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Related Questions
- What are the key advantages of using distributed training for large-scale language models like BERT?
- How does parallel processing impact the training time and computational resources required for fine-tuning BERT?
- What are the trade-offs between model accuracy and computational cost when using distributed training and parallel processing for BERT fine-tuning?
- Can you explain the concept of data parallelism and model parallelism in the context of distributed training for BERT?
- How does the choice of distributed training framework (e.g. TensorFlow, PyTorch) affect the computational cost of fine-tuning BERT?
- What are some strategies for optimizing the computational cost of fine-tuning BERT using distributed training and parallel processing?
- How does the number of GPUs and nodes used in a distributed training setup impact the overall computational cost of fine-tuning BERT?
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