I've also been doing the fastai course, where you learn Gradio and pytorch.
Python has such a messy library story. I'm not a python developer, and coming into this ecosystem and trying to make things work with pip, conda, docker, etc. It's a mess.
I like Gradio, and built a few small apps, but it is still messy compared to Bumblebee.
Livebook + Bumblebee is magical. I'm productive in an instant, and the opportunity to build with Elixir and Phoenix makes this so exciting.
I agree on python libraries. There are probably contextual/philosophical reasons why the dependency management works the way it does, but I also don't use it a ton so its not obvious to me. RubyGems with Bundler and Node with NPM/Yarn have pretty much just worked for me. Pip and pipenv just feel clunkier somehow and there seems to be more tweaking to get things working.
I don't love the node/npm library ecosystem entirely, but it seems like at least the tools are consistent: I can generally run npm or yarn or pnpm and it will work with the same package.json file. There are often weird issues with ES6 or common JS, but generally the libraries have a fix when that is an issue.
With python, when I look at a new repository, I'm always trying to figure out, ok, does it have a requirements.txt (which means pip, right?). Or, do I need to use conda and activate it? Does anyone still use virtualenv (and why not)? And, often the requirements listed in the repository seem to only work in certain contexts. I've tried normalizing it all using a docker wrapper, but, especially for ML work, I'm then fighting with the CUDA libraries, and running against the correct GPU host libraries, etc. It feels so complicated and requires you to have been in the ecosystem for years to have the intuition on where to go next.
And, it probably makes things even more complicated that my host system is nixos: I don't think python ever considered immutable filesystems in the worldview.
I was hoping that the fastai course would smooth out some of this for me, but when I started trying to run on my own hardware, it quickly fell apart. This has always been the case for me with ML: trying to just get something running so I can experiment is such a battle that I give up.
Elixir just seems like it has a more sane approach for libraries and generally seems to work the right way. That's a good foundation for progress for me.
The ergonomics of Bumblebee are so perfect.
I've also been doing the fastai course, where you learn Gradio and pytorch.
Python has such a messy library story. I'm not a python developer, and coming into this ecosystem and trying to make things work with pip, conda, docker, etc. It's a mess.
I like Gradio, and built a few small apps, but it is still messy compared to Bumblebee.
Livebook + Bumblebee is magical. I'm productive in an instant, and the opportunity to build with Elixir and Phoenix makes this so exciting.
I'm blown away.