Introduction to Synchronization Modes in TensorFlow Distributed Training
TensorFlow is a powerful open-source library developed by Google, primarily used for machine learning applications. One of its features is the ability to perform distributed training, which allows models to be trained on multiple devices to accelerate the process. A crucial aspect of distributed training is synchronization, ensuring all devices update the model parameters coherently. Two primary synchronization strategies are utilized: data-parallelism with synchronous updates and asynchronous updates. This article explores these concepts and provides examples of how they work in TensorFlow.
Synchronous Training
In synchronous training, the model's parameters are updated only after all devices in the distributed network complete their computations for a single step. This ensures consistency in the updates, as they are based on the aggregated gradients from all devices.
import tensorflow as tf
strategy = tf.distribute.MirroredStrategy()
with strategy.scope():
# Define model
dependent code here...
model = tf.keras.Sequential([
tf.keras.layers.Dense(128, activation='relu'),
tf.keras.layers.Dense(10, activation='softmax')
])
# Compile model
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
# Dataset preparation
mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0
# Fit model
model.fit(x_train, y_train, epochs=5)
In this example, we employ the MirroredStrategy, which synchronously mirrors all variables across all replicas. After each step, updates are synchronized, ensuring all replicas start each subsequent step with the same model parameters.
Asynchronous Training
Contrary to synchronous, asynchronous training does not wait for all devices to complete a step before updating model parameters. This means updates occur as soon as a device completes its computation, leading to potential inconsistency in parameters across devices. However, it generally allows for faster training since it does not require coordination across devices for every step.
import tensorflow as tf
strategy = tf.distribute.experimental.CentralStorageStrategy()
with strategy.scope():
# Define model
dependent code here...
model = tf.keras.Sequential([
tf.keras.layers.Dense(128, activation='relu'),
tf.keras.layers.Dense(10, activation='softmax')
])
# Compile model
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
# Dataset preparation
mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0
# Fit model
model.fit(x_train, y_train, epochs=5)
The above example uses CentralStorageStrategy to facilitate asynchronous updates to the model parameters. Here, the variables are stored on a single device, and all computations fetch current values as needed, allowing updates without the synchronization bottleneck.
Choosing Between Synchronization Modes
The choice between synchronous and asynchronous training depends on the specific use case with considerations on consistency, speed, and resources. Synchronous training is preferred when exact reproducibility is needed. On the other hand, asynchronous training benefits conditions requiring faster convergence with some tolerance for inconsistency across updates.
Conclusion
Synchronization modes in TensorFlow play a critical role in distributed training, directly affecting model performance and training speed. Utilizing the appropriate strategy allows for efficient resource use, ensuring model training is both effective and expedient. Explore these modes further, employing different TensorFlow strategies to understand how they impact training outcomes in various machine learning scenarios.
