fix: pass code_location to create_model for tensorflow estimator deployment

[HCharlie]

https://github.com/aws/sagemaker-python-sdk/issues/4536

Description of changes: add code_location which is passed to tensorflow estimator object but not passed to model in the deploy function.

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Codecov Report

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Additional details and impacted files

@@            Coverage Diff             @@
##           master    #4537      +/-   ##
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- Coverage   87.49%   87.44%   -0.06%     
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  Files         391      389       -2     
  Lines       37254    36889     -365     
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- Hits        32595    32256     -339     
+ Misses       4659     4633      -26     

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[HCharlie]

Hi @mohanasudhan, do you know what's missing for this PR?

[mohanasudhan]

Can you explain your usecase and add unit/integ test?

[HCharlie]

Can you explain your usecase and add unit/integ test?

hi @mohanasudhan, thanks for the reply, I think it's either a bug or I am wrong on how to use the tensorflow estimator. I am using Tensorflow estimator, and pass code_location parameter to the estimator like below, and when the code reaches the esitmator.deploy function, it's not parsing the specified parameter code_location to get the s3 bucket, but trying to create a new default s3 bucket. A more detailed version is specifed here. https://github.com/aws/sagemaker-python-sdk/issues/4536

from sagemaker.tensorflow import TensorFlow
source_dir = 's3://{}/{}/source'.format(bucket, prefix)
output_path = 's3://{}/{}/output'.format(bucket, prefix)

hyperparams = {
    'sagemaker_requirements': 'code/requirements.txt'
}

mnist_estimator = TensorFlow(entry_point='code/mnist.py',
                              base_job_name=base_job_name,
                              output_path=output_path,
                              code_location=source_dir,
                              hyperparameters=hyperparams,
                              role=role,
                              instance_count=2,
                              instance_type='ml.m5.large',
                              framework_version='2.1.0',
                              py_version='py3',
                              distribution={'parameter_server': {'enabled': True}})

## fit
print("start fitting")
mnist_estimator.fit(training_data_uri)

## deploy
print("start deploy")
predictor = mnist_estimator.deploy(initial_instance_count=1, instance_type='ml.m5.large')