First version

bayesian_ridge.zip

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bayesian_ridge/config.pbtxt

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-name: "bayesian_ridge"
-backend: "onnxruntime"
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catboost_regressor.zip

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catboost_regressor/1/model.onnx

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catboost_regressor/config.pbtxt

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gaussian_nb.zip

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gaussian_nb/config.pbtxt

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-platform: "onnxruntime_onnx"
-max_batch_size: 0
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gradient_boosting_regressor.zip

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gradient_boosting_regressor/1/model.onnx

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gradient_boosting_regressor/config.pbtxt

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isolation_forest.zip

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isolation_forest/1/model.onnx

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isolation_forest/config.pbtxt

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-name: "isolation_forest"
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kmeans.zip

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kmeans/1/model.onnx

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kmeans/config.pbtxt

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linear_regression.zip

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linear_regression/1/model.onnx

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linear_regression/config.pbtxt

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-name: "linear_regression"
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logistic_regression.zip

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logistic_regression/1/model.onnx

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logistic_regression/config.pbtxt

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-name: "logistic_regression"
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one_class_svm.zip

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one_class_svm/1/model.onnx

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one_class_svm/config.pbtxt

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random_forest_regressor.zip

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random_forest_regressor/1/model.onnx

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random_forest_regressor/config.pbtxt

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adaboost_regressor/1/model.onnx → scikit-learn/adaboost_regressor/adaboost_regressor.joblib

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+oid sha256:cffc26892d443f79f917cb7169f615b72bb1f6c7ef6e65af7715c703e63ff1ec
+size 32976

bayesian_ridge/1/model.onnx → scikit-learn/adaboost_regressor/adaboost_regressor.onnx

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+oid sha256:692f2635f9137798cea947ed31c22c28b3cafc486ec505869778a182d24fdafc
+size 25207

adaboost_regressor.zip → scikit-learn/adaboost_regressor/adaboost_regressor.zip

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+oid sha256:4777680bec1a45eec0035092e2ad1477b8e4aeed9f112bd8d01ff3ac54556ecb
+size 3890

{gaussian_nb → scikit-learn/adaboost_regressor/adaboost_regressor}/1/model.onnx

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+oid sha256:692f2635f9137798cea947ed31c22c28b3cafc486ec505869778a182d24fdafc
+size 25207

{adaboost_regressor → scikit-learn/adaboost_regressor/adaboost_regressor}/config.pbtxt

@@ -5,15 +5,10 @@ input [
   {
     name: "float_input"
     data_type: TYPE_FP32
-    dims: [ -1, 8 ]
+    dims: [ 8 ]
   }
 ]
 output [
-  {
-    name: "variable"
-    data_type: TYPE_FP32
-    dims: [ -1, 1 ]
-  }
 ]
 instance_group [
   {

scikit-learn/adaboost_regressor/convert2onnx.sh

@@ -0,0 +1 @@
+python ../convert2onnx.py california adaboost_regressor.joblib adaboost_regressor.onnx

scikit-learn/adaboost_regressor/model_packaging.sh

@@ -0,0 +1 @@
+python ../model_packaging.py .

scikit-learn/adaboost_regressor/predict.py

@@ -0,0 +1,29 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+import joblib
+
+from sklearn.datasets import fetch_california_housing
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import mean_squared_error
+
+# Load the model from disk
+loaded_model = joblib.load('adaboost_regressor.joblib')
+
+# Set the random seed
+random_seed = 0
+
+# Load the dataset
+dataset = fetch_california_housing()
+X, y = dataset.data, dataset.target
+
+# Split the dataset into training and testing sets
+_, X_test, _, y_test = train_test_split(X, y, test_size=0.25, random_state=random_seed)
+
+# Use the model to make predictions on the test data
+y_pred = loaded_model.predict(X_test)
+
+# Score the model using mean squared error
+mse = mean_squared_error(y_test, y_pred)
+
+print(f'Mean Squared Error: {mse}')

scikit-learn/adaboost_regressor/train.py

@@ -0,0 +1,25 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+import joblib
+
+from sklearn.datasets import fetch_california_housing
+from sklearn.ensemble import AdaBoostRegressor
+from sklearn.model_selection import train_test_split
+
+# Set the random seed
+random_seed = 0
+
+# Load the dataset
+dataset = fetch_california_housing()
+X, y = dataset.data, dataset.target
+
+# Split the dataset into training and testing sets
+X_train, _, y_train, _ = train_test_split(X, y, test_size=0.25, random_state=random_seed)
+
+# Create and train the model
+model = AdaBoostRegressor(n_estimators=100, random_state=random_seed)
+model.fit(X_train, y_train)
+
+# Save the trained model to disk
+joblib.dump(model, 'adaboost_regressor.joblib')

