Tweet_Sentiment_Extraction_using_Amazon_Sagemaker

In this project, I have tried to solve Tweet Sentiment Extraction Kaggle Problem.

In notebooks/01-DataPrep-and-training.ipynb, I have used Amazon Sagemaker to train and deploy a roberta model on Tweet Sentiment Extraction dataset dataset from kaggle. Then in the notebook 02-Create-Lambda.pynb I have showed how to create AWS Lambda required to host the Sagemaker Endpoint via API Gateway .

Pytorch and hugging face is used for the modeling purpose.

Follow the steps as below.

1. Create a Sagemaker notebook instance with the instance type as ml.t2.medium
2. Once the Notebook instance is In Service, clone this git repo in the Jupyter environment
3. Run notebooks/01-DataPrep-and-training.ipynb notebook to train and deploy the model with Amazon Sagemaker followed by Inference
4. Refer to src/train.py script used for training the model
5. Run notebook notebooks/02-Create-Lambda to create AWS Lambda required to host the Sagemaker Endpoint via API Gateway
6. Follow this detailed AWS tutorial to invoke lambda function via Amazon API gateway
7. Download app folder in your local and run app/app.py and change the variable url = "<<Amazon API Gateway url link>>" with your Amazon API Gateway url link to create a flask API.
8. (Optional) You can follow this medium article to run your Flask API on AWS EC2 instance.

Demo

Authors

• @Ravishukla1234

License

This library is licensed under the Apahce License 2.0. See the LICENSE file.