Structure your answer by first defining multiprocessing and multithreading, then systematically compare them across the four dimensions: memory isolation, communication patterns, performance trade-offs for CPU vs I/O workloads, and failure containment. Finally, ground your explanation with concrete examples from ML engineering, such as data preprocessing, model training, and inference serving, to show practical judgment.
Pro tip: Emphasize that the choice isn't binary—hybrid approaches like using multiprocessing for data loading and multithreading for I/O within each process are common in ML pipelines. Also, mention that Python's GIL makes multithreading ineffective for CPU-bound tasks, but libraries like NumPy release the GIL, so multithreading can still help in some numerical workloads.
Briefly explain that multiprocessing runs separate processes with their own memory space, while multithreading runs multiple threads within a single process sharing the same memory.
Discuss how multiprocessing provides strong memory isolation, requiring explicit IPC (pipes, queues, shared memory), whereas multithreading shares memory, making communication easy but risking race conditions and requiring synchronization.
Explain that for CPU-bound tasks, multiprocessing leverages multiple cores and avoids GIL limitations, while for I/O-bound tasks, multithreading is often more efficient due to lower overhead and simpler context switching.
Highlight that a crash in one process typically doesn't affect others, providing better fault isolation, while a thread crash can bring down the entire process, affecting all threads.
Give scenarios: use multiprocessing for parallel data preprocessing or hyperparameter tuning; use multithreading for asynchronous I/O in data loading or serving multiple inference requests with a shared model.
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