scikit-learn/convert2onnx.py

@@ -0,0 +1,64 @@
+import argparse
+import joblib
+
+from sklearn.datasets import fetch_california_housing, load_diabetes, load_iris
+from skl2onnx import convert_sklearn
+from skl2onnx.common.data_types import FloatTensorType
+
+
+def load_dataset(dataset_name):
+    if dataset_name == 'california':
+        dataset = fetch_california_housing()
+    elif dataset_name == 'diabetes':
+        dataset = load_diabetes()
+    elif dataset_name == 'iris':
+        dataset = load_iris()
+    else:
+        raise ValueError("Invalid dataset name")
+    return dataset.data, dataset.target
+
+
+def prepare_onnx_conversion_params(X, target_opset, model):
+    if target_opset in {9, 17}:
+        tensor = FloatTensorType([None, 4])
+    else:
+        tensor = FloatTensorType([None, X.shape[1]])
+    if target_opset == 9:
+        options = {id(model): {'zipmap': False}}
+    else:
+        options = None
+    return tensor, options
+
+
+def convert2onnx(model, initial_type, options, target_opset, onnx_filename):
+    # try:
+    #     model.save_model(onnx_filename, format="onnx")
+    #     print("Model saved in ONNX format successfully.")
+    # except Exception as e:
+    #     print("Error occurred while saving model in ONNX format:", e)
+    try:
+        onnx_model = convert_sklearn(model, initial_types=initial_type, options=options, target_opset=target_opset)
+        with open(onnx_filename, "wb") as f:
+            f.write(onnx_model.SerializeToString())
+        print("Model converted to ONNX format and saved successfully.")
+    except Exception as e:
+        print("Error occurred while converting model to ONNX format:", e)
+
+"""
+python convert2onnx.py california adaboost_regressor.joblib adaboost_regressor.onnx
+"""
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description='Converts a sklearn model to ONNX format.')
+    parser.add_argument('dataset_name', type=str, help='Name of the dataset. Choose from: "california", "diabetes", or "iris".')
+    parser.add_argument('model_path', type=str, help='Path to the trained model file.')
+    parser.add_argument('onnx_filename', type=str, help='The filename for the output ONNX file.')
+    args = parser.parse_args()
+
+    X, y = load_dataset(args.dataset_name)
+    model = joblib.load(args.model_path)
+    target_opset = 12
+    tensor, options = prepare_onnx_conversion_params(X, target_opset, model)
+
+    input_name = 'float_input'
+    initial_type = [(input_name, tensor)]
+    convert2onnx(model, initial_type, options, target_opset, args.onnx_filename)

