README.md

Ortex

Ortex is a wrapper around ONNX Runtime implemented as a (limited) Nx.Backend using Rustler and ort.

ONNX models are a standard machine learning model format that can be exported from most ML libraries like PyTorch and TensorFlow. Ortex allows for easy loading and fast inference of ONNX models using different backends available to ONNX Runtime such as CUDA, TensorRT, Core ML, and ARM Compute Library.

Examples

TL;DR

iex> model = Ortex.load("./models/resnet50.onnx")
#Ortex.Model<
  inputs: [{"input", "Float32", [nil, 3, 224, 224]}]
  outputs: [{"output", "Float32", [nil, 1000]}]>
iex> {output} = Ortex.run(model, Nx.broadcast(0.0, {1, 3, 224, 224}))
iex> output |> Nx.backend_transfer(Nx.BinaryBackend) |> Nx.argmax
#Nx.Tensor<
  s64
  499
>

Inspecting a model shows the expected inputs, outputs, data types, and shapes. Axes with nil represent a dynamic size.

To see more real world examples see examples.

Serving

Ortex also implements Nx.Serving behaviour. To use it in your application's supervision tree consult the Nx.Serving docs.

iex> serving = Nx.Serving.new(Ortex.Serving, model)
iex> batch = Nx.Batch.stack([{Nx.broadcast(0.0, {3, 224, 224})}])
iex> {result} = Nx.Serving.run(serving, batch)
iex> result |> Nx.backend_transfer |> Nx.argmax(axis: 1)
#Nx.Tensor<
  s64[1]
  [499]
>

Installation

Ortex can be installed by adding ortex to your list of dependencies in mix.exs:

def deps do
  [
    {:ortex, "~> 0.1.3"}
  ]
end