livebook VS axon

How's the maturity compared to Livebook?

https://livebook.dev/

2) Start using IEx or LiveBook for any day to day scripting that I would normally use Python for.

Definitely look into Livebook and Elixir, and the whole ecosystem around it, including:

- https://github.com/elixir-nx/axon Nx-powered Neural Networks

- https://github.com/elixir-nx/nx Multi-dimensional arrays (tensors) and numerical definitions for Elixir

- https://github.com/elixir-nx/scholar Traditional machine learning on top of Nx

- https://github.com/elixir-nx/bumblebee Pre-trained Neural Network models in Axon (+ Models integration)

- https://github.com/elixir-explorer/explorer Series (one-dimensional) and dataframes (two-dimensional) for fast and elegant data exploration in Elixir

- https://fly.io/blog/rethinking-serverless-with-flame/ (for offloading large work to remote containers)

- https://www.youtube.com/watch?v=RABXu7zqnT0 InstructorEx

And of course Livebook (https://livebook.dev)

I have always considered helping the community grow into a diverse ecosystem to be my main responsibility (the Python community being a great example here).

This particular effort started because some people got together and realized that we could do it! Do it in a way that felt part of Elixir and not just a bunch of bindings to C libraries.

We honestly never had the expectation that we had to beat Python (otherwise we would simply not have started). Early on, we were not even sure if we could be better at one single thing. However, 3 years later, we do have features that would be quite hard or impossible to implement in Python. For example:

* Nx Serving - https://hexdocs.pm/nx/Nx.Serving.html - allows you to serve machine learning models, across nodes and GPUs, with concurrency, batching, and partitioning, and it has zero dependencies

* Livebook - https://livebook.dev - brings truly reproducible workflows (hard to achieve in Python due to mutability), smart cells, and other fresh ideas

* A more cohesive ecosystem - Nx, Scholar, Explorer, etc all play together, zero-copy and all, because they are the only players in town

Of course, there are also things that Python can do, that we cannot:

* In Python, integration with C code is easier, and that matters a lot in this space. Python also allows C to call Python, and that's just not possible in the Erlang VM

* Huge ecosystem, everything happens in Python first

At the end of the day, what drives me is that the Erlang VM offers a unique set of features, and combining them with different problems have historically lead to interesting and elegant solutions. Which drives more people to join, experiment, run in production, and create new things.

To get started you need a running instance of Livebook

Definitely look into Livebook and Elixir, and the whole ecosystem around it, including:

- https://github.com/elixir-nx/axon Nx-powered Neural Networks

- https://github.com/elixir-nx/nx Multi-dimensional arrays (tensors) and numerical definitions for Elixir

- https://github.com/elixir-nx/scholar Traditional machine learning on top of Nx

- https://github.com/elixir-nx/bumblebee Pre-trained Neural Network models in Axon (+ Models integration)

- https://github.com/elixir-explorer/explorer Series (one-dimensional) and dataframes (two-dimensional) for fast and elegant data exploration in Elixir

- https://fly.io/blog/rethinking-serverless-with-flame/ (for offloading large work to remote containers)

- https://www.youtube.com/watch?v=RABXu7zqnT0 InstructorEx

And of course Livebook (https://livebook.dev)

My recommendation is to start with fast.ai to understand the machine learning part. Then, for the elixir bit, look at some of the notebooks in the Axon (elixir's NN library) github. I wrote a couple notebooks explaining how to train a basic NN using Axon. Here's one

Machine learning is rapidly expanding within the Elixir ecosystem, with tools such as Nx, Axon, and Explorer being used both by individuals and companies such as Amplified, as mentioned above.

It's my professional daily driver since 2018 but I consider it an average-to-disappointing language and ecosystem on top of an incredible VM/runtime. For more specific thoughts, back in 2020 I've previously posted some critique here and very little of these concerns are improved in the interim. There is a vestigial ML story around libraries like Nx/Axon. LiveView is inadvisable in practice but is sort of the banner marketing device right now, which disappoints me.

Yeah, as Mark said, I think the problem is related to this issue https://github.com/elixir-nx/axon/issues/244