Horovod
Originally developed by Uber, Horovod accelerates distributed deep learning, reducing model training times from days to hours. It effortlessly scales existing Python scripts to utilize hundreds of GPUs, supporting frameworks like TensorFlow, PyTorch, and MXNet. Compatible with cloud platforms and Apache Spark, Horovod streamlines data processing and model training into a unified workflow.
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Top Horovod Features
- Distributed deep learning optimization
- Rapid model training acceleration
- Easy scaling with Python
- Supports multiple frameworks
- GPU scaling efficiency above 90%
- On-premise and cloud compatibility
- Integration with Apache Spark
- Unified data processing pipeline
- Open source community contributions
- Active development and support channels
- Simple installation process
- Flexible infrastructure for training
- Cross-framework model training
- Support for Keras integration
- Efficient resource utilization
- Customizable training configurations
- Extensive documentation and tutorials
- Continuous updates and enhancements
- Mailing lists for community engagement
- User-friendly API design
