This content originally appeared on DEV Community and was authored by Furkan Gözükara
I have done an extensive multi-GPU FLUX Full Fine Tuning / DreamBooth training experimentation on RunPod by using 2x A100–80 GB GPUs (PCIe) since this was commonly asked of me.
Image 1
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Image 1 shows that only first part of installation of Kohya GUI took 30 minutes on a such powerful machine on a very expensive Secure Cloud pod — 3.28 USD per hour
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There was also part 2, so just installation took super time
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On Massed Compute, it would take like 2–3 minutes
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This is why I suggest you to use Massed Compute over RunPod, RunPod machines have terrible hard disk speeds and they are like lottery to get good ones
Image 2, 3 and 4
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Image 2 shows speed of our very best config FLUX Fine Tuning training shared below when doing 2x Multi GPU training
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Used config name is : Quality_1_27500MB_6_26_Second_IT.json
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Image 3 shows VRAM usage of this config when doing 2x Multi GPU training
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Image 4 shows the GPUs of the Pod
Image 5 and 6
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Image 5 shows speed of our very best config FLUX Fine Tuning training shared below when doing a single GPU training
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Used config name is : Quality_1_27500MB_6_26_Second_IT.json
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Image 6 shows this setup used VRAM amount
Image 7 and 8
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Image 7 shows speed of our very best config FLUX Fine Tuning training shared below when doing a single GPU training and Gradient Checkpointing is disabled
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Used config name is : Quality_1_27500MB_6_26_Second_IT.json
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Image 8 shows this setup used VRAM amount
Image 9 and 10
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Image 9 shows speed of our very best config FLUX Fine Tuning training shared below when doing 2x Multi GPU training — this time Fused Backward Pass is disabled
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Used config name is : Quality_1_27500MB_6_26_Second_IT.json
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Image 10 shows this setup used VRAM amount
Image 11
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Image 2 shows speed of our very best config FLUX Fine Tuning training shared below when doing 2x Multi GPU training on a different Pod
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Used config name is : Quality_1_27500MB_6_26_Second_IT.json
Conclusions
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For multi-GPU FLUX Fine Tuning, you have to use at least 80 GB GPUs
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When doing multi-GPU FLUX Fine Tuning, Fused Backward Pass brings 0 VRAM usage improvements but slows down the training — I am going to report this
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With A100 GPU, you are able to reach 2.89 second / it — probably it will get better as you do more steps
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With 2x A100 GPU, you are able to reach 4.1 second / it — effective speed 2.05 second / it
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The speed gain is 0.75 / 2.9 = 26% — so 2x GPU training totally doesn’t worth it at the moment
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If speed drop stays same due to multi-GPU overhead, 8x A100 may be beneficial but you have to experiment it properly and calculate speed gain
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Currently single L40S would be way cheaper and faster
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When doing multi-GPU FLUX LoRA training, we almost gain linear speed increase — I have tested with 8x RTX A6000 : https://www.patreon.com/posts/110879657
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As shown in Image 11, there is also a chance that you will get a way worse performing pod
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With same 2x A100 GPU and no visible difference, that random pod performed 1/4 speed of another same config pod — terrible terrible speed
This content originally appeared on DEV Community and was authored by Furkan Gözükara
Furkan Gözükara | Sciencx (2024-09-21T16:42:16+00:00) Multi-GPU FLUX Full Fine Tuning Experiments and Requirements on RunPod and Conclusions. Retrieved from https://www.scien.cx/2024/09/21/multi-gpu-flux-full-fine-tuning-experiments-and-requirements-on-runpod-and-conclusions/
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