We are thrilled to introduce Livebook on Hugging Face Spaces! 🎉

Each Hugging Face Space environment offers up to 16GB of RAM and 2 CPU cores for free.

If you’re unfamiliar with Hugging Face (HF), it’s a platform for building, sharing, and collaborating on machine learning applications.

This is our second integration with Hugging Face; the first was through Bumblebee, which brings pre-trained neural network models from Hugging Face to the Elixir community.

We’ve been collaborating with HF to make using Livebook on Spaces a breeze. If you have a Hugging Face account, simply click the one-click deployment button below:

Alternatively, follow the step-by-step tutorial to install Livebook in a Hugging Face Space.

Livebook with GPU acceleration on Hugging Face Spaces

One of the great features of HF Spaces is its ease of upgrading to hardware with GPU accelerators. This is particularly useful for Machine Learning applications, which can be highly parallelized and computationally intensive.

Check out this video to see how easy it is to run Stable Diffusion with Livebook on top of a Hugging Face Space powered by a GPU:

Ready to give it a try for yourself? Start experimenting with Livebook on Hugging Face Spaces now.

We are glad to announce a variety of Neural Networks models are now available to the Elixir community via the Bumblebee project.

We have implemented several models, from GPT2 to Stable Diffusion, in pure Elixir, and you can download training parameters for said models directly from Hugging Face.

To run your first Machine Learning model in Elixir, all you need is three clicks, thanks to our integration between Livebook and Bumblebee. You can also easily embed and run said models within existing Elixir projects.

Watch the video by José Valim covering all of these topics and features:

Running Machine Learning models with Livebook

Here are some examples of what it looks like to use Livebook to run Machine Learning models:

Text to image

Image classification

Text classification

Text generation

Incorporating models into any Elixir project

Thanks to the new Nx.Serving functionality, you should be able to incorporate those models into any existing project with little effort and run them at scale.

You should be able to embed and serve these models as part of your existing Phoenix web applications, integrate them into data processing pipelines with Broadway, and deploy them alongside Nerves embedded systems - without needing 3rd-party dependencies.

Here’s an example of using Bumblebee to run an image classification model inside a Phoenix app:

Integration with Hugging Face

Bumblebee allows you to download and use trained models directly from Hugging Face. Let’s see an example of how we can generate text continuation using the popular GPT-2 model:

{:ok, gpt2} = Bumblebee.load_model({:hf, "gpt2"})
{:ok, tokenizer} = Bumblebee.load_tokenizer({:hf, "gpt2"})

serving = Bumblebee.Text.generation(gpt2, tokenizer, max_new_tokens: 10)

text = "Yesterday, I was reading a book and"
Nx.Serving.run(serving, text)

#=> %{
#=>    results: [
#=>        %{
#=>            text: "Yesterday I was reading a book and I was thinking, 'What's going on?'"
#=>        }
#=>    ]
#=> }

Compiling to CPU/GPU

All of our Neural Networks can be compiled to the CPU/GPU, thanks to projects such as EXLA (based on Google XLA) and Torchx (based on Libtorch).

A massive milestone in our Numerical Elixir effort

This release is a huge milestone in our Numerical Elixir effort, which started almost two years ago. These new features, in particular, are possible thanks to the enormous efforts of José Valim, Jonatan Kłosko, Sean Moriarity, and Paulo Valente. We are also thankful to Hugging Face for enabling collaborative Machine Learning across communities and tools, which played an essential role in bringing the Elixir ecosystem up to speed.

Next, we plan to focus on training and transfer learning of Neural Networks in Elixir, allowing developers to augment and specialize pre-trained models according to the needs of their businesses and applications. We also hope to publish more on our progress in developing traditional Machine Learning algorithms.

Your turn

If you want to give Bumblebee a try, you can:

• Download Livebook v0.8 and automatically generate “Neural Networks tasks” from the “+ Smart” cell menu inside your notebooks.
• We have also written single-file Phoenix applications as examples of Bumblebee models inside Phoenix (+ LiveView) apps. Those should provide the necessary building blocks to integrate Bumblebee into your production app.
• For a more hands-on approach, read some of our notebooks.

If you want to help us build the Machine Learning ecosystem for Elixir, check out the projects above, and give them a try. There are many exciting areas, from compiler development to model building. For instance, pull requests that bring more models and architectures to Bumblebee are welcome. The future is concurrent, distributed, and fun!