fix: add fixes for tarfile extractall functionality PEP-721

[mufaddal-rohawala]

Issue #, if available:

Description of changes:

• add fixes for tarfile extractall functionality per PEP-721. As part of the fix we rely on tarfile liraries data filter functionality to have a secure path forward to . Since, per PEP-721, the data filter attribute is backported to all historical python versions, including being the default behaviour in Python 3.12, the users would need to update their Python Environments to run the related operartion.

• Testing done:*

• Local Testing.

• Made sure all units/integs are successful.

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[mufaddal-rohawala]

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[codecov]

Codecov Report

Attention: Patch coverage is 90.47619% with 2 lines in your changes are missing coverage. Please review.

Project coverage is 86.78%. Comparing base (5559ba3) to head (f174fa4).
Report is 4 commits behind head on master.

Files	Patch %	Lines
src/sagemaker/utils.py	77.77%	2 Missing ⚠️

Additional details and impacted files

@@            Coverage Diff             @@
##           master    #4441      +/-   ##
==========================================
- Coverage   86.82%   86.78%   -0.05%     
==========================================
  Files         386      386              
  Lines       35710    35742      +32     
==========================================
+ Hits        31006    31017      +11     
- Misses       4704     4725      +21     

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[mufaddal-rohawala]

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[mufaddal-rohawala]

/bot run local-mode-tests, unit-tests

[mufaddal-rohawala]

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[mufaddal-rohawala]

/bot run unit-tests

[mufaddal-rohawala]

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[kflow-stash]

this change is preventing loading saved model tarball files (any saved tar.gz model file in s3 that was created with python<3.12). I am using Python 3.10.6 and attempting to run the deploy method for sagemaker.mxnet.MXNetModel. please include a "fully_trusted" parameter to avoid generating this exception automatically when loading known and trusted tarball models.

tuning_best_model = MXNetModel(
   model_data=os.path.join(output_path_best_tuning_job, "model.tar.gz"),
   role=role,
   entry_point="inference.py",
   source_dir="lstnet",
   py_version="py37",
   name=solution_prefix + "-hpo-tuning-model",
   framework_version="1.8.0",
   code_location=code_location,
   enable_network_isolation=True,  # Set enable_network_isolation=True to ensure a security running environment
   env={"MMS_DEFAULT_RESPONSE_TIMEOUT": "1000"},
)

tuning_predictor = tuning_best_model.deploy(
   instance_type=inference_instance_type,
   endpoint_name=endpoint_name,
   initial_instance_count=1,
)

[mohanasudhan]

@mufaddal-rohawala

[mufaddal-rohawala]

@kflow-stash the data filter patch is backported to all currently supported python version's latest patch per https://peps.python.org/pep-0721/. So if you update the python from patch 3.10.6 -> 3.10.13 then the functionality should work.

May I ask whats the environment used here [to be able to get the python to latest patch], is it specific to any SageMaker offerings?