feat(train): add dry_run=True to train()

[amazeAmazing]

Summary

Add dry_run=True parameter to all trainers (SFT, DPO, RLVR, RLAIF) and evaluators (BenchMark, CustomScorer, LLMAsJudge). When dry_run=True, all validation runs but no job is submitted and no compute is consumed.

What it does

• All existing inline validation runs as normal (IAM role, hyperparameters, recipe constraints, model resolution)
• validate_data_path_exists() validates S3 URIs and DataSet ARNs unconditionally before submission
• dry_run=True short-circuits before the API call, returning None
• Raises with a clear error message on validation failure

Changes

Commit 1: feat(train): add dry_run=True to train()

• data_utils.py — new validate_data_path_exists() (S3 via list_objects_v2, DataSet ARN via describe_hub_content)
• base_trainer.py — add dry_run to train(), _train_serverful_smtj(), _train_hyperpod()
• sft/dpo/rlvr/rlaif_trainer.py — add dry_run, validate data paths, short-circuit
• Trainer notebook examples (SFT, DPO, RLVR, RLAIF)
• Unit tests in existing test files
• Integration test (test_dry_run_integration.py)

Commit 2: feat(evaluate): add dry_run=True to evaluate()

• base_evaluator.py — add dry_run to evaluate() signature
• benchmark/custom_scorer/llm_as_judge_evaluator.py — add dry_run, validate dataset paths (custom scorer + LLM-as-judge), short-circuit before _start_execution()
• Evaluator notebook examples (benchmark, custom_scorer, llm_as_judge)

Testing

• Integration test uploads sample data to default bucket, validates S3 path failures, confirms result is None, verifies no job created via SageMaker API

Issue #, if available:

Description of changes:

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