The last few years have seen rapid development in the field of natural language processing (NLP). While hardware has improved, such as with the latest generation of accelerators from NVIDIA and Amazon, advanced machine learning (ML) practitioners still regularly run into issues scaling their large language models across multiple GPU’s.
In this blog post, we briefly summarize the rise of large- and small- scale NLP models, primarily through the abstraction provided by Hugging Face and with the modular backend of Amazon SageMaker. In particular we highlight the launch of four additional features within the SageMaker model parallel library that unlock 175 billion parameter NLP model pretraining and fine-tuning for customers.
We used this library on the SageMaker training platform and achieved a throughput of 32 samples per second on 120 ml.p4d.24xlarge instances and 175 billion parameters. We anticipate that if we increased this up to 240 instances, the full model would take

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