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  1. scikit-learn/adaboost_regressor.zip +3 -0
  2. scikit-learn/adaboost_regressor/1/model.onnx +3 -0
  3. scikit-learn/adaboost_regressor/config.pbtxt +23 -0
  4. scikit-learn/bayesian_ridge.zip +3 -0
  5. scikit-learn/bayesian_ridge/1/model.onnx +3 -0
  6. scikit-learn/bayesian_ridge/config.pbtxt +22 -0
  7. scikit-learn/catboost_regressor.zip +3 -0
  8. scikit-learn/catboost_regressor/1/model.onnx +3 -0
  9. scikit-learn/catboost_regressor/config.pbtxt +23 -0
  10. scikit-learn/gaussian_nb.zip +3 -0
  11. scikit-learn/gaussian_nb/1/model.onnx +3 -0
  12. scikit-learn/gaussian_nb/config.pbtxt +28 -0
  13. scikit-learn/gradient_boosting_regressor.zip +3 -0
  14. scikit-learn/gradient_boosting_regressor/1/model.onnx +3 -0
  15. scikit-learn/gradient_boosting_regressor/config.pbtxt +23 -0
  16. scikit-learn/isolation_forest.zip +3 -0
  17. scikit-learn/isolation_forest/1/model.onnx +3 -0
  18. scikit-learn/isolation_forest/config.pbtxt +23 -0
  19. scikit-learn/kmeans.zip +3 -0
  20. scikit-learn/kmeans/1/model.onnx +3 -0
  21. scikit-learn/kmeans/config.pbtxt +23 -0
  22. scikit-learn/linear_regression.zip +3 -0
  23. scikit-learn/linear_regression/1/model.onnx +3 -0
  24. scikit-learn/linear_regression/config.pbtxt +22 -0
  25. scikit-learn/logistic_regression.zip +3 -0
  26. scikit-learn/logistic_regression/1/model.onnx +3 -0
  27. scikit-learn/logistic_regression/config.pbtxt +28 -0
  28. scikit-learn/one_class_svm.zip +3 -0
  29. scikit-learn/one_class_svm/1/model.onnx +3 -0
  30. scikit-learn/one_class_svm/config.pbtxt +23 -0
  31. scikit-learn/random_forest_regressor.zip +3 -0
  32. scikit-learn/random_forest_regressor/1/model.onnx +3 -0
  33. scikit-learn/random_forest_regressor/config.pbtxt +23 -0
  34. scikit-learn/svm.zip +3 -0
  35. scikit-learn/svm/1/model.onnx +3 -0
  36. scikit-learn/svm/config.pbtxt +28 -0
  37. scikit-learn/svr.zip +3 -0
  38. scikit-learn/svr/1/model.onnx +3 -0
  39. scikit-learn/svr/config.pbtxt +22 -0
  40. scikit-learn/xgboost_classifier.zip +3 -0
  41. scikit-learn/xgboost_classifier/1/xgboost.json +1 -0
  42. scikit-learn/xgboost_classifier/config.pbtxt +42 -0
  43. scikit-learn/zip-models.sh +18 -0
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scikit-learn/xgboost_classifier/config.pbtxt ADDED
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+ name: "input__0"
5
+ data_type: TYPE_FP32
6
+ dims: [-1, 4]
7
+ }
8
+ ]
9
+ output [
10
+ {
11
+ name: "output__0"
12
+ data_type: TYPE_FP32
13
+ dims: [ 3 ]
14
+ }
15
+ ]
16
+ instance_group [{ kind: KIND_CPU }]
17
+ parameters [
18
+ {
19
+ key: "model_type"
20
+ value: { string_value: "xgboost_json" }
21
+ },
22
+ {
23
+ key: "predict_proba"
24
+ value: { string_value: "true" }
25
+ },
26
+ {
27
+ key: "output_class"
28
+ value: { string_value: "true" }
29
+ },
30
+ {
31
+ key: "threshold"
32
+ value: { string_value: "0.5" }
33
+ },
34
+ {
35
+ key: "storage_type"
36
+ value: { string_value: "AUTO" }
37
+ }
38
+ ]
39
+
40
+ dynamic_batching {
41
+ max_queue_delay_microseconds: 100
42
+ }
scikit-learn/zip-models.sh ADDED
@@ -0,0 +1,18 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Loop through each subdirectory in the directory
2
+ for folder in */; do
3
+ # Remove the trailing slash from the folder name
4
+ folder_name=${folder%/}
5
+
6
+ # Skip if it's not a directory
7
+ if [ ! -d "$folder_name" ]; then
8
+ continue
9
+ fi
10
+
11
+ # Remove zip if it already exists
12
+ rm -f "$folder_name.zip"
13
+
14
+ # Zip the folder
15
+ zip -r "$folder_name.zip" "$folder_name" -x "*.DS_Store"
16
+ done
17
+
18
+ echo "Zipping completed."