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Huggingface finetune3/19/2023 ![]() To deploy Hugging Face models on SageMaker, you can use the Hugging Face DLCs with the new Hugging Face Inference Toolkit. With the SageMaker Python SDK, you can train and deploy your models with just a single line of code, enabling your teams to move from idea to production more quickly. The DLCs are fully integrated with the SageMaker distributed training libraries to train models more quickly using the latest generation of accelerated computing instances available on Amazon Elastic Compute Cloud (Amazon EC2). To enable our common customers, Hugging Face and AWS introduced new Hugging Face Deep Learning Containers (DLCs) to make it easier than ever to train and deploy Hugging Face transformer models on SageMaker. Through this collaboration, Hugging Face is using AWS as its preferred cloud service provider to deliver services to its customers. Earlier this year, the collaboration between Hugging Face and AWS was announced in order to make it easier for companies to use machine learning (ML) models, and ship modern NLP features faster. Hugging Face is a technology startup, with an active open-source community, that drove the worldwide adoption of transformer-based models. This post shows you how to use Amazon SageMaker and Hugging Face to fine-tune a pre-trained BERT model and deploy it as a managed inference endpoint on SageMaker. Wouldn’t it be more productive if you could just start from a pre-trained version and put them to work immediately? This would also allow you to spend more time on solving your business problems. Unfortunately, this complexity prevents most organizations from using these models effectively, if at all. This requires a significant amount of time, skill, and compute resources to train and optimize the models. As the number of model parameters increases, so does the computational infrastructure that is necessary to train these models. These models are exponentially growing larger in size from several million parameters to several hundred billion parameters. The adaptations of the transformer architecture in models such as BERT, RoBERTa, T5, GPT-2, and DistilBERT outperform previous NLP models on a wide range of tasks, such as text classification, question answering, summarization, and text generation. The last few years have seen the rise of transformer deep learning architectures to build natural language processing (NLP) model families.
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