<ul data-eligibleForWebStory="true"><li>Using DataLoader's persistent_workers for multi-processing can speed up tasks in parallel.</li><li>Persistent workers keep processes alive after an epoch, reducing the overhead of creating new subprocesses.</li><li>Updating global variables like job_pool won't affect worker subprocesses due to how forked processes inherit data.</li><li>Utilizing DataLoader's sampler to distribute jobs instead of indices can optimize performance.</li><li>By distributing jobs instead of always creating new processes, tasks can be completed faster.</li><li>Performance tests showed significant improvement compared to creating new processes each time.</li><li>Using DataLoader simplifies multi-processing tasks without needing knowledge of complex multi-processing concepts.</li></ul>

DataLoader is all you need for multi-processing?!

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