Multi-GPU Training with Unsloth

Multi-GPU Training with Unsloth

Trelis Research via YouTube Direct link

29:30 Running without multi-gpu single gpu check

15 of 19

15 of 19

29:30 Running without multi-gpu single gpu check

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Classroom Contents

Multi-GPU Training with Unsloth

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  1. 1 0:00 Faster training with multiple GPUs
  2. 2 0:39 Video Overview
  3. 3 1:24 Data parallel versus Pipeline Parallel versus Fully Sharded Data Parallel
  4. 4 6:38 Downloading a jupyter notebook as a python script for multi-gpu, e.g. an unsloth notebook
  5. 5 7:44 Unsloth vs Transformers for multi-gpu
  6. 6 8:13 Modifying a fine-tuning script for distributed data parallel
  7. 7 9:03 Starting up a GPU in one-click for fine-tuning
  8. 8 10:27 Converting a jupyter notebook to a python script
  9. 9 11:30 Installation notes for unsloth and tensorboard, and uv
  10. 10 13:32 Script modifications required for DDP
  11. 11 18:50 Training script run-through, for LoRA
  12. 12 22:46 Setting gradient accumulation steps
  13. 13 24:07 Dataset loading
  14. 14 26:22 Setting up the run name and training parameters
  15. 15 29:30 Running without multi-gpu single gpu check
  16. 16 35:47 Running with multiple GPUs using accelerate config btw torch run can result in run hangs
  17. 17 41:02 Sanity check of running with accelerate and a single gpu
  18. 18 44:48 Open at time of recording issues with loss reporting and using unsloth with batch size larger than one
  19. 19 53:11 Conclusion and shout-outs to spr1nter and rakshith

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