Azure DatabricksでMNISTやーる

投稿日 2022年4月27日
著者 azure-recipe-user
カテゴリー Azure
カテゴリー Databricks

はじめに

MNISTやっていきます

開発環境

mnist-tensorflow-keras

1．こちらのノートブックをやっていきます
https://docs.databricks.com/_static/notebooks/deep-learning/mnist-tensorflow-keras.html

2．ライブラリをインストール

%pip install tensorflow

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						%pip install tensorflow

3．関数を定義

def get_dataset(num_classes, rank=0, size=1):
  from tensorflow import keras
  
  (x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data('MNIST-data-%d' % rank)
  x_train = x_train[rank::size]
  y_train = y_train[rank::size]
  x_test = x_test[rank::size]
  y_test = y_test[rank::size]
  x_train = x_train.reshape(x_train.shape[0], 28, 28, 1)
  x_test = x_test.reshape(x_test.shape[0], 28, 28, 1)
  x_train = x_train.astype('float32')
  x_test = x_test.astype('float32')
  x_train /= 255
  x_test /= 255
  y_train = keras.utils.to_categorical(y_train, num_classes)
  y_test = keras.utils.to_categorical(y_test, num_classes)
  return (x_train, y_train), (x_test, y_test)

				
					
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						def get_dataset(num_classes, rank=0, size=1):
  from tensorflow import keras
  
  (x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data('MNIST-data-%d' % rank)
  x_train = x_train[rank::size]
  y_train = y_train[rank::size]
  x_test = x_test[rank::size]
  y_test = y_test[rank::size]
  x_train = x_train.reshape(x_train.shape[0], 28, 28, 1)
  x_test = x_test.reshape(x_test.shape[0], 28, 28, 1)
  x_train = x_train.astype('float32')
  x_test = x_test.astype('float32')
  x_train /= 255
  x_test /= 255
  y_train = keras.utils.to_categorical(y_train, num_classes)
  y_test = keras.utils.to_categorical(y_test, num_classes)
  return (x_train, y_train), (x_test, y_test)
 

					

			

def get_model(num_classes):
  from tensorflow.keras import models
  from tensorflow.keras import layers
  
  model = models.Sequential()
  model.add(layers.Conv2D(32, kernel_size=(3, 3),
                   activation='relu',
                   input_shape=(28, 28, 1)))
  model.add(layers.Conv2D(64, (3, 3), activation='relu'))
  model.add(layers.MaxPooling2D(pool_size=(2, 2)))
  model.add(layers.Dropout(0.25))
  model.add(layers.Flatten())
  model.add(layers.Dense(128, activation='relu'))
  model.add(layers.Dropout(0.5))
  model.add(layers.Dense(num_classes, activation='softmax'))
  return model

				
					
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						def get_model(num_classes):
  from tensorflow.keras import models
  from tensorflow.keras import layers
  
  model = models.Sequential()
  model.add(layers.Conv2D(32, kernel_size=(3, 3),
                   activation='relu',
                   input_shape=(28, 28, 1)))
  model.add(layers.Conv2D(64, (3, 3), activation='relu'))
  model.add(layers.MaxPooling2D(pool_size=(2, 2)))
  model.add(layers.Dropout(0.25))
  model.add(layers.Flatten())
  model.add(layers.Dense(128, activation='relu'))
  model.add(layers.Dropout(0.5))
  model.add(layers.Dense(num_classes, activation='softmax'))
  return model
 

					

			

# Specify training parameters
batch_size = 128
epochs = 2
num_classes = 10
 
def train(learning_rate=1.0):
  from tensorflow import keras
  
  (x_train, y_train), (x_test, y_test) = get_dataset(num_classes)
  model = get_model(num_classes)
 
  # Specify the optimizer (Adadelta in this example), using the learning rate input parameter of the function so that Horovod can adjust the learning rate during training
  optimizer = keras.optimizers.Adadelta(lr=learning_rate)
 
  model.compile(optimizer=optimizer,
                loss='categorical_crossentropy',
                metrics=['accuracy'])
 
  model.fit(x_train, y_train,
            batch_size=batch_size,
            epochs=epochs,
            verbose=2,
            validation_data=(x_test, y_test))
  return model

				
					
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						# Specify training parameters
batch_size = 128
epochs = 2
num_classes = 10
 
def train(learning_rate=1.0):
  from tensorflow import keras
  
  (x_train, y_train), (x_test, y_test) = get_dataset(num_classes)
  model = get_model(num_classes)
 
  # Specify the optimizer (Adadelta in this example), using the learning rate input parameter of the function so that Horovod can adjust the learning rate during training
  optimizer = keras.optimizers.Adadelta(lr=learning_rate)
 
  model.compile(optimizer=optimizer,
                loss='categorical_crossentropy',
                metrics=['accuracy'])
 
  model.fit(x_train, y_train,
            batch_size=batch_size,
            epochs=epochs,
            verbose=2,
            validation_data=(x_test, y_test))
  return model
 