scikit-learn/model_packaging.py

@@ -0,0 +1,114 @@
+import os
+import onnx
+import shutil
+import zipfile
+import argparse
+
+
+def create_triton_config(model_path, config_path, model_name, max_batch_size=0):
+    # Load the ONNX model
+    model = onnx.load(model_path)
+    # Extract input and output information
+    input_tensors = []
+    for i in model.graph.input:
+        shape = [dim.dim_value if dim.dim_value >= 1 else -1 for dim in i.type.tensor_type.shape.dim][1:]
+        input_tensors.append({"name": i.name, "data_type": "TYPE_FP32", "dims": shape})
+    output_tensors = []
+    for o in model.graph.output:
+        shape = [dim.dim_value if dim.dim_value >= 1 else -1 for dim in o.type.tensor_type.shape.dim]
+    # Create the Triton configuration
+    config = {
+        "name": model_name,
+        "backend": "onnxruntime",
+        "max_batch_size": max_batch_size,
+        "input": input_tensors,
+        "output": output_tensors,
+        "instance_group": [{"count": 1, "kind": "KIND_CPU"}],
+    }
+    # Save the configuration as a JSON file
+    with open(config_path, 'w') as f:
+        f.write("name: \"" + config['name'] + "\"\n")
+        f.write("backend: \"" + config['backend'] + "\"\n")
+        f.write("max_batch_size: " + str(config['max_batch_size']) + "\n")
+        f.write("input [\n")
+        for input_tensor in config['input']:
+            f.write("  {\n")
+            f.write("    name: \"" + input_tensor['name'] + "\"\n")
+            f.write("    data_type: " + input_tensor['data_type'] + "\n")
+            f.write("    dims: [ " + ", ".join([str(dim) for dim in input_tensor['dims']]) + " ]\n")
+            f.write("  }\n")
+        f.write("]\n")
+        f.write("output [\n")
+        for output_tensor in config['output']:
+            f.write("  {\n")
+            f.write("    name: \"" + output_tensor['name'] + "\"\n")
+            f.write("    data_type: " + output_tensor['data_type'] + "\n")
+            f.write("    dims: [ " + ", ".join([str(dim) for dim in output_tensor['dims']]) + " ]\n")
+            f.write("  }\n")
+        f.write("]\n")
+        f.write("instance_group [\n")
+        for instance_group in config['instance_group']:
+            f.write("  {\n")
+            f.write("    count: " + str(instance_group['count']) + "\n")
+            f.write("    kind: " + instance_group['kind'] + "\n")
+            f.write("  }\n")
+        f.write("]\n")
+    print(f"The configuration file has been saved to '{config_path}'")
+
+
+def list_onnx_files(directory):
+    onnx_files = []
+    for root, _, files in os.walk(directory):
+        for file in files:
+            if file.endswith(".onnx"):
+                onnx_files.append(file)
+    return onnx_files
+
+
+def zip_folder(folder_path, output_path):
+    # Create a ZipFile object
+    with zipfile.ZipFile(output_path, 'w', zipfile.ZIP_DEFLATED) as zip_file:
+        # Walk through the directory tree and add each file to the zip file
+        for root, dirs, files in os.walk(folder_path):
+            for file in files:
+                file_path = os.path.join(root, file)
+                zip_file.write(file_path, arcname=os.path.relpath(file_path, folder_path))
+            # Add an empty directory entry for each subdirectory
+            for dir in dirs:
+                dir_path = os.path.join(root, dir)
+                zip_file.write(dir_path, arcname=os.path.relpath(dir_path, folder_path))
+    print(f"The folder '{folder_path}' has been zipped to '{output_path}'")
+
+
+"""
+python model_packaging.py adaboost_regressor
+"""
+if __name__ == "__main__":
+    # Parse command-line arguments
+    parser = argparse.ArgumentParser(description='Process a directory with ONNX models.')
+    parser.add_argument('folder_path', type=str, help='Path to the directory with ONNX models.')
+    args = parser.parse_args()
+    folder_path = args.folder_path
+    # Change to the parent directory
+    os.chdir(folder_path)
+    filenames = list_onnx_files(folder_path)
+    version = '1'
+    print(filenames)
+    for filename in filenames:
+        if not filename.startswith("."): # Exclude hidden files
+            foldername = os.path.splitext(filename)[0] # Get the filename without extension
+            if not os.path.exists(foldername): # Check if folder doesn't exist
+                os.makedirs(foldername, exist_ok=True)
+                folderdir = os.path.join(foldername, version)
+                os.makedirs(folderdir, exist_ok=True)
+                shutil.copy(filename, folderdir) # Copy the file to the folder
+                model_path = os.path.join(folderdir, filename)
+                config_path = os.path.join(foldername, "config.pbtxt")
+                create_triton_config(model_path, config_path, foldername, max_batch_size=0)
+                os.rename(os.path.join(folderdir, filename), os.path.join(folderdir, 'model.onnx')) # Rename the file
+                print(f"The file '{os.path.join(folderdir, filename)}' has been renamed to '{os.path.join(folderdir, 'model.onnx')}'")
+                print(f"{foldername} folder created successfully!")
+                print(f"{filename} copied to {foldername} successfully!")
+                zip_folder(os.path.join(folder_path, foldername), f"{foldername}.zip")
+            else:
+                print(f"{foldername} folder already exists.")

svm.zip

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xgboost_classifier/config.pbtxt

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-    name: "output__0"
-    data_type: TYPE_FP32
-    dims: [ 3 ]
-  }
-]
-instance_group [{ kind: KIND_CPU }]
-parameters [
-  {
-    key: "model_type"
-    value: { string_value: "xgboost_json" }
-  },
-  {
-    key: "predict_proba"
-    value: { string_value: "true" }
-  },
-  {
-    key: "output_class"
-    value: { string_value: "true" }
-  },
-  {
-    key: "threshold"
-    value: { string_value: "0.5" }
-  },
-  {
-    key: "storage_type"
-    value: { string_value: "AUTO" }
-  }
-]
-
-dynamic_batching {
-  max_queue_delay_microseconds: 100
-}