Distributed training with Keras

Stay organized with collections Save and categorize content based on your preferences.

View on TensorFlow.org Run in Google Colab View source on GitHub Download notebook

Overview

The tf.distribute.Strategy API provides an abstraction for distributing your training across multiple processing units. It allows you to carry out distributed training using existing models and training code with minimal changes.

This tutorial demonstrates how to use the tf.distribute.MirroredStrategy to perform in-graph replication with synchronous training on many GPUs on one machine. The strategy essentially copies all of the model's variables to each processor. Then, it uses all-reduce to combine the gradients from all processors, and applies the combined value to all copies of the model.

You will use the tf.keras APIs to build the model and Model.fit for training it. (To learn about distributed training with a custom training loop and the MirroredStrategy, check out this tutorial.)

MirroredStrategy trains your model on multiple GPUs on a single machine. For synchronous training on many GPUs on multiple workers, use the tf.distribute.MultiWorkerMirroredStrategy with the Keras Model.fit or a custom training loop. For other options, refer to the Distributed training guide.

To learn about various other strategies, there is the Distributed training with TensorFlow guide.

Setup

import tensorflow_datasets as tfds
import tensorflow as tf

import os

# Load the TensorBoard notebook extension.
%load_ext tensorboard
print(tf.__version__)
2.8.0

Download the dataset

Load the MNIST dataset from TensorFlow Datasets. This returns a dataset in the tf.data format.

Setting the with_info argument to True includes the metadata for the entire dataset, which is being saved here to info. Among other things, this metadata object includes the number of train and test examples.

datasets, info = tfds.load(name='mnist', with_info=True, as_supervised=True)

mnist_train, mnist_test = datasets['train'], datasets['test']

Define the distribution strategy

Create a MirroredStrategy object. This will handle distribution and provide a context manager (MirroredStrategy.scope) to build your model inside.

strategy = tf.distribute.MirroredStrategy()
INFO:tensorflow:Using MirroredStrategy with devices ('/job:localhost/replica:0/task:0/device:GPU:0',)
INFO:tensorflow:Using MirroredStrategy with devices ('/job:localhost/replica:0/task:0/device:GPU:0',)
print('Number of devices: {}'.format(strategy.num_replicas_in_sync))
Number of devices: 1

Set up the input pipeline

When training a model with multiple GPUs, you can use the extra computing power effectively by increasing the batch size. In general, use the largest batch size that fits the GPU memory and tune the learning rate accordingly.

# You can also do info.splits.total_num_examples to get the total
# number of examples in the dataset.

num_train_examples = info.splits['train'].num_examples
num_test_examples = info.splits['test'].num_examples

BUFFER_SIZE = 10000

BATCH_SIZE_PER_REPLICA = 64
BATCH_SIZE = BATCH_SIZE_PER_REPLICA * strategy.num_replicas_in_sync

Define a function that normalizes the image pixel values from the [0, 255] range to the [0, 1] range (feature scaling):

def scale(image, label):
  image = tf.cast(image, tf.float32)
  image /= 255

  return image, label

Apply this scale function to the training and test data, and then use the tf.data.Dataset APIs to shuffle the training data (Dataset.shuffle), and batch it (Dataset.batch). Notice that you are also keeping an in-memory cache of the training data to improve performance (Dataset.cache).

train_dataset = mnist_train.map(scale).cache().shuffle(BUFFER_SIZE).batch(BATCH_SIZE)
eval_dataset = mnist_test.map(scale).batch(BATCH_SIZE)

Create the model

Within the context of Strategy.scope, create and compile the model using the Keras API:

with strategy.scope():
  model = tf.keras.Sequential([
      tf.keras.layers.Conv2D(32, 3, activation='relu', input_shape=(28, 28, 1)),
      tf.keras.layers.MaxPooling2D(),
      tf.keras.layers.Flatten(),
      tf.keras.layers.Dense(64, activation='relu'),
      tf.keras.layers.Dense(10)
  ])

  model.compile(loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
                optimizer=tf.keras.optimizers.Adam(),
                metrics=['accuracy'])
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).

Define the callbacks

Define the following Keras Callbacks:

For illustrative purposes, add a custom callback called PrintLR to display the learning rate in the notebook.

# Define the checkpoint directory to store the checkpoints.
checkpoint_dir = './training_checkpoints'
# Define the name of the checkpoint files.
checkpoint_prefix = os.path.join(checkpoint_dir, "ckpt_{epoch}")
# Define a function for decaying the learning rate.
# You can define any decay function you need.
def decay(epoch):
  if epoch < 3:
    return 1e-3
  elif epoch >= 3 and epoch < 7:
    return 1e-4
  else:
    return 1e-5
# Define a callback for printing the learning rate at the end of each epoch.
class PrintLR(tf.keras.callbacks.Callback):
  def on_epoch_end(self, epoch, logs=None):
    print('\nLearning rate for epoch {} is {}'.format(        epoch + 1, model.optimizer.lr.numpy()))
# Put all the callbacks together.
callbacks = [
    tf.keras.callbacks.TensorBoard(log_dir='./logs'),
    tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_prefix,
                                       save_weights_only=True),
    tf.keras.callbacks.LearningRateScheduler(decay),
    PrintLR()
]

Train and evaluate

Now, train the model in the usual way by calling Keras Model.fit on the model and passing in the dataset created at the beginning of the tutorial. This step is the same whether you are distributing the training or not.

