What solutions can reduce training time for large-scale generative models without compromising output quality

Question

Can i know What solutions can reduce training time for large-scale generative models without compromising output quality?

Answer 1

Use techniques like mixed precision training, gradient accumulation, distributed training, and efficient batch sizing to reduce training time without compromising output quality.

Here is the code snippet you can refer to:

In the above code we are using the following key points:

• Uses mixed precision training (fp16) to speed up computations and save memory.
• Applies gradient accumulation (steps=4) to handle larger effective batch sizes.
• Optimizes distributed training with DDP settings for multi-GPU efficiency.
• Increases per-device batch size (16) for faster throughput without memory overflow.

Hence, this combination reduces training time while maintaining high-quality output.