Processing Job automatically created when I start a training job

Hi,
I haven't used sagemaker for a while and today I started a training job (with the same old settings I always used before), but this time I noticed that a processing job has been automatically created and it's running while my training job runs (I don't even know what a processing job is). I also checked in the dashboard to be sure, this was not happening before, it's the second time (first time was in December) but I've been using sagemaker for the last two years..
Is this a wanted behaviour? I didn't find anything related in the documentation, but it's important to know because I don't want extra costs..
This is the image used by the processing job, with a instance type of ml.m5.2xlarge which I didn't set anywhere..

929884845733.dkr.ecr.eu-west-1.amazonaws.com/sagemaker-debugger-rules:latest  

And this is how I launch my training job (the entrypoint script is basically Keras code for a MobileNetV3)

import sagemaker
from sagemaker.tensorflow import TensorFlow
from sagemaker import get_execution_role

bucket = 'mybucket'

train_data = 's3://{}/{}'.format(bucket,'train')

validation_data = 's3://{}/{}'.format(bucket,'test')

s3_output_location = 's3://{}'.format(bucket)

hyperparameters = {'epochs': 130, 'batch-size' : 512, 'learning-rate' : 0.0002}

metrics = .. some regex here

tf_estimator = TensorFlow(entry_point='train.py',
role=get_execution_role(),
train_instance_count=1,
train_instance_type='ml.p2.xlarge',
train_max_run=172800,
output_path=s3_output_location,
framework_version='2.3.0',
py_version='py37',
metric_definitions = metrics,
hyperparameters = hyperparameters,
source_dir="data")

inputs = {'train': train_data, 'test': validation_data}
myJobName = 'myname'
tf_estimator.fit(inputs=inputs, job_name=myJobName)

Edited by: rokk07 on Jan 25, 2021 2:55 AM