Logistic Regression

iris_pipeline_project/Dockerfile

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+FROM openvino/model_server:latest
+
+USER root
+
+ENV LD_LIBRARY_PATH=/ovms/lib
+ENV PYTHONPATH=/ovms/lib/python
+
+RUN apt update && apt install -y python3-pip git \
+    build-essential python3-dev libatlas-base-dev
+
+RUN pip3 install --break-system-packages numpy pandas scikit-learn
+
+
+COPY pipeline/graph.pbtxt /models/iris_pipeline/
+COPY pipeline/ovmsmodel.py /models/iris_pipeline/
+COPY model/model.pkl /models/iris_pipeline/
+COPY model_config.json /model_config.json
+
+ENTRYPOINT [ "/ovms/bin/ovms" ]

iris_pipeline_project/convert.py

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+from sklearn.datasets import load_iris
+from sklearn.linear_model import LogisticRegression
+from sklearn.model_selection import train_test_split
+import joblib
+import os
+import openvino as ov
+
+X, y = load_iris(return_X_y=True)
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
+
+model = LogisticRegression(max_iter=200)
+model.fit(X_train, y_train)
+
+joblib.dump(model, 'logreg_model.pkl')
+
+from skl2onnx import to_onnx
+
+onx = to_onnx(model, X_train[:1], target_opset=12)
+ov.convert_model('logreg_model.onnx')
+
+import onnx
+model = onnx.load("model/iris_logreg/1/logreg_model.onnx")
+print("Inputs:", [i.name for i in model.graph.input])
+print("Outputs:", [o.name for o in model.graph.output])

iris_pipeline_project/model_config.json

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+{
+  "custom_node_library_config_list": [],
+  "graph_config_list": [
+    {
+      "name": "iris_pipeline",
+      "base_path": "/models/iris_pipeline",
+      "graph_path": "graph.pbtxt"
+    }
+  ]
+}

iris_pipeline_project/ovmsmodel.py

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+import pandas as pd
+import numpy as np
+import joblib
+import os
+from pyovms import Tensor
+
+class OvmsPythonModel:
+    def initialize(self, kwargs):
+        print("Initializing model")
+        model_path = os.path.join(os.path.dirname(__file__), 'model.pkl')
+        self.model = joblib.load(model_path)
+        print("Model loaded successfully.")
+
+    def execute(self, inputs):
+        print("Executing inference...")
+        input_tensor = inputs[0]
+        input_data = input_tensor.as_numpy()
+
+        csv_data = input_data.tobytes().decode('utf-8')
+        df = pd.read_csv(pd.compat.StringIO(csv_data))
+
+        features = df.iloc[:, :-1]  
+        preds = self.model.predict(features)
+
+        output = Tensor.from_numpy(np.array(preds, dtype=np.int32))
+        return [output]
+
+    def finalize(self):
+        print("Finalizing model")

iris_pipeline_project/pipeline/graph.pbtxt

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+input_stream: "inference_input"
+output_stream: "inference_output"
+
+node {
+  calculator: "InferenceCalculator"
+  input_stream: "inference_input"
+  output_stream: "inference_output"
+  options: {
+    [type.googleapis.com/openvino.CalculatorOptions] {
+      model_name: "iris_pipeline"
+      signature_name: "serving_default"
+    }
+  }
+}