Allowing to instantiate the Framework estimator class to support custom Framework containers

[giuseppeporcelli]

Is your feature request related to a problem? Please describe.
No

Describe the solution you'd like
If you build a custom container for SageMaker, you can use the sagemaker-training-toolkit library to provide script mode execution and be able to load user training module from an Amazon S3 archive, following the same approach of the open source deep learning containers implemented by AWS.

Then, running training with this container with the SM Python SDK would benefit from the ability to instantiate the Framework estimator class, in order to leverage on the SDK functionalities which build the sourcedir.tar.gz and upload it to Amazon S3 before starting the training job.

Describe alternatives you've considered
The alternative solution is extending the Framework class for the specific use case.

Additional context
Examples on how to build custom training containers for Amazon SageMaker using the training toolkit. The last example shows how to extend the Framework estimator class.
https://github.com/awslabs/amazon-sagemaker-examples/tree/master/advanced_functionality/sagemaker-custom-training-containers

[nadiaya]

The idea behind the current implementation was that the Framework class should be extended similar to how we did it for all the supported frameworks:
https://github.com/aws/sagemaker-python-sdk/blob/master/src/sagemaker/pytorch/estimator.py#L33

Would that work for your use case?

[giuseppeporcelli]

Well that's one of the alternatives I had considered (see alternatives above) and, actually, the way I have implemented it so far.

However, this poses the problem to find an elegant way to make it available to data scientists that are going to use that implementation: what I mean is that I don't expect each project using such estimator to declare it in a python module, so you should create a python package and install it to, let's say SM notebook instance environments using, for example, lifecycle configurations.

If the PyDSK would allow instantiating a generic framework estimator, custom coding, packaging and install would not be needed.

[nadiaya]

That makes sense. Thank you for the clarification!

[mollyheamazon]

Great news! This issue has been resolved with the release of SageMaker Python SDK v3.

How to Get the Latest Version

You can upgrade to the latest v3 package using pip:

pip install --upgrade sagemaker

##Or specify the version explicitly:
pip install sagemaker>=3.0.0

Additional Resources:

 PyPI: https://pypi.org/project/sagemaker/
 Documentation: https://sagemaker.readthedocs.io/

If you're still experiencing this issue after upgrading, please feel free to reopen this issue or create a new one with updated details.