## Summary of Changes

[Saingsophea]

Summary of Changes

Hello @daihaowz, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request lays the groundwork for a distributed training system by introducing a new DistributedTrainController. This controller manages the lifecycle of distributed training jobs, from allocating resources to initializing and communicating with individual training engines. To support this, core API definitions for scheduling and engine configuration have been updated to be more flexible and expressive. Furthermore, new utility functions have been implemented to enhance the system's reliability in handling distributed operations, particularly concerning connection stability and concurrent task execution.

Highlights

• New Distributed Training Controller: Introduced the DistributedTrainController class, which is responsible for orchestrating distributed training workflows, including worker allocation, engine initialization, and RPC communication among workers.
• Scheduler API Refactoring: The scheduler_api.py has undergone significant changes, including renaming SchedulingConfig to Job and updating it to accept a list of Scheduling tasks. The Worker dataclass now explicitly defines serve_port and extra_ports, and ScheduleStrategy uses Literal for stricter type hinting of strategy types.
• Engine API Enhancements: The Scheduling dataclass in engine_api.py now includes port_count and an optional cmd field. Additionally, the get_scheduling_config method has been updated to return a list of Scheduling configurations, allowing for more complex resource definitions.
• Robust Utility Functions: New utility functions create_engine_with_retry and rpc_call have been added. create_engine_with_retry ensures resilient engine initialization by retrying connections, while rpc_call facilitates concurrent remote procedure calls to workers, leveraging a new wait_future_ordered function for ordered result processing and robust exception handling.

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Originally posted by @gemini-code-assist in inclusionAI/AReaL#414 (comment)

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