# Ortex

`Ortex` is a wrapper around [ONNX Runtime](https://onnxruntime.ai/) implemented as a
(limited) `Nx.Backend` using `Rustler` and [`ort`](https://github.com/pykeio/ort).

This allows for easy loading and fast inference of ONNX models using different backends
available to ONNX Runtime such as CUDA, TensorRT, CoreML, and ARM Compute Library.

## Examples

TL;DR
```elixir
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.

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

```elixir
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`:

```elixir
def deps do
  [
    {:ortex, "~> 0.1.0"}
  ]
end
```