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Fine-tuningFreeOpen Source
HOROVOD
Distributed deep learning training with minimal code changes
Apache-2.0
ABOUT
Training large deep learning models requires distributing computation across multiple GPUs and machines, but distributed training frameworks historically require significant code changes and deep understanding of distributed systems. Horovod abstracts away the complexity of distributed training with a simple, Ring-AllReduce-based approach that works with major deep learning frameworks. Developers add just a few lines of code to scale training from one GPU to hundreds, achieving near-linear speedup without rewriting their training loops.
INSTALL
pip install horovodINTEGRATION GUIDE
1. Scale large model training from a single GPU to multi-node GPU clusters with minimal code changes
2. Accelerate fine-tuning of LLMs and vision transformers across distributed GPU hardware
3. Run distributed hyperparameter search and training experiments across multiple workers simultaneously
4. Train ensemble models and perform multi-GPU transfer learning for production AI pipelines
5. Enable elastic distributed training that scales and adjusts to available GPU resources dynamically
TAGS
distributed-trainingdeep-learningtensorflowpytorchallreducegpumulti-gputraining