					

			

4．学習

model = train(learning_rate=0.1)

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						model = train(learning_rate=0.1)

Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz
11493376/11490434 [==============================] - 0s 0us/step
11501568/11490434 [==============================] - 0s 0us/step
/local_disk0/.ephemeral_nfs/envs/pythonEnv-107bf0ee-693b-4dea-8790-78f3dc70991f/lib/python3.8/site-packages/keras/optimizer_v2/adadelta.py:74: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.
  super(Adadelta, self).__init__(name, **kwargs)
Epoch 1/2
469/469 - 64s - loss: 0.6030 - accuracy: 0.8152 - val_loss: 0.2076 - val_accuracy: 0.9394 - 64s/epoch - 137ms/step
Epoch 2/2
469/469 - 64s - loss: 0.2775 - accuracy: 0.9175 - val_loss: 0.1417 - val_accuracy: 0.9584 - 64s/epoch - 136ms/step

				
					
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						Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz
11493376/11490434 [==============================] - 0s 0us/step
11501568/11490434 [==============================] - 0s 0us/step
/local_disk0/.ephemeral_nfs/envs/pythonEnv-107bf0ee-693b-4dea-8790-78f3dc70991f/lib/python3.8/site-packages/keras/optimizer_v2/adadelta.py:74: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.
  super(Adadelta, self).__init__(name, **kwargs)
Epoch 1/2
469/469 - 64s - loss: 0.6030 - accuracy: 0.8152 - val_loss: 0.2076 - val_accuracy: 0.9394 - 64s/epoch - 137ms/step
Epoch 2/2
469/469 - 64s - loss: 0.2775 - accuracy: 0.9175 - val_loss: 0.1417 - val_accuracy: 0.9584 - 64s/epoch - 136ms/step
 

					

			

5．モデル評価

_, (x_test, y_test) = get_dataset(num_classes)
loss, accuracy = model.evaluate(x_test, y_test, batch_size=128)
print("loss:", loss)
print("accuracy:", accuracy)

				
					
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						_, (x_test, y_test) = get_dataset(num_classes)
loss, accuracy = model.evaluate(x_test, y_test, batch_size=128)
print("loss:", loss)
print("accuracy:", accuracy)
 

					

			

79/79 [==============================] - 2s 29ms/step - loss: 0.1417 - accuracy: 0.9584
loss: 0.1416797637939453
accuracy: 0.9584000110626221

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						79/79 [==============================] - 2s 29ms/step - loss: 0.1417 - accuracy: 0.9584
loss: 0.1416797637939453
accuracy: 0.9584000110626221

6．HorovodRunnerを用いた学習

import os
import time
 
# Remove any existing checkpoint files
dbutils.fs.rm(("/ml/MNISTDemo/train"), recurse=True)
 
# Create directory
checkpoint_dir = '/dbfs/ml/MNISTDemo/train/{}/'.format(time.time())
os.makedirs(checkpoint_dir)
print(checkpoint_dir)

				
					
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						import os
import time
 
# Remove any existing checkpoint files
dbutils.fs.rm(("/ml/MNISTDemo/train"), recurse=True)
 
# Create directory
checkpoint_dir = '/dbfs/ml/MNISTDemo/train/{}/'.format(time.time())
os.makedirs(checkpoint_dir)
print(checkpoint_dir)
 

					

			

def train_hvd(checkpoint_path, learning_rate=1.0):
  
  # Import tensorflow modules to each worker
  from tensorflow.keras import backend as K
  from tensorflow.keras.models import Sequential
  import tensorflow as tf
  from tensorflow import keras
  import horovod.tensorflow.keras as hvd
  
  # Initialize Horovod
  hvd.init()
 
  # Pin GPU to be used to process local rank (one GPU per process)
  # These steps are skipped on a CPU cluster
  gpus = tf.config.experimental.list_physical_devices('GPU')
  for gpu in gpus:
    tf.config.experimental.set_memory_growth(gpu, True)
  if gpus:
    tf.config.experimental.set_visible_devices(gpus[hvd.local_rank()], 'GPU')
 
  # Call the get_dataset function you created, this time with the Horovod rank and size
  (x_train, y_train), (x_test, y_test) = get_dataset(num_classes, hvd.rank(), hvd.size())
  model = get_model(num_classes)
 
  # Adjust learning rate based on number of GPUs
  optimizer = keras.optimizers.Adadelta(lr=learning_rate * hvd.size())
 
  # Use the Horovod Distributed Optimizer
  optimizer = hvd.DistributedOptimizer(optimizer)
 
  model.compile(optimizer=optimizer,
                loss='categorical_crossentropy',
                metrics=['accuracy'])
 
