What are the best practices for multiprocessing in machine learning?
An example of an AI chatbot conversation about best practices for multiprocessing in machine learning.
What are the best practices for multiprocessing in machine learning?
When using multiprocessing in machine learning, it's important to consider data sharing, process management, and resource allocation.
Can you elaborate on data sharing?
Sure! Data sharing can be done using shared memory or by passing data through queues. It's crucial to minimize data copying.
What about process management?
Effective process management involves monitoring the number of active processes and ensuring they don't overwhelm system resources.
Are there any specific libraries you recommend?
Yes, libraries like multiprocessing, joblib, and dask are great for handling multiprocessing in Python.
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