The Partnership: Amazon SageMaker and Hugging Face

Look at these smiles!

Today, we announce a strategic partnership between Hugging Face and Amazon to make it easier for companies to leverage State of the Art Machine Learning models, and ship cutting-edge NLP features faster.

Through this partnership, Hugging Face is leveraging Amazon Web Services as its Preferred Cloud Provider to deliver services to its customers.

As a first step to enable our common customers, Hugging Face and Amazon are introducing new Hugging Face Deep Learning Containers (DLCs) to make it easier than ever to train Hugging Face Transformer models in Amazon SageMaker.

To learn how to access and use the new Hugging Face DLCs with the Amazon SageMaker Python SDK, check out the guides and resources below.

On July 8th, 2021 we extended the Amazon SageMaker integration to add easy deployment and inference of Transformers models. If you want to learn how you can deploy Hugging Face models easily with Amazon SageMaker take a look at the new blog post and the documentation.

Features & Benefits 🔥

One Command is All you Need

With the new Hugging Face Deep Learning Containers available in Amazon SageMaker, training cutting-edge Transformers-based NLP models has never been simpler. There are variants specially optimized for TensorFlow and PyTorch, for single-GPU, single-node multi-GPU and multi-node clusters.

Accelerating Machine Learning from Science to Production

In addition to Hugging Face DLCs, we created a first-class Hugging Face extension to the SageMaker Python-sdk to accelerate data science teams, reducing the time required to set up and run experiments from days to minutes.

You can use the Hugging Face DLCs with the Automatic Model Tuning capability of Amazon SageMaker, in order to automatically optimize your training hyperparameters and quickly increase the accuracy of your models.

Thanks to the SageMaker Studio web-based Integrated Development Environment (IDE), you can easily track and compare your experiments and your training artifacts.