Introduction to Distributed Training

Distributed training enables you to train machine learning models across multiple devices (GPUs/TPUs) or machines. TensorFlow provides powerful tools to simplify this process. 🧠💡

Key Concepts

  • Cluster: A group of interconnected devices/machines
  • Strategy: API for distributing workloads (e.g., MirroredStrategy, MultiWorkerMirroredStrategy)
  • Data Parallelism: Replicating model across devices and splitting data
  • Model Parallelism: Splitting model across devices

Setting Up Distributed Environment

  1. Install TensorFlow 
    pip install tensorflow
  2. Configure Cluster Specification
    Use tf.distribute.cluster_resolver.ClusterResolver to define your cluster. 🛠️

Popular Strategies

1. MirroredStrategy (Single Machine Multi-GPU)

strategy = tf.distribute.MirroredStrategy()
with strategy.scope():
    model = tf.keras.Sequential([...])

2. MultiWorkerMirroredStrategy (Multi-Machine)

strategy = tf.distribute.MultiWorkerMirroredStrategy()

🔗 Learn more about strategy APIs

Training Workflow Example

  1. Create model with strategy.scope()
  2. Compile and train as usual
  3. Monitor training with tf.distribute.MetricAggregation
  4. Evaluate model performance across devices

Advanced Topics

  • TPU Support: Learn how to use Tensor Processing Units
  • Custom Training Loops: Implement distributed training manually
  • Optimization Techniques: Gradient clipping, synchronous updates

Resources

📚 TensorFlow Distributed Guide
💻 Distributed Training Demo

distributed_training
tensorflow_architecture