feat: add source code to our onnx example

src/.gitignore

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+*.pkl
+*.onnx

src/Pipfile

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+[[source]]
+url = "https://pypi.org/simple"
+verify_ssl = true
+name = "pypi"
+
+[packages]
+scikit-learn = "*"
+skl2onnx = "*"
+onnxruntime-gpu = "*"
+
+[dev-packages]
+pylint = "*"
+
+[requires]
+python_version = "3.11"

src/docker-compose.yml

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+---
+version: "3.11"
+
+services:
+  ort:
+    volumes:
+      - ".:/models"
+    ports:
+      - 9001:8001
+      - 50051:50051
+    image: mcr.microsoft.com/onnxruntime/server
+    command:
+      - --model_path
+      - /models/model.onnx

src/load.py

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+import pickle
+
+from skl2onnx import convert_sklearn
+from skl2onnx.common.data_types import FloatTensorType
+
+import onnx
+
+if __name__ == "__main__":
+    MODEL = None
+    with open("model.pkl", "rb") as f:
+        MODEL = pickle.load(f)
+
+    assert MODEL is not None
+
+    INITIAL_TYPE = [
+        ("float_input", FloatTensorType([None, 7])),
+    ]
+    ONX = convert_sklearn(MODEL, initial_types=INITIAL_TYPE)
+    onnx.save_model(
+        ONX,
+        "model.onnx",
+        save_as_external_data=True,
+        all_tensors_to_one_file=True,
+        location="filename",
+        size_threshold=1024,
+        convert_attribute=False,
+    )

src/main.py

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+"""
+To reduce the time, we create and load onnx model
+in the same script. The example is written based on:
+
+https://onnx.ai/sklearn-onnx/auto_tutorial/plot_abegin_convert_pipeline.html
+"""
+
+import pickle
+
+import numpy
+import onnxruntime as ort
+from skl2onnx import to_onnx
+
+import onnx.reference
+
+if __name__ == "__main__":
+    # load trained model from a pickle file.
+    MODEL = None
+    with open("model.pkl", "rb") as f:
+        MODEL = pickle.load(f)
+
+    assert MODEL is not None
+
+    # create a sample train data.
+    x_sample = numpy.array([[12.3, 24.4, 45.3, 65.7, 567, 4.5, 1]])
+
+    # calls convert_sklearn() with simplified parameters.
+    onx = to_onnx(MODEL, x_sample[:1].astype(numpy.float64))
+
+    session_options = ort.SessionOptions()
+    session_options.graph_optimization_level = (
+        ort.GraphOptimizationLevel.ORT_DISABLE_ALL
+    )
+
+    oinf = onnx.reference.ReferenceEvaluator(onx)
+    print(oinf)

src/model_dlw.sh

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+#!/usr/bin/env bash
+
+mcli cp smapp-teh1/nostra-baly/1/rf_1_25_85_0_0_0_0.pkl model.pkl

src/server.py

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+import onnxruntime as ort
+
+# Load the model and create InferenceSession
+model_path = "./model.onnx"
+session = ort.InferenceSession(model_path, providers=["CPUExecutionProvider"])
+
+outputs = session.run(output_names={}, input_feed={})
+print(outputs)

src/validate.py

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+import onnx
+
+onnx.checker.check_model("model.onnx")