SageMaker example: make downloads conditional

[jameslamb]

Proposes the following updates to https://docs.rapids.ai/deployment/nightly/cloud/aws/sagemaker/

• guard downloads of the HIGGS.csv file with if statements so you don't have to wait to re-download the data if it already exists locally

  • helpful, because downloading the data can take 10+ minutes

• put a floor of sagemaker>=2.198 in the lifecycle configuration script used to create the rapids JupyterLab kernel for the SageMaker notebook instance

Notes for Reviewers

I'd originally also added a floor on the sagemaker Python here, but reverted that per #446 (comment).

details about why I'd added that sagemaker floor at first (click me)

Without that floor on sagemaker, even just running import sagemaker at the top of a notebook fails like this:

TypeError                                 Traceback (most recent call last)
Cell In[1], line 4
      1 import time
      3 import boto3
----> 4 import sagemaker

File ~/anaconda3/envs/rapids/lib/python3.11/site-packages/sagemaker/__init__.py:18
     14 from __future__ import absolute_import
     16 import importlib_metadata
---> 18 from sagemaker import estimator, parameter, tuner  # noqa: F401
     19 from sagemaker.amazon.kmeans import KMeans, KMeansModel, KMeansPredictor  # noqa: F401
     20 from sagemaker.amazon.pca import PCA, PCAModel, PCAPredictor  # noqa: F401

...

File ~/anaconda3/envs/rapids/lib/python3.11/site-packages/google/protobuf/descriptor.py:621, in FieldDescriptor.__new__(cls, name, full_name, index, number, type, cpp_type, label, default_value, message_type, enum_type, containing_type, is_extension, extension_scope, options, serialized_options, has_default_value, containing_oneof, json_name, file, create_key)
    615 def __new__(cls, name, full_name, index, number, type, cpp_type, label,
    616             default_value, message_type, enum_type, containing_type,
    617             is_extension, extension_scope, options=None,
    618             serialized_options=None,
    619             has_default_value=True, containing_oneof=None, json_name=None,
    620             file=None, create_key=None):  # pylint: disable=redefined-builtin
--> 621   _message.Message._CheckCalledFromGeneratedFile()
    622   if is_extension:
    623     return _message.default_pool.FindExtensionByName(full_name)

TypeError: Descriptors cannot be created directly.
If this call came from a _pb2.py file, your generated code is out of date and must be regenerated with protoc >= 3.19.0.
If you cannot immediately regenerate your protos, some other possible workarounds are:
 1. Downgrade the protobuf package to 3.20.x or lower.
 2. Set PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python (but this will use pure-Python parsing and will be much slower).

More information: https://developers.google.com/protocol-buffers/docs/news/2022-05-06#python-updates

Conda was choosing a mix of very-old sagemaker and very-new protobuf:

  - protobuf=5.27.5=py311hfdbb021_0
  - protobuf3-to-dict=0.1.5=py311h38be061_9
  ...
  - sagemaker=2.75.1=pyhd8ed1ab_0

Forcing installation of a newer sagemaker (that controls its protobuf dependency better) seems to help.

Some related discussion of this issue:

• [Bug Report] "Getting Started" notebook fails on the first few cells in Sagemaker Studio aws/amazon-sagemaker-examples#4387
• https://stackoverflow.com/questions/72441758/typeerror-descriptors-cannot-not-be-created-directly

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