Iris Classification via ONNX Runtime

This example uses a pre-trained model to classify iris flowers. The code for the model is located at our simple-ml-models repository.

Overview

We're making use of the ONNXInferenceWorkflow class to run the model. This is one of many workflows that we currently support in our infernet-ml. Consult the library's documentation for more info on workflows that are supported.

Building & Running the Container in Isolation

Note that this container is meant to be started by the infernet-node. For development & Testing purposes, you can run the container in isolation using the following commands.

Building the Container

Simply run the following command to build the container.

make build

Consult the Makefile for the build command.

Running the Container

To run the container, you can use the following command:

make run

Testing the Container

Run the following command to run an inference:

curl -X POST http://127.0.0.1:3000/service_output \
     -H "Content-Type: application/json" \
     -d '{"source":1, "data": {"input": [[1.0380048, 0.5586108, 1.1037828, 1.712096]]}}'

Note Regarding the Input

The inputs provided above correspond to an iris flower with the following characteristics. Refer to the

Sepal Length: 5.5cm
Sepal Width: 2.4cm
Petal Length: 3.8cm
Petal Width: 1.1cm

Putting this input into a vector and scaling it, we get the following scaled input:

[1.0380048, 0.5586108, 1.1037828, 1.712096]

Refer to this function in the model's repository for more information on how the input is scaled.

For more context on the Iris dataset, refer to the UCI Machine Learning Repository.

Output

By running the above command, you should get a response similar to the following:

[
  [
    [
      0.0010151526657864451,
      0.014391022734344006,
      0.9845937490463257
    ]
  ]
]

The response corresponds to the model's prediction for each of the classes:

['setosa', 'versicolor', 'virginica']

In this case, the model predicts that the input corresponds to the class virginicawith a probability of 0.9845937490463257(~98.5%).

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