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Deep learning (DL) models continue to grow and the datasets used to train them are increasing in size, leading to longer training times. Therefore, training is being ac-celerated by deploying DL models across multiple devices (e.g., GPUs/TPUs) in parallel.
How to Use Multiple GPUs for Deep Learning. Deep learning is a subset of machine learning that does not rely on structured data to develop accurate predictive models. This method uses networks of algorithms modeled after neural networks in the brain to distill and correlate large amounts of data.
Multi-GPU Training. High Performance Deep Learning. Eric Marcus & Jonas Teuwen. Overview of the subjects for the multi-GPU part. Theory. Why GPU? Why multiple GPUS? Backends – NCCL, GLOO. GPU operations – scatter, gather, all reduce etc. Practice. Data parallel. Distributed data parallel. What more can we do? Practice ++. Pytorch Lightning.
1 sty 2021 · With the popular use of multi-GPU and multi-node GPU systems for scaling deep learning performance, optimized data communication across multiple GPUs is getting more attention in both industry and academia.
30 lip 2019 · Deploying deep learning (DL) models across multiple compute devices to train large and complex models continues to grow in importance because of the demand for faster and more frequent...
This study evaluates the running performance of four state-of-the-art distributed deep learning frameworks (i.e., Caffe-MPI, CNTK, MXNet, and TensorFlow) over single-GPU, multi- GPU, and multi-node environments and identifies bottlenecks and overheads which could be further optimized.
Working with deep learning tools, frameworks, and workflows to perform neural network training, you’ll learn concepts for implementing Horovod multi-GPUs to reduce the complexity of writing efficient distributed software.
