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import json
from airflow.contrib.hooks.sagemaker_hook import SageMakerHook
from airflow.models import BaseOperator
from airflow.utils.decorators import apply_defaults
[docs]class SageMakerBaseOperator(BaseOperator):
"""
This is the base operator for all SageMaker operators.
:param config: The configuration necessary to start a training job (templated)
:type config: dict
:param aws_conn_id: The AWS connection ID to use.
:type aws_conn_id: str
"""
template_fields = ['config']
template_ext = ()
ui_color = '#ededed'
integer_fields = []
@apply_defaults
def __init__(self,
config,
aws_conn_id='aws_default',
*args, **kwargs):
super(SageMakerBaseOperator, self).__init__(*args, **kwargs)
self.aws_conn_id = aws_conn_id
self.config = config
self.hook = None
def parse_integer(self, config, field):
if len(field) == 1:
if isinstance(config, list):
for sub_config in config:
self.parse_integer(sub_config, field)
return
head = field[0]
if head in config:
config[head] = int(config[head])
return
if isinstance(config, list):
for sub_config in config:
self.parse_integer(sub_config, field)
return
head, tail = field[0], field[1:]
if head in config:
self.parse_integer(config[head], tail)
return
def parse_config_integers(self):
# Parse the integer fields of training config to integers
# in case the config is rendered by Jinja and all fields are str
for field in self.integer_fields:
self.parse_integer(self.config, field)
def expand_role(self):
pass
def preprocess_config(self):
self.log.info(
'Preprocessing the config and doing required s3_operations'
)
self.hook = SageMakerHook(aws_conn_id=self.aws_conn_id)
self.hook.configure_s3_resources(self.config)
self.parse_config_integers()
self.expand_role()
self.log.info(
'After preprocessing the config is:\n {}'.format(
json.dumps(self.config, sort_keys=True, indent=4, separators=(',', ': ')))
)
[docs] def execute(self, context):
raise NotImplementedError('Please implement execute() in sub class!')