When to Use `VariableSynchronization` in TensorFlow

TensorFlow is an open-source platform that provides a set of comprehensive tools to help developers efficiently build and train machine learning models. For more advanced usage scenarios, TensorFlow provides several mechanisms to control how variables are accessed and updated within the model code. One such mechanism is VariableSynchronization. In this article, we will explore what VariableSynchronization is, when and why you should use it, and provide some practical examples.

Understanding Variable Synchronization in TensorFlow

In distributed machine learning, computing generally occurs across multiple devices, such as multiple GPUs or CPU cores. In such settings, there might be several copies of the same variable on different devices. Synchronizing variables ensures consistency of these variables across all devices.

In TensorFlow, VariableSynchronization is an enumeration that provides several strategies for synchronizing variables:

• ON_READ: Synchronize on read access, ensuring each read involves fetching the latest value.
• ON_WRITE: Synchronize during writes, updating all copies of the variable when it's modified.
• AUTO: The system automatically determines when to synchronize (default method).
• NONE: No synchronization is performed.

When to Use Each Synchronization Strategy

Choosing the right synchronization strategy depends on your application needs:

• ON_READ: This strategy is beneficial when it is crucial to have the most recent value of a variable for each read operation. It provides consistency but might increase read latency because it fetches the value from a variable server.
• ON_WRITE: This strategy is suitable when exact consistency across reads is less crucial, but consistency after updates is necessary. It reduces the number of write operations but ensures that all writes are consistent.
• AUTO: It is preferable to leave synchronization to AUTO if you do not have particular consistency requirements and want TensorFlow to optimize performance.

Using VariableSynchronization in TensorFlow Code

To apply VariableSynchronization in your distributed TensorFlow application, you generally specify it during the creation of variable initializers in the model. Let’s see an example of how to implement this:

import tensorflow as tf

def create_variable()
    with tf.distribute.Strategy().scope():
        var = tf.Variable(
            initial_value=0.0,
            trainable=True,
            synchronization=tf.VariableSynchronization.ON_WRITE
        )
        return var

variable = create_variable()

In the code above, a variable is initialized within the scope of a distribution strategy with the synchronization set to ON_WRITE, which ensures write consistency across devices.

Practical Example: Training with Multiple GPUs

Suppose you are training a deep learning model using multiple GPUs, you might set up a mirrored strategy:

strategy = tf.distribute.MirroredStrategy()

with strategy.scope():
    # Model and optimizer initialization here...
    var = tf.Variable(
        initial_value=0.0,
        trainable=True,
        synchronization=tf.VariableSynchronization.ON_WRITE
    )

    def step_fn(inputs):
        # Model training step...
        loss = compute_loss(inputs)
        gradients = optimizer.compute_gradients(loss)
        optimizer.apply_gradients(zip(gradients, var))

    for data in dataset:
        strategy.run(step_fn, args=(data,))

In this example, the mirrored strategy ensures that each GPU computes gradients on its local mini-batch, while VariableSynchronization.ON_WRITE maintains synchronization integrity during updates across devices.

Conclusion

Using VariableSynchronization effectively can help achieve optimal model performance and consistency across multiple devices in a distributed training setup. Depending on the application's synchronization needs, developers can make use of TensorFlow's various strategies. In most scenarios, allowing the system to automatically handle synchronization or choosing specific strategies like ON_WRITE when necessary will be adequate for ensuring that models stay synchronized and performant.