Distributed training is a powerful technique to scale machine learning models using multiple devices or machines. Here's a concise guide to get started with TensorFlow's distributed training capabilities.
🚀 Key Concepts
- Distributed Computing: Training models across multiple GPUs/TPUs or distributed systems.
- TPU/TPUv3: Google's custom ASICs designed for AI workloads.
- Multi-worker Setup: Enables collaboration between multiple machines.
🛠️ Setup Steps
Install TensorFlow
pip install tensorflow
Choose a Distributed Strategy
tf.distribute.MirroredStrategy
(for multi-GPU)tf.distribute.TPUStrategy
(for TPUv3)tf.distribute.MultiWorkerMirroredStrategy
(for multi-machine)
Configure Cluster Communication
Usetf.distribute.cluster_resolver.ClusterResolver
to define the cluster.
🔗 Explore cluster configuration detailsImplement Your Model
Wrap your training loop with the chosen strategy:strategy = tf.distribute.MirroredStrategy() with strategy.scope(): model = tf.keras.Sequential([...])
📈 Best Practices
- Data Parallelism: Distribute data across devices for parallel processing.
- Synchronization: Use
tf.distribute.Redistribute
for efficient data shuffling. - Monitoring: Track metrics with
tf.keras.callbacks.TensorBoard
.
📚 Further Reading
🔗 TensorFlow Distributed Training Guide for advanced topics.