ferruzzi commented on code in PR #38693:
URL: https://github.com/apache/airflow/pull/38693#discussion_r1552560048
##########
airflow/providers/amazon/aws/operators/bedrock.py:
##########
@@ -91,3 +96,155 @@ def execute(self, context: Context) -> dict[str, str | int]:
self.log.info("Bedrock %s prompt: %s", self.model_id, self.input_data)
self.log.info("Bedrock model response: %s", response_body)
return response_body
+
+
+class BedrockCustomizeModelOperator(AwsBaseOperator[BedrockHook]):
+ """
+ Create a fine-tuning job to customize a base model.
+
+ .. seealso::
+ For more information on how to use this operator, take a look at the
guide:
+ :ref:`howto/operator:BedrockCustomizeModelOperator`
+
+ :param job_name: A unique name for the fine-tuning job.
+ :param custom_model_name: A name for the custom model being created.
+ :param role_arn: The Amazon Resource Name (ARN) of an IAM role that Amazon
Bedrock can assume
+ to perform tasks on your behalf.
+ :param base_model_id: Name of the base model.
+ :param training_data_uri: The S3 URI where the training data is stored.
+ :param output_data_uri: The S3 URI where the output data is stored.
+ :param hyperparameters: Parameters related to tuning the model.
+ :param check_if_job_exists: If set to true, operator will check whether a
model customization
+ job already exists for the name in the config. (Default: True)
+ :param action_if_job_exists: Behavior if the job name already exists.
Options are "timestamp" (default),
+ and "fail"
+ :param customization_job_kwargs: Any optional parameters to pass to the
API.
+
+ :param wait_for_completion: Whether to wait for cluster to stop. (default:
True)
+ :param waiter_delay: Time in seconds to wait between status checks.
+ :param waiter_max_attempts: Maximum number of attempts to check for job
completion.
+ :param deferrable: If True, the operator will wait asynchronously for the
cluster to stop.
+ This implies waiting for completion. This mode requires aiobotocore
module to be installed.
+ (default: False)
+ :param aws_conn_id: The Airflow connection used for AWS credentials.
+ If this is ``None`` or empty then the default boto3 behaviour is used.
If
+ running Airflow in a distributed manner and aws_conn_id is None or
+ empty, then default boto3 configuration would be used (and must be
+ maintained on each worker node).
+ :param region_name: AWS region_name. If not specified then the default
boto3 behaviour is used.
+ :param verify: Whether or not to verify SSL certificates. See:
+
https://boto3.amazonaws.com/v1/documentation/api/latest/reference/core/session.html
+ :param botocore_config: Configuration dictionary (key-values) for botocore
client. See:
+
https://botocore.amazonaws.com/v1/documentation/api/latest/reference/config.html
+ """
+
+ aws_hook_class = BedrockHook
+ template_fields: Sequence[str] = aws_template_fields(
+ "job_name",
+ "custom_model_name",
+ "role_arn",
+ "base_model_id",
+ "hyperparameters",
+ "check_if_job_exists",
+ "action_if_job_exists",
+ "customization_job_kwargs",
+ )
+
+ def __init__(
+ self,
+ job_name: str,
+ custom_model_name: str,
+ role_arn: str,
+ base_model_id: str,
+ training_data_uri: str,
+ output_data_uri: str,
+ hyperparameters: dict[str, str],
+ check_if_job_exists: bool = True,
+ action_if_job_exists: str = "timestamp",
+ customization_job_kwargs: dict[str, Any] | None = None,
+ wait_for_completion: bool = True,
+ waiter_delay: int = 120,
+ waiter_max_attempts: int = 75,
+ deferrable: bool = conf.getboolean("operators", "default_deferrable",
fallback=False),
+ **kwargs,
+ ):
+ super().__init__(**kwargs)
+ self.wait_for_completion = wait_for_completion
+ self.waiter_delay = waiter_delay
+ self.waiter_max_attempts = waiter_max_attempts
+ self.deferrable = deferrable
+
+ self.job_name = job_name
+ self.custom_model_name = custom_model_name
+ self.role_arn = role_arn
+ self.base_model_id = base_model_id
+ self.training_data_config = {"s3Uri": training_data_uri}
+ self.output_data_config = {"s3Uri": output_data_uri}
+ self.hyperparameters = hyperparameters
+ self.check_if_job_exists = check_if_job_exists
+ self.customization_job_kwargs = customization_job_kwargs or {}
+ self.action_if_job_exists = action_if_job_exists.lower()
+
+ self.valid_action_if_job_exists: set[str] = {"timestamp", "fail"}
+
+ def execute_complete(self, context: Context, event: dict[str, Any] | None
= None) -> str:
+ event = validate_execute_complete_event(event)
+
+ if event["status"] != "success":
+ raise AirflowException(f"Error while running job: {event}")
+
+ self.log.info("Bedrock model customization job `%s` complete.",
self.job_name)
+ return self.hook.get_job_arn(event["job_name"])
+
+ def _validate_action_if_job_exists(self):
+ if self.action_if_job_exists not in self.valid_action_if_job_exists:
+ raise AirflowException(
+ f"Invalid value for argument action_if_job_exists
{self.action_if_job_exists}; "
+ f"must be one of: {self.valid_action_if_job_exists}."
+ )
+
+ def execute(self, context: Context) -> dict:
+ self._validate_action_if_job_exists()
+
+ if self.check_if_job_exists and
self.hook.job_name_exists(self.job_name):
+ if self.action_if_job_exists == "fail":
+ raise AirflowException(f"A Bedrock job with name
{self.job_name} already exists.")
+ self.job_name = f"{self.job_name}-{int(utcnow().timestamp())}"
+ self.log.info("Changed job name to '%s' to avoid collision.",
self.job_name)
+
+ self.log.info("Creating Bedrock model customization job '%s'.",
self.job_name)
+
+ response = self.hook.conn.create_model_customization_job(
+ jobName=self.job_name,
+ customModelName=self.custom_model_name,
+ roleArn=self.role_arn,
+ baseModelIdentifier=self.base_model_id,
+ trainingDataConfig=self.training_data_config,
+ outputDataConfig=self.output_data_config,
+ hyperParameters=self.hyperparameters,
+ **self.customization_job_kwargs,
Review Comment:
Sorry, I don't follow. Is the suggestion to build something like a
"job_parameters" dict in the init and just pass/unpack that here?
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