TensorFlow Overview with Horovod 🧠

Horovod is a distributed deep learning framework designed to simplify distributed training across multiple GPUs or nodes. It integrates seamlessly with TensorFlow, PyTorch, and other frameworks, making it a popular choice for scaling AI workloads.

Key Features

  • Scalability: Efficiently scales training across clusters using MPI (Message Passing Interface).
  • Ease of Use: Minimal code changes required to convert single-node TensorFlow models into distributed ones.
  • Performance: Optimized for multi-GPU environments with built-in support for TensorFlow and Kubernetes.

Getting Started

  1. Install Horovod: 
    pip install horovod
  2. Initialize a distributed environment: 
    import horovod.tensorflow as hvd
hvd.init()
  3. Launch training with mpirun or horovodrun: 
    mpirun -np 4 python train.py
TensorFlow Distributed Training

For a hands-on tutorial, check out our TensorFlow Quickstart Guide to set up a distributed model in minutes!

Tips & Resources

  • 📚 Horovod Documentation for advanced configurations.
  • 🧪 Use tf.distribute.MirroredStrategy for multi-GPU training.
  • ⚠️ Ensure all workers have synchronized data and compatible versions.
Horovod TensorFlow Integration

Let me know if you'd like to explore PyTorch tutorials next! 🚀