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Fast.ai Achieves Speed Record with NVIDIA



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Fast.ai announced a new speed record for training ImageNet, achieving 93% accuracy in only 18 minutes.

Fast.ai alumni Andrew Shaw, and Defense Innovation Unit Experimental (DIU) researcher Yaroslav Bulatov achieved the speed record using 128 NVIDIA Tesla V100 Tensor Core GPUs on the Amazon Web Services (AWS) cloud, with the fastai and cuDNN-accelerated PyTorch libraries.

The record is 40% faster than the previous record.

DIU and fast.ai will be releasing software to allow anyone to easily train and monitor their own distributed models on AWS, using the best practices developed in this project, said Jeremy Howard, a founding researcher at fast.ai.

The researchers said they were encouraged by previous speed records achieved on publicly available machines by the AWS team.

The set of tools developed by fast.ai focused on fast iteration with single-instance experiments, whilst the nexus-scheduler developed by DIU was focused on robustness and multi-machine experiments, Howard explained.

“We’re not even done yet – we have some ideas for further simple optimizations which we’ll be trying out,” Howard said. “There’s certainly plenty of room to go faster still.”

For more information about this news, visit www.fast.ai.