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- How to deploy Embedding Models to Amazon SageMaker using . . . - Hugging Face
Compared to deploying regular Hugging Face models we first need to retrieve the container uri and provide it to our HuggingFaceModel model class with a image_uri pointing to the image To retrieve the new Hugging Face Embedding Container in Amazon SageMaker, we can use the get_huggingface_llm_image_uri method provided by the sagemaker SDK This method allows us to retrieve the URI for the
- Build a Hugging Face text classification model in Amazon SageMaker . . .
For more information about how to use the new SageMaker Hugging Face text classification algorithm for transfer learning on a custom dataset, deploy the fine-tuned model, run inference on the deployed model, and deploy the pre-trained model as is without first fine-tuning on a custom dataset, see the following example notebook
- Deploy a HuggingFace model on SageMaker AI | AWS re:Post
} Note: Replace example-hf-model-id with your model ID from the HuggingFace models list on the Hugging Face website Replace example-hf-task with the task that you want to use for predictions For a list of HF_TASK values, see Pipelines on the Hugging Face website Create the class, and then deploy the class to SageMaker AI:
- Deploy models to Amazon SageMaker - Hugging Face
This guide will show you how to deploy models with zero-code using the Inference Toolkit The Inference Toolkit builds on top of the pipeline feature from 🤗 Transformers Learn how to: Install and setup the Inference Toolkit Deploy a 🤗 Transformers model trained in SageMaker Deploy a 🤗 Transformers model from the Hugging Face [model Hub] (https: huggingface co models) Run a Batch
- GitHub - aws sagemaker-huggingface-inference-toolkit
SageMaker Hugging Face Inference Toolkit is an open-source library for serving 🤗 Transformers and Diffusers models on Amazon SageMaker This library provides default pre-processing, predict and postprocessing for certain 🤗 Transformers and Diffusers models and tasks It utilizes the SageMaker Inference Toolkit for starting up the model server, which is responsible for handling inference
- Resources for using Hugging Face with Amazon SageMaker AI
Learn how to use Hugging Face models for Natural Language Processing (NLP) with Amazon SageMaker AI This includes sample notebooks for training and inference
- Text to Image - Hugging Face
We’re on a journey to advance and democratize artificial intelligence through open source and open science
- Hugging Face on Amazon SageMaker - Amazon Web Services
With Hugging Face on AWS, you can customize and deploy publicly available foundation models through Amazon SageMaker on GPUs, Trainium and Inferentia, in a matter of clicks
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