  # Create a callback to broadcast the initial variable states from rank 0 to all other processes.
  # This is required to ensure consistent initialization of all workers when training is started with random weights or restored from a checkpoint.
  callbacks = [
      hvd.callbacks.BroadcastGlobalVariablesCallback(0),
  ]
 
  # Save checkpoints only on worker 0 to prevent conflicts between workers
  if hvd.rank() == 0:
      callbacks.append(keras.callbacks.ModelCheckpoint(checkpoint_path, save_weights_only = True))
 
  model.fit(x_train, y_train,
            batch_size=batch_size,
            callbacks=callbacks,
            epochs=epochs,
            verbose=2,
            validation_data=(x_test, y_test))

				
					
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						def train_hvd(checkpoint_path, learning_rate=1.0):
  
  # Import tensorflow modules to each worker
  from tensorflow.keras import backend as K
  from tensorflow.keras.models import Sequential
  import tensorflow as tf
  from tensorflow import keras
  import horovod.tensorflow.keras as hvd
  
  # Initialize Horovod
  hvd.init()
 
  # Pin GPU to be used to process local rank (one GPU per process)
  # These steps are skipped on a CPU cluster
  gpus = tf.config.experimental.list_physical_devices('GPU')
  for gpu in gpus:
    tf.config.experimental.set_memory_growth(gpu, True)
  if gpus:
    tf.config.experimental.set_visible_devices(gpus[hvd.local_rank()], 'GPU')
 
  # Call the get_dataset function you created, this time with the Horovod rank and size
  (x_train, y_train), (x_test, y_test) = get_dataset(num_classes, hvd.rank(), hvd.size())
  model = get_model(num_classes)
 
  # Adjust learning rate based on number of GPUs
  optimizer = keras.optimizers.Adadelta(lr=learning_rate * hvd.size())
 
  # Use the Horovod Distributed Optimizer
  optimizer = hvd.DistributedOptimizer(optimizer)
 
  model.compile(optimizer=optimizer,
                loss='categorical_crossentropy',
                metrics=['accuracy'])
 
  # Create a callback to broadcast the initial variable states from rank 0 to all other processes.
  # This is required to ensure consistent initialization of all workers when training is started with random weights or restored from a checkpoint.
  callbacks = [
      hvd.callbacks.BroadcastGlobalVariablesCallback(0),
  ]
 
  # Save checkpoints only on worker 0 to prevent conflicts between workers
  if hvd.rank() == 0:
      callbacks.append(keras.callbacks.ModelCheckpoint(checkpoint_path, save_weights_only = True))
 
  model.fit(x_train, y_train,
            batch_size=batch_size,
            callbacks=callbacks,
            epochs=epochs,
            verbose=2,
            validation_data=(x_test, y_test))
 

					

			

sparkdlをインストール

%pip install sparkdl
%pip install tensorframes

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						%pip install sparkdl
%pip install tensorframes

from sparkdl import HorovodRunner
 
checkpoint_path = checkpoint_dir + '/checkpoint-{epoch}.ckpt'
learning_rate = 0.1
hr = HorovodRunner(np=2)
hr.run(train_hvd, checkpoint_path=checkpoint_path, learning_rate=learning_rate)

				
					
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						from sparkdl import HorovodRunner
 
checkpoint_path = checkpoint_dir + '/checkpoint-{epoch}.ckpt'
learning_rate = 0.1
hr = HorovodRunner(np=2)
hr.run(train_hvd, checkpoint_path=checkpoint_path, learning_rate=learning_rate)
 

					

			

エラー

ImportError: cannot import name 'resnet50' from 'keras.applications' (/local_disk0/.ephemeral_nfs/envs/pythonEnv-107bf0ee-693b-4dea-8790-78f3dc70991f/lib/python3.8/site-packages/keras/applications/__init__.py)

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						ImportError: cannot import name 'resnet50' from 'keras.applications' (/local_disk0/.ephemeral_nfs/envs/pythonEnv-107bf0ee-693b-4dea-8790-78f3dc70991f/lib/python3.8/site-packages/keras/applications/__init__.py)

お疲れ様でした。

mnist-pytorch

mnist-pytorch – Databricks

Azure Databricksの導入ならナレコムにおまかせください。

導入から活用方法までサポートします。お気軽にご相談ください。

Azure Databricksソリューションページはこちら

この記事を書いた人

azure-recipe-user

記事一覧

Azure DatabricksでMNISTやーる

はじめに

開発環境

mnist-tensorflow-keras

mnist-pytorch

Azure Databricksの導入ならナレコムにおまかせください。

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azure-recipe-user

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