERNAL ERROR: %s", e)
return DangerouslyDeleteBentoResponse(status=Status.INTERNAL(str(e)))
def UpdateBento(self, request, context=None):
try:
# TODO: validate request
if request.upload_status:
self.bento_metadata_store.update_upload_status(
request.bento_name, request.bento_version,
request.upload_status)
if request.service_metadata:
self.bento_metadata_store.update_bento_service_metadata(
request.bento_name, request.bento_version,
request.service_metadata)
return UpdateBentoResponse(status=Status.OK())
except BentoMLException as e:
logger.error("RPC ERROR UpdateBento: %s", e)
return UpdateBentoResponse(status=e.status_proto)
except Exception as e: # pylint: disable=broad-except
logger.error("RPC ERROR UpdateBento: %s", e)
return UpdateBentoResponse(status=Status.INTERNAL())
def GetBento(self, request, context=None):
try:
# TODO: validate request
bento_metadata_pb = self.bento_metadata_store.get(
request.bento_name, request.bento_version)
if request.bento_version.lower() == 'latest':
logger.info(f'Getting latest version {request.bento_name}:'
f'{bento_metadata_pb.version}')
if bento_metadata_pb:
return GetBentoResponse(status=Status.OK(),
bento=bento_metadata_pb)
else:
return GetBentoResponse(status=Status.NOT_FOUND(
"BentoService `{}:{}` is not found".format(
request.bento_name, request.bento_version)))
except BentoMLException as e:
logger.error("RPC ERROR GetBento: %s", e)
return GetBentoResponse(status=e.status_proto)
def delete(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
)
delete_endpoint_response = sagemaker_client.delete_endpoint(
EndpointName=endpoint_name
)
logger.debug("AWS delete endpoint response: %s", delete_endpoint_response)
if delete_endpoint_response["ResponseMetadata"]["HTTPStatusCode"] == 200:
# We will also try to delete both model and endpoint configuration for user.
# Since they are not critical, even they failed, we will still count delete
# deployment a success action
model_name = create_sagemaker_model_name(
deployment_spec.bento_name, deployment_spec.bento_version
)
delete_model_response = sagemaker_client.delete_model(ModelName=model_name)
logger.debug("AWS delete model response: %s", delete_model_response)
if delete_model_response["ResponseMetadata"]["HTTPStatusCode"] != 200:
logger.error(
"Encounter error when deleting model: %s", delete_model_response
)
endpoint_config_name = create_sagemaker_endpoint_config_name(
deployment_spec.bento_name, deployment_spec.bento_version
)
delete_endpoint_config_response = sagemaker_client.delete_endpoint_config( # noqa: E501
EndpointConfigName=endpoint_config_name
)
logger.debug(
"AWS delete endpoint config response: %s",
delete_endpoint_config_response,
)
return DeleteDeploymentResponse(status=Status.OK())
else:
return DeleteDeploymentResponse(
status=Status.INTERNAL(str(delete_endpoint_response))
)
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