V100 or RTX A6000

I was hoping that just brute-force of gpu will help me get up in the leaderboard. :slight_smile: But apparently that wasn’t the case :D.

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A100 on JV is 40GB, but I wrote to DC as well as the A6000 runned out, and they claim to resolve the issue. I will give it a try and let you know.


This is my plan too :smiley: … just to see if it works, maybe add a few more models in the ensemble and tweak the sizes a bit, maybe get a few tenths of a percent more out …

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I am sure brute-force approach of gpu is working and burning compute is way to be on top of leaderboard atleast the public leaderboard :pray:. TBH that is exactly what I have also done also. I have shared my approach here:

From burning few GPU hours, I don’t think training transformers architectures like Swin, VIT for large epochs doesn’t give great results. That’s why I personally ask to trust the results of Jeremy study on the best vision models for fine tuning, as my it was a late night experiment with Convnext which took me to the top :smile:


We can help at Q Blocks with a lot of GPU options to choose from.

We pool computing from a lot of compute providers to offer GPU capacity at best rates possible.


On A100 80GB, I ended up with 0:48 per epoch, memory usage of 77GB at a batch size of 128.
A batch size of 64 was 0:49 so only marginal improvements with a large batch size.

GPU utilization is not ideal but fairly good.

I can share my image to anyone interested or dependency list. Running on Cuda 11.3 with Pytorch 1.11.0.

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Thanks for sharing Ruben! I think @piotr.czapla might be interested :smiley:

It seems the timings have improved (almost 3x improvement IINM,) is it a special image or some tweaks that were done to bring down the time/epoch from the ones originally mentioned by Piotr above?

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Our FastAI image is outdated (working on it :no_mouth:) so I started with a clean slate.
I took our Ubuntu 20.04 image and installed Fastai with this env.yml:

name: fastai
    - fastchan
    - fastai
    - pytorch
    - defaults
  - cudatoolkit=11.3
  - fastai>=2.7.4
  - jupyterlab
  - python>=3.10.4
  - pytorch=1.11
  - torchvision=0.12
  - pip
  - pip:
    - -r requirements.txt



I had to pip install “timm>=0.6.2.dev0” as well as mentioned above.
We’ll be rolling out an updated fastai image which will run this out of the box.

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Thanks, @mike.moloch :blush: We have also updated to the latest fastai and timm versions.


I still see fastai version as 2.6.3 when latest version v2.7.4 in JL

Fixed now. It was not reflected in the UI :blush: earlier.


@Ruben, thank you for taking care of the issue. I was running the latest version of fastai and timm so the fix is coming from other dependencies (cudatoolkit perhaps?). Let us know once the image is fixed.