Horovod is a distributed deep learning training framework designed for TensorFlow, PyTorch, and Keras. Below are key advanced topics and configurations:

🧠 Distributed Training Optimization

  • Multi-GPU Support: Use HorovodRunner for multi-machine multi-GPU training
    Distributed_Training
  • Data Parallelism: Leverage allreduce operations for efficient gradient synchronization
    Data_Parallelism
  • Custom Backend: Implement custom communication backends for specialized hardware
    Custom_Backend

🛠️ Custom Configuration Options

  • Environment Variables:
    • OMPI_MCA_btl: Control bandwidth allocation for inter-node communication
    • HOROVOD_GPU_ALLREDUCE: Enable/disable GPU-based allreduce
      Environment_Variables
  • TensorFlow Keras Integration: Use tf.keras with Horovod for distributed model training
    TensorFlow_Keras

📊 Performance Monitoring

  • TensorBoard Integration: Monitor training metrics across distributed nodes
    TensorBoard
  • System Profiling: Use horovodrun with --log flag for detailed execution logs
    System_Profiling

For deeper insights into training strategies, visit our Advanced Training Strategies guide. 🚀