--- /dev/null
+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})
Code Review / demo.git / blobdiff