Can you provide some thoughts on the benefits of doing ML on Elixir vs. Python? Is the benefit the language semantics? Is it much easier to get distributed work in Elixir ML vs Python ML? Are the tools better/smoother? Are there ML ops improvements? Perhaps there’s a blog post I missed :)
In a nutshell, there has been trends in Python (such as JAX and Thinc.ai) that argue functional programming can provide better abstractions and more composable building blocks for deep learning libraries. And I believe Elixir, as a functional language with Lisp-style macros, is in an excellent position to exploit that - as seen in "numerical definitions" which compile a subset of Elixir to the CPU/GPU.
I also think the Erlang VM, with its distribution and network capabilities, can provide exciting developments in the realm of federated and distributed learning. We aren't exploring those aspects yet but we are getting closer to having the foundation to do so.
Regarding ML ops, I believe one main advantage is explained in this video announcement. When deploying a ML model with Nx, you can embed the model within your applications: you don't need a 3rd-party service because we batch and route requests from multiple cores and multiple nodes withing Erlang/Elixir. This can be specially beneficial for projects like Nerves [5] and we will see how it evolves in the long term (as we _just_ announced it).
Finally, one of the benefits on starting from scratch after Python has paved the way is that we can learn from its ecosystem and provide a unified experience. You can think of Nx as Numpy+JAX+TFServing all in one place and we hope that doing so streamlines the developer experience. This also means libraries like Scholar [2] (which aims to serve a similar role as SciPy) and Meow [3] (for Genetic Algorithms) get to use the same abstractions and compile to the CPU/GPU. The latter can show an order of magnitude improvement over other currently used frameworks [4].
[0]: https://dashbit.co/blog/nx-numerical-elixir-is-now-publicly-... [1]: https://dashbit.co/blog/elixir-and-machine-learning-nx-v0.1 [2]: https://github.com/elixir-nx/scholar/ [3]: https://github.com/jonatanklosko/meow [4]: https://dl.acm.org/doi/10.1145/3512290.3528753 [5]: https://www.nerves-project.org/
Has anyone switched to Elixir from Go for writing web apps? What has been your experience like?
Maybe a more serious question: is anyone using Elixir ML in production? I'm absolutely gobsmacked at the quantity and quality of development effort that's gone into it (and use Livebook daily, though not for ML stuff). It's clearly a major focus for the team. I'm wondering if it's ready for production adoption, and if so, if anyone has used it "in anger" yet.
We were able to completely eliminate a few python services and consolidate to all Elixir for ETL and ML.
Checkout this recent talk about how https://www.amplified.ai/ moved from Python to an Elixir ML stack: https://www.youtube.com/watch?v=Y2Nr4dNu6hI
How easy would it be to support OpenAI's new Whisper transcription model in Bumblebee?
If I have a Huggingface model that I've finetuned, can I load it using Bumblebee? I fine-tuned ConvNext and changed it into a multi-label classifier and saved it as a PyTorch model. It works great but being able to use it in LiveBook instead of Jupyter Notebook would be fantastic.
I think I'd have to convert the format, but what then?
Alternatively to EXLA/Torchx, any thoughts on supporting an ML compiler frontend like Google IREE/MLIR by generating StableHLO or LinAlg? This could pave the way towards supporting multiple hardware targets (Vulkan-based, RVV-based, SME-based etc.) with minimal effort from the framework.
It is late here but feel free to drop questions and I will answer them when I am up. Meanwhile I hope you will enjoy the content we put out: the announcements, example apps, and sample notebooks!