Refactor Distributed Analytics project structure

Modified the project structure to improve maintainability and to add future CI and
integration test support.

Change-Id: Id30bfb1f83f23785a6b5f99e81f42f752d59c0f8
Issue-ID: ONAPARC-280
Signed-off-by: Dileep Ranganathan <dileep.ranganathan@intel.com>

1 files changed, 0 insertions, 169 deletions

diff --git a/vnfs/DAaaS/applications/sample-horovod-app/keras_mnist_advanced_modified.py b/vnfs/DAaaS/applications/sample-horovod-app/keras_mnist_advanced_modified.py
deleted file mode 100644
index fa39cb6a..00000000
--- a/vnfs/DAaaS/applications/sample-horovod-app/keras_mnist_advanced_modified.py
+++ /dev/null
@@ -1,169 +0,0 @@
-from __future__ import print_function
-import keras
-import os
-from tensorflow.keras.datasets import mnist
-from tensorflow.keras.models import Sequential
-from tensorflow.keras.layers import Dense, Dropout, Flatten
-from tensorflow.keras.layers import Conv2D, MaxPooling2D
-from tensorflow.keras.preprocessing.image import ImageDataGenerator
-from tensorflow.keras import backend as K
-from tensorflow_estimator.python.estimator.export import export as export_helpers
-from tensorflow.python.saved_model import builder as saved_model_builder
-from tensorflow.python.saved_model import tag_constants, signature_constants
-from tensorflow.python.saved_model.signature_def_utils_impl import predict_signature_def
-import tensorflow as tf
-import horovod.tensorflow.keras as hvd
-
-
-# Horovod: initialize Horovod.
-hvd.init()
-
-# Horovod: pin GPU to be used to process local rank (one GPU per process)
-config = tf.ConfigProto()
-#config.gpu_options.allow_growth = True
-#config.gpu_options.visible_device_list = str(hvd.local_rank())
-K.set_session(tf.Session(config=config))
-
-batch_size = 128
-num_classes = 10
-
-# Enough epochs to demonstrate learning rate warmup and the reduction of
-# learning rate when training plateaues.
-epochs = 24
-
-# Input image dimensions
-img_rows, img_cols = 28, 28
-
-# The data, shuffled and split between train and test sets
-(x_train, y_train), (x_test, y_test) = mnist.load_data()
-
-# Determine how many batches are there in train and test sets
-train_batches = len(x_train) // batch_size
-test_batches = len(x_test) // batch_size
-
-if K.image_data_format() == 'channels_first':
-    x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)
-    x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)
-    input_shape = (1, img_rows, img_cols)
-else:
-    x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
-    x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
-    input_shape = (img_rows, img_cols, 1)
-
-x_train = x_train.astype('float32')
-x_test = x_test.astype('float32')
-x_train /= 255
-x_test /= 255
-print('x_train shape:', x_train.shape)
-print(x_train.shape[0], 'train samples')
-print(x_test.shape[0], 'test samples')
-
-# Convert class vectors to binary class matrices
-y_train = tf.keras.utils.to_categorical(y_train, num_classes)
-y_test = tf.keras.utils.to_categorical(y_test, num_classes)
-
-model = Sequential()
-model.add(Conv2D(32, kernel_size=(3, 3),
-                 activation='relu',
-                 input_shape=input_shape))
-model.add(Conv2D(64, (3, 3), activation='relu'))
-model.add(MaxPooling2D(pool_size=(2, 2)))
-model.add(Dropout(0.25))
-model.add(Flatten())
-model.add(Dense(128, activation='relu'))
-model.add(Dropout(0.5))
-model.add(Dense(num_classes, activation='softmax'))
-
-# Horovod: adjust learning rate based on number of GPUs.
-opt = tf.keras.optimizers.Adadelta(lr=1.0 * hvd.size())
-
-# Horovod: add Horovod Distributed Optimizer.
-opt = hvd.DistributedOptimizer(opt)
-
-model.compile(loss=tf.keras.losses.categorical_crossentropy,
-              optimizer=opt,
-              metrics=['accuracy'])
-
-callbacks = [
