TensorFlow Distribution Strategies provide a flexible way to scale models across multiple devices and hosts. This guide will walk you through key concepts and implementation examples.

Key Concepts 📚

  • MirroredStrategy: Synchronizes gradients across all GPUs/TPUs
    Mirrored_Strategy
  • MultiWorkerMirroredStrategy: Enables multi-machine training
    MultiWorker_Mirrored_Strategy
  • TPUStrategy: Optimized for Google Cloud TPUs
    TPU_Strategy

Implementation Example 🧪

import tensorflow as tf

strategy = tf.distribute.MirroredStrategy()
with strategy.scope():
    model = tf.keras.Sequential([
        tf.keras.layers.Dense(128, activation='relu', input_shape=(64,)),
        tf.keras.layers.Dense(64, activation='relu'),
        tf.keras.layers.Dense(10)
    ])
    model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])

Best Practices ✅

  • Use MirroredStrategy for single-machine multi-GPU training
  • For distributed training across multiple machines, use MultiWorkerMirroredStrategy
  • Always validate your strategy configuration with tf.distribute.cluster_resolver

For deeper exploration of TensorFlow's distributed training capabilities, check here.