[SageMaker] [GraphBolt] Add support for launching GraphBolt jobs on SageMaker

[thvasilo]

Issue #, if available:

Description of changes:

• Add a new SageMaker job to convert DistPart data to GraphBolt. This is our only option currently as there's no way to directly use S3 as a writable, shared file system in SageMaker, see Add support for direct S3 access on SageMaker tasks #1081 for details.
• The sagemaker/launch_graphbolt_convert.py will launch the SageMaker job, that downloads the entire partitioned graph to one instance, then runs the GB conversion, one partition at a time. Because DGL writes the new fused CSC graph representation in the same directory as the input data, we can't use one of SageMaker's FastFile modes to stream the data, as that creates read-only filesystems.
• [Optional] We also include an example of how one could use a SageMaker Pipeline to run the GSPartition and GBConvert jobs in sequence, but this can be removed (because SageMaker Pipelines are persistent once created).
• Added unit test mechanism to test sagemaker scripts, we start with testing our parsing logic. To make the scripts available to the runner's python runtime we add the graphstorm/sagemaker/launch directory to the runner's PYTHONPATH.

EDIT: One note about the PR: The changes to the partition launch that use a SageMaker Pipeline are for demonstration purposes, I think I'll remove them alltogether and just have separate partition/gbconvert jobs. But we might want to have an example of how to programmatically build an SM pipeline as an example, e.g. from gsprocessing to training (as SM jobs)

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[classicsong]

How do you launch the convert2graphbolt task?

[jalencato]

In overall I think we need to run separate sagemaker regression test before we actually merge the code to make sure there is no other backward compatibility problem.