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Register a new userWhale: Scaling Deep Learning Model Training to the Trillions
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Xianyan Jia
Publication date
2020
fields
Informatics Engineering
and research's language is
English
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Scaling up deep neural networks has been proven effective in improving model quality, while it also brings ever-growing training challenges. This paper presents Whale, an automatic and hardware-aware distributed training framework for giant models. Whale generalizes the expression of parallelism with four primitives, which can define various parallel strategies, as well as flexible hybrid strategies including combination and nesting patterns. It allows users to build models at an arbitrary scale by adding a few annotations and automatically transforms the local model to a distributed implementation. Moreover, Whale is hardware-aware and highly efficient even when training on GPUs of mixed types, which meets the growing demand of heterogeneous training in industrial clusters. Whale sets a milestone for training the largest multimodal pretrained model M6. The success of M6 is achieved by Whales design to decouple algorithm modeling from system implementations, i.e., algorithm developers can focus on model innovation, since it takes only three lines of code to scale the M6 model to trillions of parameters on a cluster of 480 GPUs.
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We present scalable hybrid-parallel algorithms for training large-scale 3D convolutional neural networks. Deep learning-based emerging scientific workflows often require model training with large, high-dimensional samples, which can make training much more costly and even infeasible due to excessive memory usage. We solve these challenges by extensively applying hybrid parallelism throughout the end-to-end training pipeline, including both computations and I/O. Our hybrid-parallel algorithm extends the standard data parallelism with spatial parallelism, which partitions a single sample in the spatial domain, realizing strong scaling beyond the mini-batch dimension with a larger aggregated memory capacity. We evaluate our proposed training algorithms with two challenging 3D CNNs, CosmoFlow and 3D U-Net. Our comprehensive performance studies show that good weak and strong scaling can be achieved for both networks using up 2K GPUs. More importantly, we enable training of CosmoFlow with much larger samples than previously possible, realizing an order-of-magnitude improvement in prediction accuracy.
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