release

Releases all resources held by the learning engine and performs cleanup.

This method implements proper resource management by cleaning up computational resources, cancelling background tasks, and releasing memory allocations. It should be called when the engine is no longer needed to prevent resource leaks and ensure graceful shutdown.

Resource cleanup scope:

• Memory allocations: Free model weights, buffers, and intermediate results

• Background tasks: Cancel training jobs, optimization tasks, and monitoring

• File handles: Close any open files, logs, or temporary storage

• Network connections: Terminate any active network resources

• Hardware resources: Release GPU memory, specialized processors, or accelerators

Cleanup process:

1. Graceful shutdown: Allow current operations to complete when possible

2. Task cancellation: Cancel background coroutines and async operations

3. Resource deallocation: Free memory and release hardware resources

4. State finalization: Ensure any pending state changes are persisted

5. Verification: Confirm all resources have been properly released

Thread safety and coordination:

• Safe to call concurrently with other engine operations

• Blocks until all cleanup operations are complete

• Ensures no resource access after release completion

• Coordinates with container shutdown procedures

Throws