Bagua is a deep learning training acceleration framework for PyTorch developed by AI platform@Kuaishou Technology and DS3 Lab@ETH Zürich. Bagua currently supports:
- Advanced Distributed Training Algorithms: Users can
extend the training on a single GPU to multi-GPUs (may across multiple
machines) by simply adding a few lines of code (optionally in elastic mode).
One prominent feature of Bagua is to provide a flexible system
abstraction that supports state-of-the-art system relaxation techniques
of distributed training. So far, Bagua has integrated communication
primitives including
- Centralized Synchronous Communication (e.g. Gradient AllReduce)
- Decentralized Synchronous Communication (e.g. Decentralized SGD)
- Low Precision Communication (e.g. ByteGrad)
- Asynchronous Communication (e.g. Async Model Average)
- TCP Communication Acceleration (Bagua-Net): Bagua-Net is a low level communication acceleration feature provided by Bagua. It can greatly improve the throughput of AllReduce on TCP network. You can enable Bagua-Net optimization on any distributed training job that uses NCCL to do GPU communication (this includes PyTorch-DDP, Horovod, DeepSpeed, and more).
- Performance Autotuning: Bagua can automatically tune system parameters to achieve the highest throughput.
- Generic Fused Optimizer: Bagua provides generic fused optimizer which improve the performance of optimizers by fusing the optimizer
.step()operation on multiple layers. It can be applied to arbitrary PyTorch optimizer, in contrast to NVIDIA Apex's approach, where only some specific optimizers are implemented. - Load Balanced Data Loader: When the computation complexity of samples in training data are different, for example in NLP and speech tasks, where each sample have different lengths, distributed training throughput can be greatly improved by using Bagua's load balanced data loader, which distributes samples in a way that each worker's workload are similar.
Its effectiveness has been evaluated in various scenarios, including VGG and ResNet on ImageNet, BERT Large and many industrial applications at Kuaishou.
https://github.com/BaguaSys/bagua
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