--- /dev/null
+# ==================================================================================
+#
+# Copyright (c) 2023 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.
+#
+# ==================================================================================
+
+#!/usr/bin/env python
+# coding: utf-8
+
+
+
+import kfp
+import kfp.components as components
+import kfp.dsl as dsl
+from kfp.components import InputPath, OutputPath
+
+
+def train_export_model(trainingjobName: str, epochs: str, version: 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
+ 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()
+
+ features = fs_sdk.get_features(trainingjobName, ['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("./")
+ import json
+ data = {}
+ data['metrics'] = []
+ data['metrics'].append({'Accuracy': str(np.mean(np.absolute(np.asarray(xx)-np.asarray(yy))<5))})
+
+ mm_sdk.upload_metrics(data, trainingjobName, version)
+ mm_sdk.upload_model("./", trainingjobName, version)
+
+
+
+
+BASE_IMAGE = "traininghost/pipelineimage:latest"
+
+
+
+
+def train_and_export(trainingjobName: str, epochs: str, version: str):
+ trainOp = components.func_to_container_op(train_export_model, base_image=BASE_IMAGE)(trainingjobName, epochs,version)
+ # Below line to disable caching of pipeline step
+ trainOp.execution_options.caching_strategy.max_cache_staleness = "P0D"
+ trainOp.container.set_image_pull_policy("IfNotPresent")
+
+
+
+
+@dsl.pipeline(
+ name="qoe Pipeline",
+ description="qoe",
+)
+def super_model_pipeline(
+ trainingjob_name: str, epochs: str, version: str):
+
+ train_and_export(trainingjob_name, epochs, version)
+
+
+if __name__ == '__main__':
+ # Compiling the pipeline
+ pipeline_func = super_model_pipeline
+ file_name = "qoe_model_pipeline"
+ kfp.compiler.Compiler().compile(pipeline_func, file_name + '.yaml')
+
Code Review / aiml-fw / aimlfw-dep.git / commitdiff