Horovod
What it is: Distributed deep learning framework from Uber that makes training on multiple GPUs and machines as easy as single GPU training.
What It Does Best
Simple distributed training. Add a few lines of code to scale from single GPU to hundreds of GPUs. No need to rewrite your training loop or manage complex distributed logic.
Framework agnostic. Works with TensorFlow, PyTorch, Keras, and MXNet. Switch frameworks without learning new distributed APIs.
MPI-based efficiency. Uses ring-allreduce algorithm for optimal communication. Near-linear scaling to hundreds of GPUs without the overhead of parameter servers.
Key Features
Data parallelism: Distribute training data across multiple GPUs/machines
Framework support: TensorFlow, PyTorch, Keras, MXNet, Apache MXNet
Ring-allreduce: Efficient gradient synchronization algorithm
Timeline: Performance profiling and bottleneck detection
Elastic training: Add/remove workers during training
Pricing
Free: Open source (Apache 2.0 license)
Commercial: No licensing costs for any use
Cloud: Free software, pay only for compute resources
When to Use It
✅ Need to scale training across multiple GPUs
✅ Want simple distributed training without complexity
✅ Using standard frameworks (TensorFlow, PyTorch)
✅ Training takes too long on single GPU
✅ Need data parallelism for large datasets
When NOT to Use It
❌ Single GPU is sufficient for your training
❌ Need model parallelism (model doesn't fit in GPU memory)
❌ Using very small models (overhead not worth it)
❌ Custom communication patterns required
❌ Prefer vendor-specific solutions (AWS SageMaker, etc.)
Common Use Cases
Image model training: Scale ResNet, EfficientNet training to multi-GPU
NLP models: Distribute BERT, GPT training across cluster
Hyperparameter search: Run multiple experiments in parallel
Large batch training: Increase batch size across GPUs
Research acceleration: Speed up iteration cycles
Horovod vs Alternatives
vs PyTorch DDP: Horovod simpler setup, DDP more PyTorch-native
vs DeepSpeed: DeepSpeed better for huge models, Horovod simpler for data parallelism
vs tf.distribute: Horovod framework-agnostic, tf.distribute TensorFlow-only
Unique Strengths
Simplicity: Minimal code changes for distributed training
Framework agnostic: One API for TensorFlow, PyTorch, Keras
Ring-allreduce: Efficient communication pattern
Uber-proven: Battle-tested at scale in production
Bottom line: Best choice for straightforward data parallelism across frameworks. Perfect when you need to scale existing training code to multiple GPUs without major rewrites. Not for model parallelism, but unbeatable for simple distributed data parallel training.