What are the best practices for using multiprocessing in AI?
When using multiprocessing in AI, it's important to consider factors like task granularity, data sharing, and process management.
Can you explain task granularity?
Task granularity refers to the size of the tasks you break your workload into. Finer granularity can lead to better CPU utilization but may increase overhead.
What about data sharing between processes?
Data sharing can be challenging in multiprocessing. It's best to minimize shared data or use shared memory structures to avoid bottlenecks.
Are there any libraries you recommend for multiprocessing in AI?
Yes, libraries like Python's multiprocessing
, joblib
, and concurrent.futures
are great for implementing multiprocessing in AI applications.
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