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Cog: Containers for Machine Learning

github.com

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Re: Cog: Containers for Machine Learning

#11

Post by bfirsh »

anonymousDan wrote: post #7

Is there an easy way to create a similar dev environment for training on Linux that will take care of all the CUDA driver nonsense?

You can do this with Cog! Once you've written cog.yaml, you can run arbitrary commands inside the environment which has CUDA installed correctly:

  $ cog run python train.py

Re: Cog: Containers for Machine Learning

#12

Post by ellisv »

My first reaction was: sigh _another_ tool to help ML/DS folk not write a Dockerfile? Aren't there enough already?

But at closer glance cog seems to have an edge on some of the competitors like Seldon or Bento - namely using modern Python libraries (like Pedantic and FastAPI), CUDA/cuDNN/PyTorch/Tensorflow/Python compatibility, and (probably most important to me) automatic queue workers.

It generated a 1GB image with nothing but Python 3.8 in the config, so folks who really care about deployment size would want to continue writing their own container files.

Re: Cog: Containers for Machine Learning

#13

Post by justinsaccount »

p1esk wrote: post #10
uniqueuid wrote: post #3

This, here is going to save time in the order of (wo-)man lifetimes! > No more CUDA hell. Cog knows which CUDA/cuDNN/PyTorch/Tensorflow/Python combos are compatible and will set it all up correctly for you. I've personally spent more than a week of my life in sum sorting this out. It's really overdue someone stops the madness!

No cuda hell if you’re using pytorch. A single pip or conda command installs everything you need.

It only does that by installing a bunch of pre-compiled shared libraries

https://hpc.guix.info/blog/2021/09/whats-in-a-package/

Re: Cog: Containers for Machine Learning

#14

Post by p1esk »

justinsaccount wrote: post #13
p1esk wrote: post #10

Earlier quoted context omitted.

No cuda hell if you’re using pytorch. A single pip or conda command installs everything you need.

It only does that by installing a bunch of pre-compiled shared libraries https://hpc.guix.info/blog/2021/09/whats-in-a-package/

Awesome, right?
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