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Deepset achieves a 3.9x speedup and 12.8x cost reduction for training NLP models by working with AWS and NVIDIA

 Deepset achieves a 3.9x speedup and 12.8x cost reduction for training NLP models by working with AWS and NVIDIA

This is a guest post from deepset (creators of the open source frameworks FARM and Haystack), and was contributed to by authors from NVIDIA and AWS. 

At deepset, we’re building the next-level search engine for business documents. Our core product, Haystack, is an open-source framework that enables developers to utilize the latest NLP models for semantic search and question answering at scale. Our software as a service (SaaS) platform, Haystack Hub, is used by developers from various industries, including finance, legal, and automotive, to find answers in all kinds of text documents. You can use these answers to improve the search experience, cover the long-tail of chat bot queries, extract structured data from documents, or automate invoicing processes.

Pretrained language models like BERT, RoBERTa, and ELECTRA form the core for this latest type of semantic search and many other NLP applications. Although plenty of English models are available, the availability for other languages and more industry-specific terms (such as finance or automotive) is usually very limited and often complicates applications in the industry. Therefore, we regularly train language models for languages not covered by existing models (such as German BERT and German ELECTRA), models for special domains (such as finance and aerospace), or even models for client-specific jargon.


Pretraining language models from scratch typically involves two major challenges: cost and development effort.

Training a language model is an extremely compute-intensive task and requires multiple GPUs running for multiple days. To give you a rough idea, training the original RoBERTa model took about 1 day on 1024 NVIDIA V100 GPUs.

Computation costs aren’t the only thing that can stress your budget. A considerable amount of manual development is required to create the training data and vocabulary, configure hyperparameters, start and monitor training jobs, and run periodical evaluation of different model checkpoints. In our first training runs, we also found several bugs only after multiple hours of training, resulting in a slow development cycle. In summary, language model training can be a painful job for a developer and easily consumes multiple days of work.


In a collaborative effort, AWS, NVIDIA, and deepset were able to complete training 3.9 times faster while lowering cost by 12.8 times and reducing developer effort from days to hours. We optimized the GPU utilization during training via PyTorch’s DistributedDataParallel (DDP) and enabled larger batch sizes by switching to Automatic Mixed Precision (AMP). Furthermore, we introduced a StreamingDataSilo that allows us to load the training data lazily from disk and to do the preprocessing on the fly, leading to a lower mem


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