-    # Horovod: broadcast initial variable states from rank 0 to all other processes.
-    # This is necessary to ensure consistent initialization of all workers when
-    # training is started with random weights or restored from a checkpoint.
-    hvd.callbacks.BroadcastGlobalVariablesCallback(0),
-
-    # Horovod: average metrics among workers at the end of every epoch.
-    #
-    # Note: This callback must be in the list before the ReduceLROnPlateau,
-    # TensorBoard or other metrics-based callbacks.
-    hvd.callbacks.MetricAverageCallback(),
-
-    # Horovod: using `lr = 1.0 * hvd.size()` from the very beginning leads to worse final
-    # accuracy. Scale the learning rate `lr = 1.0` ---> `lr = 1.0 * hvd.size()` during
-    # the first five epochs. See https://arxiv.org/abs/1706.02677 for details.
-    hvd.callbacks.LearningRateWarmupCallback(warmup_epochs=5, verbose=1),
-
-    # Reduce the learning rate if training plateaues.
-    tf.keras.callbacks.ReduceLROnPlateau(patience=10, verbose=1),
-]
-
-# Horovod: save checkpoints only on worker 0 to prevent other workers from corrupting them.
-if hvd.rank() == 0:
-    callbacks.append(tf.keras.callbacks.ModelCheckpoint(
-        './checkpoint-{epoch}.h5'))
-
-# Set up ImageDataGenerators to do data augmentation for the training images.
-train_gen = ImageDataGenerator(rotation_range=8, width_shift_range=0.08, shear_range=0.3,
-                               height_shift_range=0.08, zoom_range=0.08)
-test_gen = ImageDataGenerator()
-
-# Train the model.
-# Horovod: the training will randomly sample 1 / N batches of training data and
-# 3 / N batches of validation data on every worker, where N is the number of workers.
-# Over-sampling of validation data helps to increase probability that every validation
-# example will be evaluated.
-model.fit_generator(train_gen.flow(x_train, y_train, batch_size=batch_size),
-                    steps_per_epoch=train_batches // hvd.size(),
-                    callbacks=callbacks,
-                    epochs=epochs,
-                    verbose=1,
-                    validation_data=test_gen.flow(
-                        x_test, y_test, batch_size=batch_size),
-                    validation_steps=3 * test_batches // hvd.size())
-
-# Evaluate the model on the full data set.
-score = model.evaluate(x_test, y_test, verbose=0)
-print('Test loss:', score[0])
-print('Test accuracy:', score[1])
-
-# Save Model to Minio
-if hvd.rank() == 0:
-    print('Model Summary')
-    model.summary()
-    print('Exporting trained model to Minio Model Repo')
-    base_path = os.environ['MODEL_BASE_PATH']
-
-    # Option 1(Preferred) - Using Keras api and Tensorflow v1.13 version
-    saved_model_path = tf.contrib.saved_model.save_keras_model(model, base_path)
-    print('Model Saved to {} Using new Keras API!!!'.format(saved_model_path))
-    # Option 2 - Tensorflow v1.13+ Builder saved_model api.
-    # builder = saved_model_builder.SavedModelBuilder(base_path)
-
-    # print(model.input)
-    # print(model.outputs)
-
-    # signature = predict_signature_def(inputs={"inputs": model.input},
-    #                                   outputs={t.name:t for t in model.outputs})
-    # print(signature)
-    # K.set_learning_phase(0)
-    # with K.get_session() as sess:
-    #     builder.add_meta_graph_and_variables(sess=sess,
-    #                                          tags=[tag_constants.SERVING],
-    #                                          signature_def_map={'predict': signature})
-    #     builder.save()
-    # print('Model Saved to S3 Using Builder!!!')
-
-    # Option 3 - Tensorflow v1.13 Will be deprecated in Tensorflow v2
-    # tf.saved_model.simple_save(
-    #     keras.backend.get_session(),
-    #     export_path,
-    #     inputs={'input_image': model.input},
-    #     outputs={t.name: t for t in model.outputs})