Everyone has their own way to do this. Every step is broken by some unfamiliar dependency that requires special arcane knowledge to fix. Part of me is a grumpy old man that doesn’t gravitate to the shiny new tools that come out every week that the younger devs keep up with :)

pip and venv are neither shiny nor new, it's the standard way of doing things for a while. I am an outsider to python and am incredibly thankful for this standardization, because i agree getting python env set up correctly before venv was a huge pain

If your guys arent on this I'd suggest you get them on it, it dramatically simplifies setup

Here is a tiny excerpt try to get dvc to work just so I could get the training weights for deployment ... remember I don't develop much w Python...

    $ dvc pull
    Command 'dvc' not found, but can be installed with:
    sudo snap install dvc

    $ sudo snap install dvc
    error: This revision of snap "dvc" was published using classic confinement and thus may perform
    arbitrary system changes outside of the security sandbox that snaps are usually confined to,
    which may put your system at risk.

    If you understand and want to proceed repeat the command including --classic.

 ok I get dvc installed somehow -- don't remember. Time to get the weights...

    $ python3 -m dvc pull
    ERROR: unexpected error - Forbidden: An error occurred (403) when calling the HeadObject operation: Forbidden            

    Having any troubles? Hit us up at https://dvc.org/support, we are always happy to help!

Finally I just have my colleague manually copy the weights. This kind of thing went for hours.

Researchers are notorious for writing bad code

What even is dvc

I think dvc is like git for large binary files. You need someway to manage your NN weights -- what are other methods?

git lfs is what everyone is using, HF in particular

Hey, DVC maintainer here.

Thanks for giving DVC a try!

With snap, you need to use `--classic` flag, as noted in https://dvc.org/doc/install/linux#install-with-snap Unfortunately that's just how snap works for us there :(

Regarding the pull error, it simply looks like you don't have some credentials set up. See https://dvc.org/doc/user-guide/data-management/remote-storag... Still, the error could be better, so that's on us.

Feel free to ping us in discord (see invite link in https://dvc.org/support). I'm @ruslan there. We'll be happy to help.

Thanks, Ruslan

Thanks… i know my colleague uses it a lot. I generally use his models and don’t do much ML development yet. At some point I need to properly learn all of this. It seems ML tools are only for developers not for those who simply want to deploy and use the resulting NN.

Unfortunatly its not that simple expecially for NVIDIA driver and cuda install. That's why we usually use conda that can handle cuda install but even with that some time it work flawlessly and some time not.

> Are they not using venvs or something? It should be as simple as python -m venv venv; ./activate; pip install -r requirements.txt

In most cases, it would be possible to do close to that, but it is extremely common to run into things being distributed in the AI/ML space with install instructions that don’t include that, and instruct you to have a global install of a certain Python version, and then to pip install the dependencies (and globally install non-Python package dependencies, if there are any), so even if they’d work in a venv, you have to (1) indepently know you should be doing that, and (b) translate the instructions – which where (1) applies is usually trivial if all the dependencies are proper python packages, but can be more involved otherwise.

So, yeah, I can see that a lot of the time the path of least resistance is just to create an isolated container environment for it.