# Iris Classification via Torch This example uses a pre-trained model to classify iris flowers. The code for the model is located at our [simple-ml-models](https://github.com/ritual-net/simple-ml-models/tree/main/iris_classification) repository. ## Overview We're making use of the [TorchInferenceWorkflow](https://github.com/ritual-net/infernet-ml/blob/main/src/ml/workflows/inference/torch_inference_workflow.py) class to run the model. This is one of many workflows that we currently support in our [infernet-ml](https://github.com/ritual-net/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. ```bash make build ``` Consult the [Makefile](./Makefile) for the build command. ### Running the Container To run the container, you can use the following command: ```bash make run ``` ## Testing the Container Run the following command to perform an inference: ```bash 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 1. Sepal Length: `5.5cm` 2. Sepal Width: `2.4cm` 3. Petal Length: `3.8cm` 4. Petal Width: `1.1cm` Putting this input into a vector and scaling it, we get the following scaled input: ```python [1.0380048, 0.5586108, 1.1037828, 1.712096] ``` Refer to [this function in the model's repository](https://github.com/ritual-net/simple-ml-models/blob/03ebc6fb15d33efe20b7782505b1a65ce3975222/iris_classification/iris_inference_pytorch.py#L13) for more information on how the input is scaled. For more context on the Iris dataset, refer to the [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets/iris). ### Output By running the above command, you should get a response similar to the following: ```json { "input_data": [ [ 1.0380048, 0.5586108, 1.1037828, 1.712096 ] ], "input_shapes": [ [ 4 ] ], "output_data": [ [ 0.0016699483385309577, 0.021144982427358627, 0.977185070514679 ] ] } ``` The `output_data` corresponds to the model's prediction for each of the classes: ```python ['setosa', 'versicolor', 'virginica'] ``` In this case, the model predicts that the input corresponds to the class `virginica` with a probability of `0.977185070514679` (97.7%).