save_and_load.ipynb

Kernel: Python 3

Copyright 2019 The TensorFlow Authors.

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Save and load a model using a distribution strategy

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Overview

This tutorial demonstrates how you can save and load models in a SavedModel format with tf.distribute.Strategy during or after training. There are two kinds of APIs for saving and loading a Keras model: high-level (tf.keras.Model.save and tf.keras.models.load_model) and low-level (tf.saved_model.save and tf.saved_model.load).

To learn about SavedModel and serialization in general, please read the saved model guide, and the Keras model serialization guide. Let's start with a simple example.

Caution: TensorFlow models are code and it is important to be careful with untrusted code. Learn more in Using TensorFlow securely.

Import dependencies:

import tensorflow_datasets as tfds

import tensorflow as tf

Load and prepare the data with TensorFlow Datasets and tf.data, and create the model using tf.distribute.MirroredStrategy:

mirrored_strategy = tf.distribute.MirroredStrategy()

def get_data():
  datasets = tfds.load(name='mnist', as_supervised=True)
  mnist_train, mnist_test = datasets['train'], datasets['test']

  BUFFER_SIZE = 10000

  BATCH_SIZE_PER_REPLICA = 64
  BATCH_SIZE = BATCH_SIZE_PER_REPLICA * mirrored_strategy.num_replicas_in_sync

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

    return image, label

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

  return train_dataset, eval_dataset

def get_model():
  with mirrored_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=[tf.metrics.SparseCategoricalAccuracy()])
    return model

Train the model with tf.keras.Model.fit:

model = get_model()
train_dataset, eval_dataset = get_data()
model.fit(train_dataset, epochs=2)

Save and load the model

Now that you have a simple model to work with, let's explore the saving/loading APIs. There are two kinds of APIs available:

• High-level (Keras): Model.save and tf.keras.models.load_model (.keras zip archive format)

• Low-level: tf.saved_model.save and tf.saved_model.load (TF SavedModel format)

The Keras API

Here is an example of saving and loading a model with the Keras API:

keras_model_path = '/tmp/keras_save.keras'
model.save(keras_model_path)

Restore the model without tf.distribute.Strategy:

restored_keras_model = tf.keras.models.load_model(keras_model_path)
restored_keras_model.fit(train_dataset, epochs=2)

After restoring the model, you can continue training on it, even without needing to call Model.compile again, since it was already compiled before saving. The model is saved a Keras zip archive format, marked by the .keras extension. For more information, please refer to the guide on Keras saving.

Now, restore the model and train it using a tf.distribute.Strategy:

another_strategy = tf.distribute.OneDeviceStrategy('/cpu:0')
with another_strategy.scope():
  restored_keras_model_ds = tf.keras.models.load_model(keras_model_path)
  restored_keras_model_ds.fit(train_dataset, epochs=2)

As the Model.fit output shows, loading works as expected with tf.distribute.Strategy. The strategy used here does not have to be the same strategy used before saving.

The tf.saved_model API

Saving the model with lower-level API is similar to the Keras API:

model = get_model()  # get a fresh model
saved_model_path = '/tmp/tf_save'
tf.saved_model.save(model, saved_model_path)

Loading can be done with tf.saved_model.load. However, since it is a lower-level API (and hence has a wider range of use cases), it does not return a Keras model. Instead, it returns an object that contain functions that can be used to do inference. For example:

DEFAULT_FUNCTION_KEY = 'serving_default'
loaded = tf.saved_model.load(saved_model_path)
inference_func = loaded.signatures[DEFAULT_FUNCTION_KEY]

The loaded object may contain multiple functions, each associated with a key. The "serving_default" key is the default key for the inference function with a saved Keras model. To do inference with this function:

predict_dataset = eval_dataset.map(lambda image, label: image)
for batch in predict_dataset.take(1):
  print(inference_func(batch))

You can also load and do inference in a distributed manner:

another_strategy = tf.distribute.MirroredStrategy()
with another_strategy.scope():
  loaded = tf.saved_model.load(saved_model_path)
  inference_func = loaded.signatures[DEFAULT_FUNCTION_KEY]

  dist_predict_dataset = another_strategy.experimental_distribute_dataset(
      predict_dataset)

  # Calling the function in a distributed manner
  for batch in dist_predict_dataset:
    result = another_strategy.run(inference_func, args=(batch,))
    print(result)
    break

