What are some applications of data parallelism in AI?

What are some applications of data parallelism in AI?

Data parallelism is widely used in AI for tasks like training deep learning models, image processing, and natural language processing.

Can you explain how it works in deep learning?

In deep learning, data parallelism involves splitting the dataset into smaller batches that can be processed simultaneously across multiple GPUs.

What are the benefits of using data parallelism?

The main benefits include faster training times, improved resource utilization, and the ability to handle larger datasets.

Are there any challenges associated with it?

Yes, challenges include managing communication overhead between devices and ensuring that the model converges correctly.