aws / sagemaker-python-sdk

A library for training and deploying machine learning models on Amazon SageMaker
https://sagemaker.readthedocs.io/
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Receiving configuration error when loading SageMaker Session #4403

Open kijosp opened 5 months ago

kijosp commented 5 months ago

Describe the bug In SageMaker Studio within a SageMaker Notebook instance, we use the SageMaker Python SDK. When using the sagemaker library, we are receiving errors in the SageMaker session and other methods.

It looks like the session tries to read a default config file that doesn't exists.

Not sure how to mitigate the error, and prevent SageMaker to look at default config file.

To reproduce We execute: from sagemaker.session import Session

And receive the following message:

sagemaker.config INFO - Not applying SDK defaults from location: /etc/xdg/sagemaker/config.yaml
Unable to create default JumpStart SageMaker Session due to the following error: None is not of type 'string'

Failed validating 'type' in schema['properties']['SageMaker']['properties']['FeatureGroup']['properties']['Tags']['items']['properties']['Value']:
    {'maxLength': 256,
     'minLength': 0,
     'pattern': '^[\\w\\s\\d_.:/=+\\-@]*$',
     'type': 'string'}

On instance['SageMaker']['FeatureGroup']['Tags'][0]['Value']:
    None.

Then later we execute: sagemaker_session = Session()

And receive this error:

File /opt/conda/lib/python3.8/site-packages/jsonschema/validators.py:1308, in validate(instance, schema, cls, *args, **kwargs)
   1306 error = exceptions.best_match(validator.iter_errors(instance))
   1307 if error is not None:
-> 1308     raise error

ValidationError: None is not of type 'string'

Failed validating 'type' in schema['properties']['SageMaker']['properties']['FeatureGroup']['properties']['Tags']['items']['properties']['Value']:
    {'maxLength': 256,
     'minLength': 0,
     'pattern': '^[\\w\\s\\d_.:/=+\\-@]*$',
     'type': 'string'}

On instance['SageMaker']['FeatureGroup']['Tags'][0]['Value']:
    None

System information Using a SageMaker ml.t3.medium instance with a Data Science 3.0 kernel on SageMaker Studio within a SageMaker Notebook Instance. Using sagemaker version: 2.206.0

Additional context Add any other context about the problem here.

SiddhantOjha17 commented 3 months ago

I am also facing this. How did you solve it ?