If you’re new to Hugging Face and NLP, the biggest highlight you need to know is that applications using natural language processing (NLP) are starting to achieve human level performance. NLP on Amazon SageMaker – Hugging Face and model parallelism To learn more about how to use the new features within SageMaker model parallel, refer to Extended Features of the SageMaker Model Parallel Library for PyTorch, and Use with the SageMaker Python SDK. You can also see the GPT2 notebook we used to generate these performance numbers on our GitHub repository. We anticipate that if we increased this up to 240 instances, the full model would take 25 days to train.įor more information about model parallelism, see the paper Amazon SageMaker Model Parallelism: A General and Flexible Framework for Large Model Training. We used this library on AWS and achieved a throughput of 32 samples per second on 120 ml.p4d.24xlarge instances and 175 billion parameters. 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. 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. 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. The last few years have seen rapid development in the field of natural language processing (NLP).

Another word for running the training