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scikit-learn/xgboost_classifier/config.pbtxt

@@ -0,0 +1,42 @@
+backend: "fil"
+input [                                 
+ {  
+    name: "input__0"
+    data_type: TYPE_FP32
+    dims: [-1, 4]                    
+  } 
+]
+output [
+ {
+    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
+}

scikit-learn/zip-models.sh

@@ -0,0 +1,18 @@
+# Loop through each subdirectory in the directory
+for folder in */; do
+  # Remove the trailing slash from the folder name
+  folder_name=${folder%/}
+
+  # Skip if it's not a directory
+  if [ ! -d "$folder_name" ]; then
+    continue
+  fi
+
+  # Remove zip if it already exists
+  rm -f "$folder_name.zip"
+
+  # Zip the folder
+  zip -r "$folder_name.zip" "$folder_name" -x "*.DS_Store"
+done
+
+echo "Zipping completed."