I’ve been exploring distributed training options and came across numerous articles on Distributed Data Parallel (DDP) and Data Parallel (DP) techniques. However, I find the information somewhat scattered and not very clear, especially in the context of Hugging Face’s capabilities. I’m reaching out to clarify my understanding and to learn how to best leverage these technologies for my projects. I have a couple of specific questions:
- Does Hugging Face natively support DDP and DP for model training? I am interested in knowing whether these parallel processing technologies are integrated into the Hugging Face ecosystem and how they can be utilized for efficient training.
- If Hugging Face supports DDP and DP, could you provide some guidance or examples on how to implement these methods in training? Practical examples or documentation links would be incredibly helpful.
For context, I am working with a system equipped with 2 T4 GPUs and am keen on optimizing my training processes to make the best use of my hardware.
I appreciate any insights or experiences you could share regarding the use of DDP and DP within the Hugging Face framework. Thank you in advance for your time and assistance.