Adding Latest qoe_pipeline under samples

Updated the qoe_pipeline as per new tensorflow version(2.20.0)

Issue_id: AIMLFW-244

Change-Id: I46ffe70c5a06340e98932bfe43292777d4b25e00
Signed-off-by: ashishj1729 <jain.ashish@samsung.com>

diff --git a/samples/qoe/qoe_pipeline.py b/samples/qoe/qoe_pipeline.py
new file mode 100644 (file)
index 0000000..ed1875e
--- /dev/null
+++ b/samples/qoe/qoe_pipeline.py
@@ -0,0 +1,142 @@
+# ==================================================================================
+#
+#       Copyright (c) 2025 Samsung Electronics Co., Ltd. All Rights Reserved.
+#
+#   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
+#
+#   Unless required by applicable law or agreed to in writing, software
+#   distributed under the License is distributed on an "AS IS" BASIS,
+#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#   See the License for the specific language governing permissions and
+#   limitations under the License.
+#
+# ==================================================================================
+
+
+import kfp
+import kfp.dsl as dsl
+from kfp.dsl import InputPath, OutputPath
+from kfp.dsl import component as component
+from kfp import kubernetes
+
+BASE_IMAGE = "traininghost/pipelineimage:latest"
+
+@component(base_image=BASE_IMAGE)
+def train_export_model(featurepath: str, epochs: str, modelname: str, modelversion:str):
+    
+    import tensorflow as tf
+    from numpy import array
+    from tensorflow.keras.models import Sequential
+    from tensorflow.keras.layers import Dense
+    from tensorflow.keras.layers import Flatten, Dropout, Activation
+    from tensorflow.keras.layers import LSTM
+    import numpy as np
+    import requests
+    print("numpy version")
+    print(np.__version__)
+    import pandas as pd
+    import os
+    from featurestoresdk.feature_store_sdk import FeatureStoreSdk
+    from modelmetricsdk.model_metrics_sdk import ModelMetricsSdk
+    
+    fs_sdk = FeatureStoreSdk()
+    mm_sdk = ModelMetricsSdk()
+    print("featurepath is: ", featurepath)
+    features = fs_sdk.get_features(featurepath, ['pdcpBytesDl','pdcpBytesUl'])
+    print("Dataframe:")
+    print(features)
+
+    features_cellc2b2 = features
+    print(features_cellc2b2)
+    print('Previous Data Types are --> ', features_cellc2b2.dtypes)
+    features_cellc2b2["pdcpBytesDl"] = pd.to_numeric(features_cellc2b2["pdcpBytesDl"], downcast="float")
+    features_cellc2b2["pdcpBytesUl"] = pd.to_numeric(features_cellc2b2["pdcpBytesUl"], downcast="float")
+    print('New Data Types are --> ', features_cellc2b2.dtypes)
+    
+    features_cellc2b2 = features_cellc2b2[['pdcpBytesDl', 'pdcpBytesUl']]
+    
+    def split_series(series, n_past, n_future):
+        X, y = list(), list()
+        for window_start in range(len(series)):
+            past_end = window_start + n_past
+            future_end = past_end + n_future
+            if future_end > len(series):
+                break
+            # slicing the past and future parts of the window
+            past, future = series[window_start:past_end, :], series[past_end:future_end, :]
+            X.append(past)
+            y.append(future)
+        return np.array(X), np.array(y)
+    X, y = split_series(features_cellc2b2.values,10, 1)
+    X = X.reshape((X.shape[0], X.shape[1],X.shape[2]))
+    y = y.reshape((y.shape[0], y.shape[2]))
+    print(X.shape)
+    print(y.shape)
+    
+    model = Sequential()
+    model.add(LSTM(units = 150, activation="tanh" ,return_sequences = True, input_shape = (X.shape[1], X.shape[2])))
+
+    model.add(LSTM(units = 150, return_sequences = True,activation="tanh"))
+
+    model.add(LSTM(units = 150,return_sequences = False,activation="tanh" ))
+
+    model.add((Dense(units = X.shape[2])))
+    
+    model.compile(loss='mse', optimizer='adam',metrics=['mse'])
+    model.summary()
+    
+    model.fit(X, y, batch_size=10,epochs=int(epochs), validation_split=0.2)
+    yhat = model.predict(X, verbose = 0)
+
+    
+    xx = y
+    yy = yhat
+    model_save_filepath = "./"
+    model.export(model_save_filepath)
+    
+    import json
+    data = {}
+    data['metrics'] = []
+    data['metrics'].append({'Accuracy': str(np.mean(np.absolute(np.asarray(xx)-np.asarray(yy))<5))})
+    
+    #as new artifact after training will always be 1.0.0
+    artifactversion="1.0.0"
+    url = f"http://modelmgmtservice.traininghost:8082/ai-ml-model-registration/v1/model-registrations/updateArtifact/{modelname}/{modelversion}/{artifactversion}"
+    updated_model_info= requests.post(url).json()
+    print(updated_model_info)
+    
+    #featurepath is a combination of <feature_group>_<trainingjob_Id>
+    trainingjob_id = featurepath.split('_')[-1]
+    mm_sdk.upload_metrics(data, trainingjob_id)
+    print("Model-metric : ", mm_sdk.get_metrics(trainingjob_id))
+    mm_sdk.upload_model(model_save_filepath, modelname, modelversion, artifactversion)
+    
+
+
+@dsl.pipeline(
+    name="qoe Pipeline",
+    description="qoe",
+)
+def super_model_pipeline( 
+    featurepath: str, epochs: str, modelname: str, modelversion:str):
+    
+    trainop=train_export_model(featurepath=featurepath, epochs=epochs, modelname=modelname, modelversion=modelversion)
+    trainop.set_caching_options(False)
+    kubernetes.set_image_pull_policy(trainop, "IfNotPresent")
+
+
+pipeline_func = super_model_pipeline
+file_name = "qoe_model_pipeline"
+
+kfp.compiler.Compiler().compile(pipeline_func,  
+  '{}.yaml'.format(file_name))
+
+
+import requests
+pipeline_name="qoe_Pipeline"
+pipeline_file = file_name+'.yaml'
+requests.post("http://tm.traininghost:32002/pipelines/{}/upload".format(pipeline_name), files={'file':open(pipeline_file,'rb')})
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