Fine tuning KoGPT2 and deploy KoGPT2 based model in Amazon SageMaker

이 튜토리얼은 KoGPT2 모델을 활용해서 텍스트 감정 분석, 짝 문장 찾기과 같은 여러 NLP 문제를 해결하기 위한 fine-tuning 방법과 만들어진 모델을 Amazon SageMaker를 활용해서 배포하는 방법을 설명합니다.

Tutorial 1: Fine-tuning (Coming soon)

Tutorial 2: Amazon SageMaker MXNet inference 컨테이너를 활용한 확장성 있는 KoGPT2 모델 배포하기

• Notebook to deploy a model
• Inference code

This is a tutorial on how to do fine-tuning on pre-trained KoGPT2 model to solve downstream NLP tasks such as sentimental analysis and paraphrase detection, and how to deploy the NLP model to Amazon SageMaker for production purpose at scale.

Tutorial 1: Fine-tuning (Coming soon)

Tutorial 2: Building scalable KoGPT2 model inference using Amazon SageMaker by extending MXNet inference container

• Notebook to deploy a model
• Inference code

Resources

• KoGPT2 https://github.com/SKT-AI/KoGPT2
• GluonNLP https://gluon-nlp.mxnet.io/
• SageMaker MXNet serving container git repository https://github.com/aws/sagemaker-mxnet-serving-container
• Amazon SageMaker Python SDK - Deploy Endpoints from Model Data https://sagemaker.readthedocs.io/en/stable/using_mxnet.html#deploy-endpoints-from-model-data
• MXNet Model Serving sample https://github.com/awslabs/amazon-sagemaker-examples/blob/master/sagemaker-python-sdk/mxnet_gluon_sentiment

License

This library is licensed under the MIT-0 License. See the LICENSE file.