I have been training and fine-tuning large language models using PyTorch recently. During this process, I am looking to better understand and monitor the inter-GPU communication, the transfer of parameters and operators, as well as the usage of GPU memory and CPU memory.
Specifically, I am facing the following challenges:
- How can I monitor the communication and data transfer efficiency in a multi-GPU environment?
- What are the best practices to accurately measure and optimize the memory usage of the GPU in my model?
- Are there tools or techniques that can help me better monitor the usage of CPU memory during training?
I have tried using nvidia-smi to observe GPU usage, but I find the information provided somewhat limited. I am looking for more detailed analysis, especially in a distributed training context.