Problem

When benchmarking models across OpenML suites (e.g., OpenML-CC18 with multiple tasks), users have no visibility into:

• How many tasks have completed
• How long each task takes
• Estimated time remaining
• Whether the process is still running or has stalled

Proposed Solution

Add a run_suite_with_progress() function that wraps suite benchmarking with a progress bar using tqdm.

Benefits

• Better UX: Users can monitor long-running benchmarks
• Debugging: Easy to identify slow/stuck tasks
• Planning: ETA helps users schedule experiments
• AI-on-Demand Platform: Improves platform usability for researchers