Example Jupyter notebooks that demonstrate how to build, train, and deploy Hugging Face Transformers using Amazon SageMaker and the Amazon SageMaker Python SDK. For inferencing, SageMaker HuggingFace Inference Toolkit
The quickest setup to run example notebooks includes:
- An AWS account
- Proper IAM User and Role setup
- An Amazon SageMaker Notebook Instance
- An S3 bucket
| Notebook | Type | Description |
|---|---|---|
| 01 Getting started with PyTorch | Training | Getting started end-to-end example on how to fine-tune a pre-trained Hugging Face Transformer for Text-Classification using PyTorch |
| 02 getting started with TensorFlow | Training | Getting started end-to-end example on how to fine-tune a pre-trained Hugging Face Transformer for Text-Classification using TensorFlow |
| 03 Distributed Training: Data Parallelism | Training | End-to-end example on how to use distributed training with data-parallelism strategy for fine-tuning a pre-trained Hugging Face Transformer for Question-Answering using Amazon SageMaker Data Parallelism |
| 04 Distributed Training: Model Parallelism | Training | End-to-end example on how to use distributed training with model-parallelism strategy to pre-trained Hugging Face Transformer using Amazon SageMaker Model Parallelism |
| 05 How to use Spot Instances & Checkpointing | Training | End-to-end example on how to use Spot Instances and Checkpointing to reduce training cost |
| 06 Experiment Tracking with SageMaker Metrics | Training | End-to-end example on how to use SageMaker metrics to track your experiments and training jobs |
| 07 Distributed Training: Data Parallelism | Training | End-to-end example on how to use Amazon SageMaker Data Parallelism with TensorFlow |
| 08 Distributed Training: Summarization with T5/BART | Training | End-to-end example on how to fine-tune BART/T5 for Summarization using Amazon SageMaker Data Parallelism |
| 09 Vision: Fine-tune ViT | Training | End-to-end example on how to fine-tune Vision Transformer for Image-Classification |
| 10 Deploy HF Transformer from Amazon S3 | Inference | End-to-end example on how to deploy a model from Amazon S3 |
| 11 Deploy HF Transformer from Hugging Face Hub | Inference | End-to-end example on how to deploy a model from the Hugging Face Hub |
| 12 Batch Processing with Amazon SageMaker Batch Transform | Inference | End-to-end example on how to do batch processing with Amazon SageMaker Batch Transform |
| 13 Autoscaling SageMaker Endpoints | Inference | End-to-end example on how to use autoscaling for a HF Endpoint |
| 14 Fine-tune and push to Hub | Training | End-to-end example on how to use the Hugging Face Hub as MLOps backend for saving checkpoints during training |
| 15 Training Compiler | Training | End-to-end example on how to use Amazon SageMaker Training Compiler to speed up training time |
| 16 Asynchronous Inference | Inference | End-to-end example on how to use Amazon SageMaker Asynchronous Inference endpoints with Hugging Face Transformers |
| 17 Custom inference.py script | Inference | End-to-end example on how to create a custom inference.py for Sentence Transformers and sentence embeddings |
| 18 AWS Inferentia | Inference | End-to-end example on how to AWS Inferentia to speed up inference time |
| 19 Serverless Inference | Inference | Serverless Inference example to save cost |
| 20 Automatic Speech Recognition | Inference | Example how to do speech recognition with wav2vec2 |
| 21 Image Segmentation | Inference | Example how to do image segmentation with segformer |
| 22 Accelerate AWS SageMaker Integration examples | Training | End-to-end examples on how to use AWS SageMaker integration of Accelerate |
| 23 Stable Diffusion | Inference | Example how to generate images with stable diffusion |
| 24 Train BLOOM with PEFT | Training | Example how to train BLOOM on a single GPU using PEFT & LoRA |
| 25 PyTorch FSDP model parallelism | Training | Example how to train LLMs on multi-node multi GPU with PyTorch FSDP |
| 26 Document AI Donut | Training | In this tutorial, you will learn how to fine-tune and deploy Donut-base for document-understand/document-parsing using Hugging Face Transformers and Amazon SageMaker. |
| 27 Deploy Large Language Models | Inference | Learn how to deploy LLMs with the Hugging Face LLM DLC |
| 28 Train LLMs with QLora | Training | Example on how to fine-tune LLMs using Q-Lora |
| 29 Deploy LLMs with Inferentia2 | Inference | Learn how to deploy LLMs using AWS Inferentia2 |
| 30 Evaluate LLMs with ligtheval | Inference | Learn how to evaluate LLMs using Hugging Face LightEval |
| 31 Deploy Embedding Models with TEI | Inference | Learn how to deploy Embedding models for RAG applications with Hugging Face TEI |
| 32 Train and deploy Embedding Models | Train & Inference | Learn how to train and deploy embedding models with Sentence Transformers and TEI |