Horovod Configuration Guide

Horovod is a distributed deep learning training framework designed for multi-GPU and multi-node setups. Proper configuration ensures optimal performance and scalability. Below are key settings and recommendations:

Installation & Dependencies

• Install Horovod:

  pip install horovod  
  

  📌 For detailed installation steps, see Horovod Installation Docs.
• Dependencies:
  • TensorFlow/PyTorch version compatibility
  • MPI (Message Passing Interface) support via openmpi
  • Networking configuration for cluster communication

Core Concepts

• Distributed Training:
  • Use horovodrun to launch training across multiple workers
  • Example:

    horovodrun -np 4 python train.py  
    

• Data Parallelism:
  • Splits data across GPUs and aggregates gradients
  • 📎 Learn more about data parallel
• Model Parallelism:
  • Splits model across GPUs (for large models)
  • Requires custom implementation

Advanced Configuration

• Customize Horovod Settings:
  • Modify horovod/tensorflow/vars.py or horovod/pytorch/vars.py for framework-specific tweaks
  • Adjust HOROVOD_MPI_THREADS_AWARE environment variable for thread-aware MPI
• Cluster Architecture:
  • Single-node multi-GPU vs multi-node distributed setups
  • Use --hostfile to specify worker addresses
  • 📊 Cluster setup diagram

Best Practices

• Optimize Communication:
  • Use rdma or gRPC for faster inter-node communication
  • Monitor bandwidth with horovodrun --monitor
• Configuration Example:

  horovod:  
    backend: "mpi"  
    tensorflow:  
      num_gpus: 4  
    pytorch:  
      use_all_available_gpus: true  
  

  📌 For more examples, check Horovod Configuration Samples.