Calling the restored function is just a forward pass on the saved model (tf.keras.Model.predict). What if you want to continue training the loaded function? Or what if you need to embed the loaded function into a bigger model? A common practice is to wrap this loaded object into a Keras layer to achieve this. Luckily, TF Hub has hub.KerasLayer for this purpose, shown here:

import tensorflow_hub as hub

def build_model(loaded):
  x = tf.keras.layers.Input(shape=(28, 28, 1), name='input_x')
  # Wrap what's loaded to a KerasLayer
  keras_layer = hub.KerasLayer(loaded, trainable=True)(x)
  model = tf.keras.Model(x, keras_layer)
  return model

another_strategy = tf.distribute.MirroredStrategy()
with another_strategy.scope():
  loaded = tf.saved_model.load(saved_model_path)
  model = build_model(loaded)

  model.compile(loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
                optimizer=tf.keras.optimizers.Adam(),
                metrics=[tf.metrics.SparseCategoricalAccuracy()])
  model.fit(train_dataset, epochs=2)

In the above example, Tensorflow Hub's hub.KerasLayer wraps the result loaded back from tf.saved_model.load into a Keras layer that is used to build another model. This is very useful for transfer learning.

Which API should I use?

For saving, if you are working with a Keras model, use the Keras Model.save API unless you need the additional control allowed by the low-level API. If what you are saving is not a Keras model, then the lower-level API, tf.saved_model.save, is your only choice.

For loading, your API choice depends on what you want to get from the model loading API. If you cannot (or do not want to) get a Keras model, then use tf.saved_model.load. Otherwise, use tf.keras.models.load_model. Note that you can get a Keras model back only if you saved a Keras model.

It is possible to mix and match the APIs. You can save a Keras model with Model.save, and load a non-Keras model with the low-level API, tf.saved_model.load.

model = get_model()

# Saving the model using Keras `Model.save`
model.save(saved_model_path)

another_strategy = tf.distribute.MirroredStrategy()
# Loading the model using the lower-level API
with another_strategy.scope():
  loaded = tf.saved_model.load(saved_model_path)

Saving/Loading from a local device

When saving and loading from a local I/O device while training on remote devices—for example, when using a Cloud TPU—you must use the option experimental_io_device in tf.saved_model.SaveOptions and tf.saved_model.LoadOptions to set the I/O device to localhost. For example:

model = get_model()

# Saving the model to a path on localhost.
saved_model_path = '/tmp/tf_save'
save_options = tf.saved_model.SaveOptions(experimental_io_device='/job:localhost')
model.save(saved_model_path, options=save_options)

# Loading the model from a path on localhost.
another_strategy = tf.distribute.MirroredStrategy()
with another_strategy.scope():
  load_options = tf.saved_model.LoadOptions(experimental_io_device='/job:localhost')
  loaded = tf.keras.models.load_model(saved_model_path, options=load_options)

Caveats

One special case is when you create Keras models in certain ways, and then save them before training. For example:

class SubclassedModel(tf.keras.Model):
  """Example model defined by subclassing `tf.keras.Model`."""

  output_name = 'output_layer'

  def __init__(self):
    super(SubclassedModel, self).__init__()
    self._dense_layer = tf.keras.layers.Dense(
        5, dtype=tf.dtypes.float32, name=self.output_name)

  def call(self, inputs):
    return self._dense_layer(inputs)

my_model = SubclassedModel()
try:
  my_model.save(saved_model_path)
except ValueError as e:
  print(f'{type(e).__name__}: ', *e.args)

A SavedModel saves the tf.types.experimental.ConcreteFunction objects generated when you trace a tf.function (check When is a Function tracing? in the Introduction to graphs and tf.function guide to learn more). If you get a ValueError like this it's because Model.save was not able to find or create a traced ConcreteFunction.

Caution: You shouldn't save a model without at least one ConcreteFunction, since the low-level API will otherwise generate a SavedModel with no ConcreteFunction signatures (learn more about the SavedModel format). For example:

tf.saved_model.save(my_model, saved_model_path)
x = tf.saved_model.load(saved_model_path)
x.signatures

Usually the model's forward pass—the call method—will be traced automatically when the model is called for the first time, often via the Keras Model.fit method. A ConcreteFunction can also be generated by the Keras Sequential and Functional APIs, if you set the input shape, for example, by making the first layer either a tf.keras.layers.InputLayer or another layer type, and passing it the input_shape keyword argument.

To verify if your model has any traced ConcreteFunctions, check if Model.save_spec is None:

print(my_model.save_spec() is None)

Let's use tf.keras.Model.fit to train the model, and notice that the save_spec gets defined and model saving will work:

BATCH_SIZE_PER_REPLICA = 4
BATCH_SIZE = BATCH_SIZE_PER_REPLICA * mirrored_strategy.num_replicas_in_sync

dataset_size = 100
dataset = tf.data.Dataset.from_tensors(
    (tf.range(5, dtype=tf.float32), tf.range(5, dtype=tf.float32))
    ).repeat(dataset_size).batch(BATCH_SIZE)

my_model.compile(optimizer='adam', loss='mean_squared_error')
my_model.fit(dataset, epochs=2)

print(my_model.save_spec() is None)
my_model.save(saved_model_path)