This project involves training a simple machine learning (ML) model in Python, converting it to ONNX format, and serving it over HTTPS using a Golang server.

• golang/: Contains Go server for serving the trained model.
• images/: Test image
• python/: Contains Python script for training the ML model.

Trained sklearn ML model is converted to ONNX format using skl2onnx converter and stored in the models/ directory.

1. Create and activate a virtual environment (recommended):

conda create --name [name] python=3.10
conda activate [name]

2. Navigate to python/ directory and install the requirements

pip install -r requirements.txt

3. Train and store the model

python3 python/train.py

ONNX model is loaded using onnxruntime_go wrapper and served via HTTPS.
For reproducing make sure you have go installed and run the following commands:

1. Navigate to the Go directory

cd golang

2. Build the server binary:

go build -o go-serving main.go

3. Run the server:

./go-serving

Alternatively, this can be ran using Docker.

1. Build the Docker image

docker build -t go-serving:latest .

2. Run Docker container

docker run -p 8080:8080 go-serving:latest

Open a new terminal and paste the following request:

curl -X POST https://localhost:8080/inference -d '{"input_data": [5.8, 2.8, 5.1, 2.4]}' -H "Content-Type: application/json"

Example input/output: