Apache Airflow dags w/ backend configuration bundle.
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#
# Licensed to the Apache Software Foundation (ASF) under one
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# to you under the Apache License, Version 2.0 (the
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#
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# software distributed under the License is distributed on an
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# specific language governing permissions and limitations
# under the License.
from typing import Optional
from airflow.exceptions import AirflowException
from airflow.providers.amazon.aws.hooks.base_aws import AwsBaseHook
from airflow.providers.amazon.aws.operators.sagemaker_base import SageMakerBaseOperator
from airflow.utils.decorators import apply_defaults
from botocore.exceptions import ClientError
class SageMakerEndpointOperator(SageMakerBaseOperator):
"""
Create a SageMaker endpoint.
This operator returns The ARN of the endpoint created in Amazon SageMaker
:param config:
The configuration necessary to create an endpoint.
If you need to create a SageMaker endpoint based on an existed
SageMaker model and an existed SageMaker endpoint config::
config = endpoint_configuration;
If you need to create all of SageMaker model, SageMaker endpoint-config and SageMaker endpoint::
config = {
'Model': model_configuration,
'EndpointConfig': endpoint_config_configuration,
'Endpoint': endpoint_configuration
}
For details of the configuration parameter of model_configuration see
:py:meth:`SageMaker.Client.create_model`
For details of the configuration parameter of endpoint_config_configuration see
:py:meth:`SageMaker.Client.create_endpoint_config`
For details of the configuration parameter of endpoint_configuration see
:py:meth:`SageMaker.Client.create_endpoint`
:type config: dict
:param aws_conn_id: The AWS connection ID to use.
:type aws_conn_id: str
:param wait_for_completion: Whether the operator should wait until the endpoint creation finishes.
:type wait_for_completion: bool
:param check_interval: If wait is set to True, this is the time interval, in seconds, that this operation
waits before polling the status of the endpoint creation.
:type check_interval: int
:param max_ingestion_time: If wait is set to True, this operation fails if the endpoint creation doesn't
finish within max_ingestion_time seconds. If you set this parameter to None it never times out.
:type max_ingestion_time: int
:param operation: Whether to create an endpoint or update an endpoint. Must be either 'create or 'update'.
:type operation: str
"""
@apply_defaults
def __init__(
self,
*,
config: dict,
wait_for_completion: bool = True,
check_interval: int = 30,
max_ingestion_time: Optional[int] = None,
operation: str = "create",
**kwargs,
):
super().__init__(config=config, **kwargs)
self.config = config
self.wait_for_completion = wait_for_completion
self.check_interval = check_interval
self.max_ingestion_time = max_ingestion_time
self.operation = operation.lower()
if self.operation not in ["create", "update"]:
raise ValueError(
'Invalid value! Argument operation has to be one of "create" and "update"'
)
self.create_integer_fields()
def create_integer_fields(self) -> None:
"""Set fields which should be casted to integers."""
if "EndpointConfig" in self.config:
self.integer_fields = [
["EndpointConfig", "ProductionVariants", "InitialInstanceCount"]
]
def expand_role(self) -> None:
if "Model" not in self.config:
return
hook = AwsBaseHook(self.aws_conn_id, client_type="iam")
config = self.config["Model"]
if "ExecutionRoleArn" in config:
config["ExecutionRoleArn"] = hook.expand_role(config["ExecutionRoleArn"])
def execute(self, context) -> dict:
self.preprocess_config()
model_info = self.config.get("Model")
endpoint_config_info = self.config.get("EndpointConfig")
endpoint_info = self.config.get("Endpoint", self.config)
if model_info:
self.log.info("Creating SageMaker model %s.", model_info["ModelName"])
self.hook.create_model(model_info)
if endpoint_config_info:
self.log.info(
"Creating endpoint config %s.",
endpoint_config_info["EndpointConfigName"],
)
self.hook.create_endpoint_config(endpoint_config_info)
if self.operation == "create":
sagemaker_operation = self.hook.create_endpoint
log_str = "Creating"
elif self.operation == "update":
sagemaker_operation = self.hook.update_endpoint
log_str = "Updating"
else:
raise ValueError(
'Invalid value! Argument operation has to be one of "create" and "update"'
)
self.log.info(
"%s SageMaker endpoint %s.", log_str, endpoint_info["EndpointName"]
)
try:
response = sagemaker_operation(
endpoint_info,
wait_for_completion=self.wait_for_completion,
check_interval=self.check_interval,
max_ingestion_time=self.max_ingestion_time,
)
except ClientError: # Botocore throws a ClientError if the endpoint is already created
self.operation = "update"
sagemaker_operation = self.hook.update_endpoint
log_str = "Updating"
response = sagemaker_operation(
endpoint_info,
wait_for_completion=self.wait_for_completion,
check_interval=self.check_interval,
max_ingestion_time=self.max_ingestion_time,
)
if response["ResponseMetadata"]["HTTPStatusCode"] != 200:
raise AirflowException(f"Sagemaker endpoint creation failed: {response}")
else:
return {
"EndpointConfig": self.hook.describe_endpoint_config(
endpoint_info["EndpointConfigName"]
),
"Endpoint": self.hook.describe_endpoint(endpoint_info["EndpointName"]),
}