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Migrate single-worker multiple-GPU training

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This guide demonstrates how to migrate the single-worker multiple-GPU workflows from TensorFlow 1 to TensorFlow 2.

To perform synchronous training across multiple GPUs on one machine:


Start with imports and a simple dataset for demonstration purposes:

import tensorflow as tf
import tensorflow.compat.v1 as tf1
features = [[1., 1.5], [2., 2.5], [3., 3.5]]
labels = [[0.3], [0.5], [0.7]]
eval_features = [[4., 4.5], [5., 5.5], [6., 6.5]]
eval_labels = [[0.8], [0.9], [1.]]

TensorFlow 1: Single-worker distributed training with tf.estimator.Estimator

This example demonstrates the TensorFlow 1 canonical workflow of single-worker multiple-GPU training. You need to set the distribution strategy (tf.distribute.MirroredStrategy) through the config parameter of the tf.estimator.Estimator:

def _input_fn():
  return, labels)).batch(1)

def _eval_input_fn():
      (eval_features, eval_labels)).batch(1)

def _model_fn(features, labels, mode):
  logits = tf1.layers.Dense(1)(features)
  loss = tf1.losses.mean_squared_error(labels=labels, predictions=logits)
  optimizer = tf1.train.AdagradOptimizer(0.05)
  train_op = optimizer.minimize(loss, global_step=tf1.train.get_global_step())
  return tf1.estimator.EstimatorSpec(mode, loss=loss, train_op=train_op)

strategy = tf1.distribute.MirroredStrategy()
config = tf1.estimator.RunConfig(
    train_distribute=strategy, eval_distribute=strategy)
estimator = tf1.estimator.Estimator(model_fn=_model_fn, config=config)

train_spec = tf1.estimator.TrainSpec(input_fn=_input_fn)
eval_spec = tf1.estimator.EvalSpec(input_fn=_eval_input_fn)
tf1.estimator.train_and_evaluate(estimator, train_spec, eval_spec)

TensorFlow 2: Single-worker training with Keras

When migrating to TensorFlow 2, you can use the Keras APIs with tf.distribute.MirroredStrategy.

If you use the tf.keras APIs for model building and Keras for training, the main difference is instantiating the Keras model, an optimizer, and metrics in the context of Strategy.scope, instead of defining a config for tf.estimator.Estimator.

If you need to use a custom training loop, check out the Using tf.distribute.Strategy with custom training loops guide.

dataset =, labels)).batch(1)
eval_dataset =
      (eval_features, eval_labels)).batch(1)
strategy = tf.distribute.MirroredStrategy()
with strategy.scope():
  model = tf.keras.models.Sequential([tf.keras.layers.Dense(1)])
  optimizer = tf.keras.optimizers.Adagrad(learning_rate=0.05)

model.compile(optimizer=optimizer, loss='mse')
model.evaluate(eval_dataset, return_dict=True)

Next steps

To learn more about distributed training with tf.distribute.MirroredStrategy in TensorFlow 2, check out the following documentation: