HiDhineshRaja/Image-Captioning-Sagemaker

Deploy Huggingface Image-to-text pretrained model on AWS Sagemaker with Flask-based webapp deployed on AWS Elastic Beanstalk

Image Captioning Sagemaker App

This application deploys a Huggingface Image-to-text pretrained model on AWS SageMaker and provides a Flask-based web interface to caption images. The web application is capable of handling both URL and uploaded images.

Prerequisites

• AWS Account with SageMaker and Elastic Beanstalk permissions
• AWS CLI installed and configured with user credentials
• Git LFS: On Mac, you can install it using brew install git-lfs
• Python3 with Flask, Boto3, SageMaker, Pillow and Requests installed

Deployment Steps

1. Clone the repository and navigate to the project directory.

2. Prepare the model for deployment:

   • Create a folder called code in the project directory.
   • Add inference.py and requirements.txt to the code folder.
   • Create a model.tar.gz file with the layout specified in the Huggingface SageMaker inference documentation.

    model.tar.gz/
    |- pytorch_model.bin
    |- ....
    |- code/
      |- inference.py
      |- requirements.txt 

3. Upload the model to S3 using AWS CLI: aws s3 cp model.tar.gz s3://<your-bucket-name>

4. Deploy the model on SageMaker. You can follow the instructions in the SageMaker notebook provided in this repository. Alternatively, you can load the deployed model using the following Python code:

   from sagemaker import Session
   from sagemaker.huggingface.model import HuggingFacePredictor
   
   sagemaker_session = Session()
   predictor = HuggingFacePredictor(
       endpoint_name="<endpoint-name>",
       sagemaker_session=sagemaker_session
   )

5. Set up the Flask application:

   • Create application.py and index.html in the templates folder.
   • Test the application locally by running flask run and visiting http://127.0.0.1:5000/.

6. Prepare the application for Elastic Beanstalk:

   • Rename app.py to application.py.

   • Create a requirements.txt file listing all the necessary Python packages.

   • Create a .ebextensions directory and a 01_flask.config file inside it with the following content:

     option_settings:
       aws:elasticbeanstalk:application:environment:
         PYTHONPATH: "/var/app/current:$PYTHONPATH"
       aws:elasticbeanstalk:container:python:
         WSGIPath: "application:application"

   • Zip all the application files at the root level of the flask_app folder.

Please note that the file structure of your application should look like this:

flask_app/
├── application.py
├── requirements.txt
├── templates/
│   └── index.html
└── .ebextensions/
    └── 01_flask.config

7. Deploy the application on Elastic Beanstalk:

   • Go to the AWS Management Console and select Elastic Beanstalk.
   • Add SageMaker Full Access to aws-elasticbeanstalk-ec2-role.
   • Create a new application and select the default VPC.
   • Upload the zip archive created in the previous step and launch the application.

8. Setting Environment Variables in Elastic Beanstalk

Once your application is deployed on Elastic Beanstalk, you need to set some environment variables for your application to function properly.

1. In the Elastic Beanstalk dashboard, navigate to your application.
2. Under the "Software" configuration, click on "Modify".
3. Scroll down to the "Environment properties" section.

Here, you will need to add the following variables:

1. AWS_REGION: The AWS region where your resources are located.
2. PYTHONPATH: Should be set automatically
3. AWS_ACCESS_KEY_ID: Your AWS Access Key ID for programmatic access.
4. AWS_SECRET_ACCESS_KEY: Your AWS Secret Access Key corresponding to the Access Key ID.
5. SAGEMAKER_ENDPOINT_NAME: The endpoint name of your deployed SageMaker model.

Your environment variables section should look like this:

Make sure to replace the placeholders with your actual values and click "Apply" to save the changes.