[REVIEW] updating the filenames in the paths and adding the missing file for abp

models/training-tuning-scripts/abp-models/abp-nvsmi-xgb-20210310.ipynb

@@ -46,7 +46,7 @@
    "source": [
     "import xgboost as xgb\n",
     "import cudf\n",
-    "from cuml.preprocessing.model_selection import train_test_split\n",
+    "from sklearn.model_selection import train_test_split\n",
     "from cuml import ForestInference\n",
     "import sklearn.datasets\n",
     "import cupy\n",
@@ -63,7 +63,13 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    },
+    "tags": []
+   },
    "outputs": [
     {
      "name": "stderr",
@@ -75,7 +81,7 @@
     }
    ],
    "source": [
-    "df = cudf.read_json(\"./labelled_nv_smi.json\")"
+    "df = cudf.read_json(\"../../datasets/training-data/abp-sample-nvsmi-training-data.json\")"
    ]
   },
   {
@@ -354,7 +360,7 @@
     "## Conclusion\n",
     "The model predicted all the workloads in the test set correctly.\n",
     "Since our dataset in this experiment is balanced, we use the accuracy metric.\n",
-    "We are not able to publish the internal data used in this notebook, however, users can use this notebook with `nvidia-smi` outputs from their machines with multiple combinations of different workloads."
+    "We publish a small sample dataset with this notebook, however, users can use this notebook with `nvidia-smi` outputs from their machines with multiple combinations of different workloads."
    ]
   }
  ],
@@ -374,7 +380,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.1"
+   "version": "3.8.10"
   }
  },
  "nbformat": 4,

models/training-tuning-scripts/abp-models/abp-nvsmi-xgb-20210310.py

@@ -1,10 +1,11 @@
-# Copyright (c) 2020, NVIDIA CORPORATION.
+# SPDX-FileCopyrightText: Copyright (c) 2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at
 #
-#     http://www.apache.org/licenses/LICENSE-2.0
+# http://www.apache.org/licenses/LICENSE-2.0
 #
 # Unless required by applicable law or agreed to in writing, software
 # distributed under the License is distributed on an "AS IS" BASIS,
@@ -13,58 +14,73 @@
 # limitations under the License.
 
 """
-Example Usage: 
-python run_abp_training.py \
-       --training-data /rapidsai-data/cyber/abp.json \
-
+Example Usage:
+python abp-nvsmi-xgb-20210310.py \
+       --training-data \
+       ../../datasets/training-data/abp-sample-nvsmi-training-data.json \
 """
+
 import argparse
 import xgboost as xgb
 import cudf
 from cuml.preprocessing.model_selection import train_test_split
-from cuml import ForestInference
-import sklearn.datasets
-import cupy
 
 
 def preprocess(trainingdata):
+
     # read json data
+
     df = cudf.read_json(trainingdata)
+
     # print list of columns
-    print("List of columns")
 
     print(list(df))
 
     # print labels
-    print("Labels:")
-    print(df["label"].unique())
+
+    print(df['label'].unique())
 
     return df
 
+
 def train_val_split(df):
-    X_train, X_test, y_train, y_test= train_test_split(df.drop(["label","nvidia_smi_log.timestamp"],axis=1), df['label'],  train_size=0.8, random_state=1)
-    return X_train, X_test, y_train, y_test
 
+    (X_train, X_test, y_train, y_test) = \
+        train_test_split(df.drop(['label', 'nvidia_smi_log.timestamp'],
+                         axis=1), df['label'], train_size=0.8,
+                         random_state=1)
+
+    return (X_train, X_test, y_train, y_test)
 
-
 
 def train(X_train, X_test, y_train, y_test):
+
     # move to Dmatrix
+
     dmatrix_train = xgb.DMatrix(X_train, label=y_train)
     dmatrix_validation = xgb.DMatrix(X_test, label=y_test)
+
     # Set parameters
-    params = {'tree_method': 'gpu_hist', 'eval_metric': 'auc', 'objective': 'binary:logistic', 'max_depth': 5,
-              'learning_rate': 0.1}
-    evallist = [(dmatrix_validation, 'validation'), (dmatrix_train, 'train')]
+
+    params = {
+        'tree_method': 'gpu_hist',
+        'eval_metric': 'auc',
+        'objective': 'binary:logistic',
+        'max_depth': 5,
+        'learning_rate': 0.1,
+        }
+    evallist = [(dmatrix_validation, 'validation'), (dmatrix_train,
+                'train')]
     num_round = 5
+
     # Train the model
+
     bst = xgb.train(params, dmatrix_train, num_round, evallist)
     return bst
 
 
 def save_model(model):
-    model.save_model("./abp-nvsmi-xgb-20210310.bst")
-
+    model.save_model('./abp-nvsmi-xgb-20210310.bst')
 
 
 # def eval(model,X_test, y_test):
@@ -78,21 +94,23 @@ def save_model(model):
 #     acc = accuracy_score(y_true, y_pred)
 #     print("Validation_score: ", acc)
 
-
 def main():
-    print("Preprocessing...")
-    X_train, X_test, y_train, y_test= train_val_split(preprocess(args.trainingdata))
-    print("Model Training...")
+    print('Preprocessing...')
+    (X_train, X_test, y_train, y_test) = \
+        train_val_split(preprocess(args.trainingdata))
+    print('Model Training...')
     model = train(X_train, X_test, y_train, y_test)
-    print("Saving Model")
+    print('Saving Model')
     save_model(model)
+
+
 #     print("Model Evaluation...")
 #     eval(model,X_test, y_test)
-    
-if __name__ == "__main__":
-  parser = argparse.ArgumentParser(description=__doc__)
-  parser.add_argument("--trainingdata", required=True,
-                      help="Labelled data in JSON format")
-  args = parser.parse_args()
-
-  main()
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument('--trainingdata', required=True,
+                        help='Labelled data in JSON format')
+    args = parser.parse_args()
+
+    main()

models/training-tuning-scripts/dfp-models/hammah-20211017.ipynb

@@ -71,7 +71,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "TRAINING_DATA_PATH = \"../../datasets/training-data/dfp-user123-training-data.csv\"\n",
+    "TRAINING_DATA_PATH = \"../../datasets/training-data/dfp-cloudtrail-user123-training-data.csv\"\n",
     "VAL_DATA_PATH = \"../../datasets/validation-data/dfp-cloudtrail-user123-validation-data-input.csv\"\n",
     "OUTPUT_MODEL_NAME = \"hammah-user123-20211017.pkl\""
    ]
@@ -1261,7 +1261,7 @@
    "version": "0.3.2"
   },
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },