Distributed Computing
Distributed computing for LLM training spreads computation and data across multiple GPUs or nodes to train models too large or slow for a single device, using data, tensor, and pipeline parallelism.
What is Distributed Computing?
Distributed computing for LLM training spreads computation and data across multiple GPUs or nodes to train models too large or slow for a single device, using data, tensor, and pipeline parallelism.
Distributed computing for LLM training spreads computation and data across multiple GPUs or nodes to train models too large or slow for a single device, using data, tensor, and pipeline parallelism.
Where is it used?
GPT-4 and Llama-3 training use thousands of GPUs across nodes with Megatron-LM and FSDP; Hugging Face `Accelerate` and `torch.distributed` orchestrate multi-GPU training.
How to build it
Wrap your model with `accelerate.Accelerator()`, call `accelerator.prepare(model, optimizer, dataloader)`, and launch with `accelerate launch --multi_gpu --num_processes 8 train.py`.