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While state-of-the-art NLP models are very powerful, they also require massive computational resources to train.
Access to GPUs is increasingly necessary for modern NLP teams, but that frequently comes with headaches: sharing a GPU cluster is difficult, and porting your code to use distributed training is a hassle.
Consequently, many deep learning teams spend more time on DevOps than they do on deep learning.