EPOCHS = 12

model.fit(train_dataset, epochs=EPOCHS, callbacks=callbacks)
2022-04-01 01:21:46.208813: W tensorflow/core/grappler/optimizers/data/auto_shard.cc:547] The `assert_cardinality` transformation is currently not handled by the auto-shard rewrite and will be removed.
Epoch 1/12
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
931/938 [============================>.] - ETA: 0s - loss: 0.1985 - accuracy: 0.9429
Learning rate for epoch 1 is 0.0010000000474974513
938/938 [==============================] - 21s 5ms/step - loss: 0.1977 - accuracy: 0.9431 - lr: 0.0010
Epoch 2/12
934/938 [============================>.] - ETA: 0s - loss: 0.0630 - accuracy: 0.9813
Learning rate for epoch 2 is 0.0010000000474974513
938/938 [==============================] - 4s 4ms/step - loss: 0.0630 - accuracy: 0.9813 - lr: 0.0010
Epoch 3/12
929/938 [============================>.] - ETA: 0s - loss: 0.0459 - accuracy: 0.9860
Learning rate for epoch 3 is 0.0010000000474974513
938/938 [==============================] - 4s 4ms/step - loss: 0.0459 - accuracy: 0.9859 - lr: 0.0010
Epoch 4/12
937/938 [============================>.] - ETA: 0s - loss: 0.0241 - accuracy: 0.9935
Learning rate for epoch 4 is 9.999999747378752e-05
938/938 [==============================] - 4s 4ms/step - loss: 0.0242 - accuracy: 0.9934 - lr: 1.0000e-04
Epoch 5/12
932/938 [============================>.] - ETA: 0s - loss: 0.0212 - accuracy: 0.9944
Learning rate for epoch 5 is 9.999999747378752e-05
938/938 [==============================] - 4s 5ms/step - loss: 0.0212 - accuracy: 0.9944 - lr: 1.0000e-04
Epoch 6/12
938/938 [==============================] - ETA: 0s - loss: 0.0196 - accuracy: 0.9949
Learning rate for epoch 6 is 9.999999747378752e-05
938/938 [==============================] - 4s 4ms/step - loss: 0.0196 - accuracy: 0.9949 - lr: 1.0000e-04
Epoch 7/12
934/938 [============================>.] - ETA: 0s - loss: 0.0180 - accuracy: 0.9954
Learning rate for epoch 7 is 9.999999747378752e-05
938/938 [==============================] - 4s 4ms/step - loss: 0.0179 - accuracy: 0.9955 - lr: 1.0000e-04
Epoch 8/12
937/938 [============================>.] - ETA: 0s - loss: 0.0156 - accuracy: 0.9964
Learning rate for epoch 8 is 9.999999747378752e-06
938/938 [==============================] - 4s 4ms/step - loss: 0.0156 - accuracy: 0.9964 - lr: 1.0000e-05
Epoch 9/12
930/938 [============================>.] - ETA: 0s - loss: 0.0152 - accuracy: 0.9966
Learning rate for epoch 9 is 9.999999747378752e-06
938/938 [==============================] - 4s 4ms/step - loss: 0.0153 - accuracy: 0.9966 - lr: 1.0000e-05
Epoch 10/12
936/938 [============================>.] - ETA: 0s - loss: 0.0151 - accuracy: 0.9967
Learning rate for epoch 10 is 9.999999747378752e-06
938/938 [==============================] - 4s 4ms/step - loss: 0.0151 - accuracy: 0.9967 - lr: 1.0000e-05
Epoch 11/12
931/938 [============================>.] - ETA: 0s - loss: 0.0149 - accuracy: 0.9967
Learning rate for epoch 11 is 9.999999747378752e-06
938/938 [==============================] - 4s 4ms/step - loss: 0.0149 - accuracy: 0.9967 - lr: 1.0000e-05
Epoch 12/12
935/938 [============================>.] - ETA: 0s - loss: 0.0148 - accuracy: 0.9967
Learning rate for epoch 12 is 9.999999747378752e-06
938/938 [==============================] - 4s 4ms/step - loss: 0.0148 - accuracy: 0.9967 - lr: 1.0000e-05
<keras.callbacks.History at 0x7f463c575150>

Check for saved checkpoints:

