Powerful Machine Learning at your fingertips

presentation.livemd

Jonatan Kłosko

@jonatanklosko

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Powerful Machine Learning at your fingertips

Mix.install([
  {:nx, "~> 0.5.2"},
  {:exla, "~> 0.5.2"},
  {:bumblebee, "~> 0.3.0"},
  # Livebook integration
  {:kino_bumblebee, "~> 0.3.0"}
])

Nx.global_default_backend(EXLA.Backend)

What are we looking for?

BigBrain.detect_emotion("The show last night was fantastic!")
#=> "joy"

BigBrain.describe_image("image.jpg")
#=> "a cat sitting on a chair"

BigBrain.transcribe("audio.mp3")
#=> "Hate is yelled, love is felt."

Zooming out

Nx

x = Nx.tensor([[1, 2], [3, 4]])

defn softmax(x) do
  Nx.exp(x) / Nx.sum(Nx.exp(x))
end

What we got? => Efficient numerical operations

Axon

model =
  Axon.input("input", shape: {nil, 16})
  |> Axon.dense(64)
  |> Axon.tanh()
  |> Axon.dense(10)
  |> Axon.softmax()

What we got? => Framework for building Neural Network models

Pre-trained models

{:ok, model_info} = Bumblebee.load_model({:hf, "bert-base-uncased"})

{:ok, tokenizer} = Bumblebee.load_tokenizer({:hf, "bert-base-uncased"})

serving = Bumblebee.Text.fill_mask(model_info, tokenizer)

Nx.Serving.run(serving, "The capital of Portugal is [MASK].")

{:ok, model_info} = Bumblebee.load_model({:hf, "microsoft/resnet-50"})
{:ok, featurizer} = Bumblebee.load_featurizer({:hf, "microsoft/resnet-50"})

serving =
  Bumblebee.Vision.image_classification(model_info, featurizer,
    compile: [batch_size: 8],
    defn_options: [compiler: EXLA]
  )

Kino.start_child({Nx.Serving, serving: serving, name: MyServing, partitions: true})

image_input = Kino.Input.image("Image", size: {224, 224})
form = Kino.Control.form([image: image_input], submit: "Run")
frame = Kino.Frame.new()

Kino.async_listen(form, fn %{data: %{image: image}} ->
  if image do
    Kino.Frame.render(frame, Kino.Text.new("Running..."))

    image = image.data |> Nx.from_binary(:u8) |> Nx.reshape({image.height, image.width, 3})

    output = Nx.Serving.batched_run(MyServing, image)

    output.predictions
    |> Enum.map(&amp;{&amp;1.label, &amp;1.score})
    |> Kino.Bumblebee.ScoredList.new()
    |> then(&amp;Kino.Frame.render(frame, &amp;1))
  end
end)

Kino.Layout.grid([form, frame], boxed: true, gap: 16)

Serving everyone

Documentation ->

Piecing it together

LiveView demo ->

> “With Bumblebee, the intersection of AI and web development becomes a harmonious symphony rather than a cacophony of complexity.” ~ GPT 3.5

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