Horovod is an open-source distributed deep learning training framework designed to scale TensorFlow and PyTorch workloads across multiple GPUs and nodes. It simplifies the process of training models on distributed systems by providing efficient communication and synchronization mechanisms.
Key Features
- Cross-framework support (TensorFlow & PyTorch)
- High-performance distributed training with minimal overhead
- Ease of use for multi-GPU and multi-node setups
- Flexible communication backends (MPI, Horovod's built-in Ring AllReduce)
Use Cases
- Training large-scale models (e.g., NLP, computer vision)
- Accelerating research workflows with parallel computation
- Deploying in production environments for distributed inference
For more details on distributed training techniques, visit our guide on Distributed_Learning_Methods.
Why Choose Horovod?
- ✅ Seamless integration with popular deep learning frameworks
- 🚀 Scalability from single machines to clusters
- 📈 Performance optimization via efficient all-reduce algorithms
Explore practical examples and tutorials in our Horovod Documentation.
Community & Resources
Join the conversation on AI_Frameworks to discuss distributed training solutions!