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 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 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 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 _apply(self, deployment_pb, bento_pb, yatai_service, bento_path): if loader._is_remote_path(bento_path): with loader._resolve_remote_bundle_path(bento_path) as local_path: return self._apply(deployment_pb, bento_pb, yatai_service, local_path) deployment_spec = deployment_pb.spec aws_config = deployment_spec.aws_lambda_operator_config bento_service_metadata = bento_pb.bento.bento_service_metadata template = 'aws-python3' if version.parse(bento_service_metadata.env.python_version ) < version.parse('3.0.0'): template = 'aws-python' api_names = ([aws_config.api_name] if aws_config.api_name else [api.name for api in bento_service_metadata.apis]) ensure_deploy_api_name_exists_in_bento( [api.name for api in bento_service_metadata.apis], api_names) with TempDirectory() as serverless_project_dir: init_serverless_project_dir( serverless_project_dir, bento_path, deployment_pb.name, deployment_spec.bento_name, template, ) generate_aws_lambda_handler_py(deployment_spec.bento_name, api_names, 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, ) logger.info( 'Installing additional packages: serverless-python-requirements' ) install_serverless_plugin("serverless-python-requirements", serverless_project_dir) logger.info('Deploying to AWS Lambda') call_serverless_command(["deploy"], serverless_project_dir) res_deployment_pb = Deployment(state=DeploymentState()) res_deployment_pb.CopyFrom(deployment_pb) state = self.describe(res_deployment_pb, yatai_service).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 ) validation_errors = validate_deployment_pb_schema(request.deployment) if validation_errors: return ApplyDeploymentResponse( status=Status.ABORTED( '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 = DeploymentState( state=DeploymentState.PENDING ) 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, 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 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 apply(self, deployment_pb, yatai_service, prev_deployment=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, )) if bento_pb.bento.uri.type != BentoUri.LOCAL: raise BentoMLException( 'BentoML currently only support local repository') else: bento_path = bento_pb.bento.uri.uri 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]) ensure_deploy_api_name_exists_in_bento( [api.name for api in bento_service_metadata.apis], api_names) with TempDirectory() as serverless_project_dir: init_serverless_project_dir( serverless_project_dir, bento_path, deployment_pb.name, deployment_spec.bento_name, 'google-python', ) generate_gcp_function_main_py(deployment_spec.bento_name, api_names, 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, ) call_serverless_command(["deploy"], serverless_project_dir) res_deployment_pb = Deployment(state=DeploymentState()) res_deployment_pb.CopyFrom(deployment_pb) state = self.describe(res_deployment_pb, yatai_service).state res_deployment_pb.state.CopyFrom(state) return ApplyDeploymentResponse(status=Status.OK(), deployment=res_deployment_pb) except BentoMLException as error: return ApplyDeploymentResponse( status=exception_to_return_status(error))
def apply(self, deployment_pb, repo, prev_deployment=None): ensure_docker_available_or_raise() 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) template = 'aws-python3' minimum_python_version = version.parse('3.0.0') bento_python_version = version.parse( bento_config['env']['python_version']) if bento_python_version < minimum_python_version: template = 'aws-python' with TemporaryServerlessContent( archive_path=bento_path, deployment_name=deployment_pb.name, bento_name=deployment_spec.bento_name, template_type=template, ) as output_path: generate_handler_py(deployment_spec.bento_name, bento_config['apis'], output_path) generate_serverless_configuration_for_aws_lambda( service_name=deployment_pb.name, apis=bento_config['apis'], output_path=output_path, region=aws_config.region, stage=deployment_pb.namespace, ) logger.info( 'Installing additional packages: serverless-python-requirements, ' 'serverless-apigw-binary') install_serverless_plugin("serverless-python-requirements", output_path) install_serverless_plugin("serverless-apigw-binary", output_path) logger.info('Deploying to AWS Lambda') 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 apply(self, deployment_pb, repo, prev_deployment=None): deployment_spec = deployment_pb.spec sagemaker_config = deployment_spec.sagemaker_operator_config if sagemaker_config is None: raise BentoMLDeploymentException('Sagemaker configuration is missing.') archive_path = repo.get( deployment_spec.bento_name, deployment_spec.bento_version ) # config = load_bentoml_config(bento_path)... sagemaker_client = boto3.client('sagemaker', sagemaker_config.region) with TemporarySageMakerContent( archive_path, deployment_spec.bento_name, deployment_spec.bento_version ) as temp_path: ecr_image_path = create_push_image_to_ecr( deployment_spec.bento_name, deployment_spec.bento_version, temp_path ) execution_role_arn = get_arn_role_from_current_user() model_name = create_sagemaker_model_name( deployment_spec.bento_name, deployment_spec.bento_version ) sagemaker_model_info = { "ModelName": model_name, "PrimaryContainer": { "ContainerHostname": model_name, "Image": ecr_image_path, "Environment": { "API_NAME": sagemaker_config.api_name, "BENTO_SERVER_TIMEOUT": config().get( 'apiserver', 'default_timeout' ), "BENTO_SERVER_WORKERS": config().get( 'apiserver', 'default_gunicorn_workers_count' ), }, }, "ExecutionRoleArn": execution_role_arn, } logger.info("Creating sagemaker model %s", model_name) create_model_response = sagemaker_client.create_model(**sagemaker_model_info) logger.debug("AWS create model response: %s", create_model_response) production_variants = [ { "VariantName": generate_aws_compatible_string( deployment_spec.bento_name ), "ModelName": model_name, "InitialInstanceCount": sagemaker_config.instance_count, "InstanceType": sagemaker_config.instance_type, } ] endpoint_config_name = create_sagemaker_endpoint_config_name( deployment_spec.bento_name, deployment_spec.bento_version ) logger.info( "Creating Sagemaker endpoint %s configuration", endpoint_config_name ) create_endpoint_config_response = sagemaker_client.create_endpoint_config( EndpointConfigName=endpoint_config_name, ProductionVariants=production_variants, ) logger.debug( "AWS create endpoint config response: %s", create_endpoint_config_response ) endpoint_name = generate_aws_compatible_string( deployment_pb.namespace + '-' + deployment_spec.bento_name ) if prev_deployment: logger.info("Updating sagemaker endpoint %s", endpoint_name) update_endpoint_response = sagemaker_client.update_endpoint( EndpointName=endpoint_name, EndpointConfigName=endpoint_config_name ) logger.debug("AWS update endpoint response: %s", update_endpoint_response) else: logger.info("Creating sagemaker endpoint %s", endpoint_name) create_endpoint_response = sagemaker_client.create_endpoint( EndpointName=endpoint_name, EndpointConfigName=endpoint_config_name ) logger.debug("AWS create endpoint response: %s", create_endpoint_response) res_deployment_pb = Deployment(state=DeploymentState()) res_deployment_pb.CopyFrom(deployment_pb) return ApplyDeploymentResponse(status=Status.OK(), deployment=res_deployment_pb)
def apply(self, deployment_pb, yatai_service, prev_deployment=None): try: ensure_docker_available_or_raise() deployment_spec = deployment_pb.spec sagemaker_config = deployment_spec.sagemaker_operator_config if sagemaker_config is None: raise BentoMLDeploymentException('Sagemaker configuration is missing.') bento_pb = yatai_service.GetBento( GetBentoRequest( bento_name=deployment_spec.bento_name, bento_version=deployment_spec.bento_version, ) ) if bento_pb.bento.uri.type != BentoUri.LOCAL: raise BentoMLException( 'BentoML currently only support local repository' ) else: bento_path = bento_pb.bento.uri.uri ensure_deploy_api_name_exists_in_bento( [api.name for api in bento_pb.bento.bento_service_metadata.apis], [sagemaker_config.api_name], ) sagemaker_client = boto3.client('sagemaker', sagemaker_config.region) with TempDirectory() as temp_dir: sagemaker_project_dir = os.path.jon( temp_dir, deployment_spec.bento_name ) init_sagemaker_project(sagemaker_project_dir, bento_path) ecr_image_path = create_push_docker_image_to_ecr( deployment_spec.bento_name, deployment_spec.bento_version, sagemaker_project_dir, ) execution_role_arn = get_arn_role_from_current_aws_user() model_name = create_sagemaker_model_name( deployment_spec.bento_name, deployment_spec.bento_version ) sagemaker_model_info = { "ModelName": model_name, "PrimaryContainer": { "ContainerHostname": model_name, "Image": ecr_image_path, "Environment": { "API_NAME": sagemaker_config.api_name, "BENTO_SERVER_TIMEOUT": config().get( 'apiserver', 'default_timeout' ), "BENTO_SERVER_WORKERS": config().get( 'apiserver', 'default_gunicorn_workers_count' ), }, }, "ExecutionRoleArn": execution_role_arn, } logger.info("Creating sagemaker model %s", model_name) try: create_model_response = sagemaker_client.create_model( **sagemaker_model_info ) logger.debug("AWS create model response: %s", create_model_response) except ClientError as e: status = _parse_aws_client_exception_or_raise(e) status.error_message = ( 'Failed to create model for SageMaker Deployment: %s', status.error_message, ) return ApplyDeploymentResponse(status=status, deployment=deployment_pb) production_variants = [ { "VariantName": generate_aws_compatible_string( deployment_spec.bento_name ), "ModelName": model_name, "InitialInstanceCount": sagemaker_config.instance_count, "InstanceType": sagemaker_config.instance_type, } ] endpoint_config_name = create_sagemaker_endpoint_config_name( deployment_spec.bento_name, deployment_spec.bento_version ) logger.info( "Creating Sagemaker endpoint %s configuration", endpoint_config_name ) try: create_config_response = sagemaker_client.create_endpoint_config( EndpointConfigName=endpoint_config_name, ProductionVariants=production_variants, ) logger.debug( "AWS create endpoint config response: %s", create_config_response ) except ClientError as e: # create endpoint failed, will remove previously created model cleanup_model_error = _cleanup_sagemaker_model( sagemaker_client, deployment_spec.bento_name, deployment_spec.bento_version, ) if cleanup_model_error: cleanup_model_error.error_message = ( 'Failed to clean up model after unsuccessfully ' 'create endpoint config: %s', cleanup_model_error.error_message, ) return ApplyDeploymentResponse( status=cleanup_model_error, deployment=deployment_pb ) status = _parse_aws_client_exception_or_raise(e) status.error_message = ( 'Failed to create endpoint config for SageMaker deployment: %s', status.error_message, ) return ApplyDeploymentResponse(status=status, deployment=deployment_pb) endpoint_name = generate_aws_compatible_string( deployment_pb.namespace + '-' + deployment_spec.bento_name ) try: if prev_deployment: logger.debug("Updating sagemaker endpoint %s", endpoint_name) update_endpoint_response = sagemaker_client.update_endpoint( EndpointName=endpoint_name, EndpointConfigName=endpoint_config_name, ) logger.debug( "AWS update endpoint response: %s", update_endpoint_response ) else: logger.debug("Creating sagemaker endpoint %s", endpoint_name) create_endpoint_response = sagemaker_client.create_endpoint( EndpointName=endpoint_name, EndpointConfigName=endpoint_config_name, ) logger.debug( "AWS create endpoint response: %s", create_endpoint_response ) except ClientError as e: # create/update endpoint failed, will remove previously created config # and then remove the model cleanup_endpoint_config_error = _cleanup_sagemaker_endpoint_config( client=sagemaker_client, name=deployment_spec.bento_name, version=deployment_spec.bento_version, ) if cleanup_endpoint_config_error: cleanup_endpoint_config_error.error_message = ( 'Failed to clean up endpoint config after unsuccessfully ' 'apply SageMaker deployment: %s', cleanup_endpoint_config_error.error_message, ) return ApplyDeploymentResponse( status=cleanup_endpoint_config_error, deployment=deployment_pb ) cleanup_model_error = _cleanup_sagemaker_model( client=sagemaker_client, name=deployment_spec.bento_name, version=deployment_spec.bento_version, ) if cleanup_model_error: cleanup_model_error.error_message = ( 'Failed to clean up model after unsuccessfully ' 'apply SageMaker deployment: %s', cleanup_model_error.error_message, ) return ApplyDeploymentResponse( status=cleanup_model_error, deployment=deployment_pb ) status = _parse_aws_client_exception_or_raise(e) status.error_message = ( 'Failed to apply SageMaker deployment: %s', status.error_message, ) return ApplyDeploymentResponse(status=status, deployment=deployment_pb) res_deployment_pb = Deployment(state=DeploymentState()) res_deployment_pb.CopyFrom(deployment_pb) return ApplyDeploymentResponse( status=Status.OK(), deployment=res_deployment_pb ) except BentoMLException as error: return ApplyDeploymentResponse(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)
def describe(self, deployment_pb, repo): # fetch deployment state deployment_state = DeploymentState() # deployment_state.state = ... return deployment_state
def _apply( self, deployment_pb, bento_pb, yatai_service, bento_path, prev_deployment=None ): if loader._is_remote_path(bento_path): with loader._resolve_remote_bundle_path(bento_path) as local_path: return self._apply( deployment_pb, bento_pb, yatai_service, local_path, prev_deployment ) deployment_spec = deployment_pb.spec sagemaker_config = deployment_spec.sagemaker_operator_config ensure_deploy_api_name_exists_in_bento( [api.name for api in bento_pb.bento.bento_service_metadata.apis], [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) ecr_image_path = create_push_docker_image_to_ecr( sagemaker_config.region, deployment_spec.bento_name, deployment_spec.bento_version, sagemaker_project_dir, ) try: model_name = _create_sagemaker_model( sagemaker_client, deployment_spec.bento_name, deployment_spec.bento_version, ecr_image_path, sagemaker_config.api_name, ) except ClientError as e: status = _parse_aws_client_exception(e) status.error_message = ( 'Failed to create model for SageMaker' ' Deployment: {}'.format(status.error_message) ) return ApplyDeploymentResponse(status=status, deployment=deployment_pb) try: endpoint_config_name = _create_sagemaker_endpoint_config( sagemaker_client, model_name, deployment_spec.bento_name, deployment_spec.bento_version, sagemaker_config, ) except ClientError as e: # create endpoint failed, will remove previously created model cleanup_model_error = _cleanup_sagemaker_model( sagemaker_client, deployment_spec.bento_name, deployment_spec.bento_version, ) if cleanup_model_error: cleanup_model_error.error_message = ( 'Failed to clean up model after unsuccessfully ' 'create endpoint config: %s', cleanup_model_error.error_message, ) return ApplyDeploymentResponse( status=cleanup_model_error, deployment=deployment_pb ) status = _parse_aws_client_exception(e) status.error_message = ( 'Failed to create endpoint config for SageMaker deployment: %s', status.error_message, ) return ApplyDeploymentResponse(status=status, deployment=deployment_pb) endpoint_name = generate_aws_compatible_string( deployment_pb.namespace + '-' + deployment_spec.bento_name ) try: if prev_deployment: logger.debug("Updating sagemaker endpoint %s", endpoint_name) update_endpoint_response = sagemaker_client.update_endpoint( EndpointName=endpoint_name, EndpointConfigName=endpoint_config_name ) logger.debug( "AWS update endpoint response: %s", update_endpoint_response ) else: logger.debug("Creating sagemaker endpoint %s", endpoint_name) create_endpoint_response = sagemaker_client.create_endpoint( EndpointName=endpoint_name, EndpointConfigName=endpoint_config_name ) logger.debug( "AWS create endpoint response: %s", create_endpoint_response ) except ClientError as e: # create/update endpoint failed, will remove previously created config # and then remove the model cleanup_endpoint_config_error = _cleanup_sagemaker_endpoint_config( client=sagemaker_client, name=deployment_spec.bento_name, version=deployment_spec.bento_version, ) if cleanup_endpoint_config_error: cleanup_endpoint_config_error.error_message = ( 'Failed to clean up endpoint config after unsuccessfully ' 'apply SageMaker deployment: %s', cleanup_endpoint_config_error.error_message, ) return ApplyDeploymentResponse( status=cleanup_endpoint_config_error, deployment=deployment_pb ) cleanup_model_error = _cleanup_sagemaker_model( client=sagemaker_client, name=deployment_spec.bento_name, version=deployment_spec.bento_version, ) if cleanup_model_error: cleanup_model_error.error_message = ( 'Failed to clean up model after unsuccessfully apply ' 'SageMaker deployment: {}'.format(cleanup_model_error.error_message) ) return ApplyDeploymentResponse( status=cleanup_model_error, deployment=deployment_pb ) status = _parse_aws_client_exception(e) status.error_message = 'Failed to apply SageMaker ' 'deployment: {}'.format( status.error_message ) return ApplyDeploymentResponse(status=status, deployment=deployment_pb) res_deployment_pb = Deployment(state=DeploymentState()) res_deployment_pb.CopyFrom(deployment_pb) return ApplyDeploymentResponse(status=Status.OK(), deployment=res_deployment_pb)
def apply(self, deployment_pb, yatai_service, prev_deployment=None): try: ensure_docker_available_or_raise() 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, ) ) if bento_pb.bento.uri.type != BentoUri.LOCAL: raise BentoMLException( 'BentoML currently only support local repository' ) else: bento_path = bento_pb.bento.uri.uri bento_service_metadata = bento_pb.bento.bento_service_metadata template = 'aws-python3' if version.parse(bento_service_metadata.env.python_version) < version.parse( '3.0.0' ): template = 'aws-python' api_names = ( [aws_config.api_name] if aws_config.api_name else [api.name for api in bento_service_metadata.apis] ) ensure_deploy_api_name_exists_in_bento( [api.name for api in bento_service_metadata.apis], api_names ) with TempDirectory() as serverless_project_dir: init_serverless_project_dir( serverless_project_dir, bento_path, deployment_pb.name, deployment_spec.bento_name, template, ) generate_aws_lambda_handler_py( deployment_spec.bento_name, api_names, 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, ) logger.info( 'Installing additional packages: serverless-python-requirements' ) install_serverless_plugin( "serverless-python-requirements", serverless_project_dir ) logger.info('Deploying to AWS Lambda') call_serverless_command(["deploy"], serverless_project_dir) res_deployment_pb = Deployment(state=DeploymentState()) res_deployment_pb.CopyFrom(deployment_pb) state = self.describe(res_deployment_pb, yatai_service).state res_deployment_pb.state.CopyFrom(state) return ApplyDeploymentResponse( status=Status.OK(), deployment=res_deployment_pb ) except BentoMLException as error: return ApplyDeploymentResponse(status=exception_to_return_status(error))