Add Arguments for Distributed Mode in Qualification Tool CLI

[parthosa]

Fixes #1430.

This PR adds the initial changes needed in CLI to support distributed execution in the Qualification Tool CLI. It adds arguments to enable distributed mode and sets the stage for future implementation PRs.

Note:

• An environment setup document will be shared internally.

Changes Overview

• Extended RapidsJob: Introduced two subclasses—RapidsDistributedJob and RapidsLocalJob and a concrete class for the OnPrem platform.
• Created a JarCmdArgs class to encapsulate all arguments needed to construct the JAR command.
• Implemented the DistributedToolsConfig class, allowing configurations for distributed tools (like Spark properties) to be specified via the existing --tools_config_file option.

CMD:

spark_rapids qualification --platform onprem --eventlogs /path/to/eventlogs  --verbose --filter_apps all \
 --distributed --tools_config_file /path/to/custom_conf_file.yaml

Sample Config File:

api_version: '1.0'
distributed_tools:
  spark_properties:
    - name: 'spark.executor.memory'
      value: '20g'

Details:

• user_tools/src/spark_rapids_pytools/cloud_api/onprem.py: Added a new class OnPremDistributedRapidsJob and a method create_distributed_submission_job to support distributed RAPIDS jobs. [1] [2]
• user_tools/src/spark_rapids_pytools/rapids/rapids_job.py: Introduced RapidsDistributedJob class and updated methods to handle distributed tool configurations. [1] [2] [3] [4]
• user_tools/src/spark_rapids_pytools/rapids/rapids_tool.py: Added methods to get distributed tools configurations and submit distributed jobs. [1] [2]

Enhancements to argument processing:

• user_tools/src/spark_rapids_pytools/rapids/qualification.py: Added methods to process distributed tools arguments. [1] [2]
• user_tools/src/spark_rapids_tools/cmdli/argprocessor.py: Updated QualifyUserArgModel and build_tools_args to include distributed_tools_enabled. [1] [2]

Platform class updates:

• user_tools/src/spark_rapids_pytools/cloud_api/databricks_aws.py, databricks_azure.py, dataproc.py, dataproc_gke.py, emr.py: Disabled pylint warnings for abstract methods. [1] [2] [3] [4] [5]

Other improvements:

• user_tools/src/spark_rapids_pytools/rapids/qualification.py: Added a check to ensure the DataFrame is not empty before accessing it.
• user_tools/src/spark_rapids_tools/cmdli/tools_cli.py: Added a new parameter distributed to the qualification function.