# Check the checkpoint directory.
ls {checkpoint_dir}
checkpoint           ckpt_4.data-00000-of-00001
ckpt_1.data-00000-of-00001   ckpt_4.index
ckpt_1.index             ckpt_5.data-00000-of-00001
ckpt_10.data-00000-of-00001  ckpt_5.index
ckpt_10.index            ckpt_6.data-00000-of-00001
ckpt_11.data-00000-of-00001  ckpt_6.index
ckpt_11.index            ckpt_7.data-00000-of-00001
ckpt_12.data-00000-of-00001  ckpt_7.index
ckpt_12.index            ckpt_8.data-00000-of-00001
ckpt_2.data-00000-of-00001   ckpt_8.index
ckpt_2.index             ckpt_9.data-00000-of-00001
ckpt_3.data-00000-of-00001   ckpt_9.index
ckpt_3.index

To check how well the model performs, load the latest checkpoint and call Model.evaluate on the test data:

model.load_weights(tf.train.latest_checkpoint(checkpoint_dir))

eval_loss, eval_acc = model.evaluate(eval_dataset)

print('Eval loss: {}, Eval accuracy: {}'.format(eval_loss, eval_acc))
2022-04-01 01:22:53.681910: W tensorflow/core/grappler/optimizers/data/auto_shard.cc:547] The `assert_cardinality` transformation is currently not handled by the auto-shard rewrite and will be removed.
157/157 [==============================] - 4s 4ms/step - loss: 0.0378 - accuracy: 0.9873
Eval loss: 0.037795402109622955, Eval accuracy: 0.9872999787330627

To visualize the output, launch TensorBoard and view the logs:

%tensorboard --logdir=logs

ls -sh ./logs
total 4.0K
4.0K train

Export to SavedModel

Export the graph and the variables to the platform-agnostic SavedModel format using Keras Model.save. After your model is saved, you can load it with or without the Strategy.scope.

path = 'saved_model/'
model.save(path, save_format='tf')
2022-04-01 01:22:57.859002: W tensorflow/python/util/util.cc:368] Sets are not currently considered sequences, but this may change in the future, so consider avoiding using them.
INFO:tensorflow:Assets written to: saved_model/assets
INFO:tensorflow:Assets written to: saved_model/assets

Now, load the model without Strategy.scope:

unreplicated_model = tf.keras.models.load_model(path)

unreplicated_model.compile(
    loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
    optimizer=tf.keras.optimizers.Adam(),
    metrics=['accuracy'])

eval_loss, eval_acc = unreplicated_model.evaluate(eval_dataset)

print('Eval loss: {}, Eval Accuracy: {}'.format(eval_loss, eval_acc))
157/157 [==============================] - 1s 3ms/step - loss: 0.0378 - accuracy: 0.9873
Eval loss: 0.037795402109622955, Eval Accuracy: 0.9872999787330627

Load the model with Strategy.scope:

with strategy.scope():
  replicated_model = tf.keras.models.load_model(path)
  replicated_model.compile(loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
                           optimizer=tf.keras.optimizers.Adam(),
                           metrics=['accuracy'])

  eval_loss, eval_acc = replicated_model.evaluate(eval_dataset)
  print ('Eval loss: {}, Eval Accuracy: {}'.format(eval_loss, eval_acc))
2022-04-01 01:22:59.497815: W tensorflow/core/grappler/optimizers/data/auto_shard.cc:547] The `assert_cardinality` transformation is currently not handled by the auto-shard rewrite and will be removed.
157/157 [==============================] - 3s 4ms/step - loss: 0.0378 - accuracy: 0.9873
Eval loss: 0.037795402109622955, Eval Accuracy: 0.9872999787330627

Additional resources

More examples that use different distribution strategies with the Keras Model.fit API:

  1. The Solve GLUE tasks using BERT on TPU tutorial uses tf.distribute.MirroredStrategy for training on GPUs and tf.distribute.TPUStrategy on TPUs.
  2. The Save and load a model using a distribution strategy tutorial demonstates how to use the SavedModel APIs with tf.distribute.Strategy.
  3. The official TensorFlow models can be configured to run multiple distribution strategies.

To learn more about TensorFlow distribution strategies:

  1. The Custom training with tf.distribute.Strategy tutorial shows how to use the tf.distribute.MirroredStrategy for single-worker training with a custom training loop.
  2. The Multi-worker training with Keras tutorial shows how to use the MultiWorkerMirroredStrategy with Model.fit.
  3. The Custom training loop with Keras and MultiWorkerMirroredStrategy tutorial shows how to use the MultiWorkerMirroredStrategy with Keras and a custom training loop.
  4. The Distributed training in TensorFlow guide provides an overview of the available distribution strategies.
  5. The Better performance with tf.function guide provides information about other strategies and tools, such as the TensorFlow Profiler you can use to optimize the performance of your TensorFlow models.