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Fine-tuningFreeOpen Source
ONEFLOW
Deep learning framework built for scaling
Apache-2.0
ABOUT
Scaling deep learning training across multiple GPUs and machines traditionally requires manual parallelism strategies — data parallel, model parallel, pipeline parallel — each demanding deep expertise to configure correctly. OneFlow introduces an automatic parallelism architecture that analyzes the computation graph and distributes operations optimally without code changes, so teams can go from single-GPU prototyping to multi-node training with zero refactoring.
INSTALL
pip install oneflowINTEGRATION GUIDE
1. Scale a large language model fine-tuning job from one GPU to dozens without rewriting the training script
2. Train computer vision models on massive image datasets using automatic data and model parallelism
3. Prototype experiments on a single GPU and seamlessly transition to a multi-node cluster for production runs
4. Build custom neural architectures that benefit from automatic graph-level parallelism optimization
TAGS
pythondeep-learningdistributed-trainingparallelismgputraining-framework