ritual/projects/onnx-iris/container/README.md

97 lines
2.6 KiB
Markdown

# 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](https://github.com/ritual-net/simple-ml-models/tree/main/iris_classification)
repository.
## Overview
We're making use of
the [ONNXInferenceWorkflow](https://github.com/ritual-net/infernet-ml-internal/blob/main/src/ml/workflows/inference/onnx_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-internal). 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 run 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
[
[
[
0.0010151526657864451,
0.014391022734344006,
0.9845937490463257
]
]
]
```
The response 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 `0.9845937490463257`(~98.5%).