Multi-GPU Fine-tuning with DDP and FSDP

Multi-GPU Fine-tuning with DDP and FSDP

Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only! Grab it Dive into the world of multi-GPU fine-tuning with this comprehensive tutorial on Distributed Data Parallel (DDP) and Fully Sharded Data Parallel (FSDP) techniques. Learn how to optimize VRAM usage, understand the intricacies of the Adam optimizer, and explore the trade-offs between various distributed training methods. Gain practical insights on choosing the right GPU setup, implementing LoRA and quantization for VRAM reduction, and utilizing tools like DeepSpeed and Accelerate. Follow along with code examples for Model Parallel, DDP, and FSDP implementations, and discover how to set up and use rented GPUs via SSH. By the end of this tutorial, you'll be equipped with the knowledge to efficiently fine-tune large language models across multiple GPUs.