thirubaranikumar / AWS-Serverless-AI

Serverless AI Workshop

This repo contains source code and supporting files for the Serverless AI workshop.

It included the following folders:

• /SAM/digit_classifier - a serverless application for classifying handwritten digits using a machine learning model in PyTorch
• /SAM/clip_crop - a serverless application for searching and cropping objects in a image using YoloV5 and OpenAI CLIP
• /Sagemaker/serverless_inference.ipynb - jupyter notebook to deploy Huggingface sentiment classifier as Sagemaker serverless endpoint

Prerequisite

• AWS account - Create AWS account
• AWS CLI - Install the AWS CLI
• SAM CLI - Install the SAM CLI
• Docker - Install Docker community edition

SAM

SAM is Serverless Application Model to deploy serverless applications in AWS

In this workshop, we will deploy two applications. One is a simple mnist digit classifier and second one is little advanced that use Open AI CLIP and YoloV5 to search and crop objects in a image.

SAM installation

1. Download SAM https://github.com/aws/aws-sam-cli/releases/latest/download/AWS_SAM_CLI_64_PY3.msi
2. Install SAM
3. Verify installation - Open cmd terminal and run sam —version

For more details : https://docs.aws.amazon.com/serverless-application-model/latest/developerguide/serverless-sam-cli-install-windows.html

Serverless application deployment:

1. git clone --recursive https://github.com/bismillahkani/AWS-Serverless-AI.git

2. Create IAM user with following permissions,

   • IAMFullAccess
   • AWSLambda_FullAccess
   • AmazonAPIGatewayAdministrator
   • AWSCloudFormationFullAccess
   • AmazonEC2ContainerRegistryFullAccess
   • AmazonS3FullAccess
   • CloudWatchLogsFullAccess
   • AmazonEventBridgeFullAccess

3. IAM User access key - Create access keys

4. open terminal and run aws configure. Enter Access key and Secret key.

   Digit Classifier application

5. cd ~/SAM/digit_classifier

6. open terminal and run sam build

7. run sam deploy —guided and follow the instructions as shown in terminal

8. Once deployed, copy the API url from Outputs and test it in POSTMAN`

   Clip Crop application [Homework]

9. For clip_crop application, change directory cd ~/SAM/clip_crop

10. Download the model weights for CLIP model from https://openaipublic.azureedge.net/clip/models/40d365715913c9da98579312b702a82c18be219cc2a73407c4526f58eba950af/ViT-B-32.pt and copy the weights to /SAM/clip_crop/app/weights

11. run sam build and sam deploy --guided

12. Once deployed, copy the API url of clip_crop from Outputs and update the /SAM/clip_crop/app/testapi.py

13. Run testapi.py

Sagemaker serverless

1. Create a notebook instance in Sagemaker
2. Upload the serverless_inference.ipynb notebook
3. Run cell, deploy serverless endpoint and do inference

Demo

https://sam-bkvgvd906y7.streamlit.app/

Cost

I have not investigated the free tier usage for this workshop. Hence cannot guarantee that you will not be charged. You can refer the below AWS pricing details,

Lambda pricing - https://aws.amazon.com/lambda/pricing/

Sagemaker pricing - https://aws.amazon.com/sagemaker/pricing/

ECR pricing - https://aws.amazon.com/ecr/pricing/

API gateway pricing - https://aws.amazon.com/api-gateway/pricing/

Clean-up

Don’t forget to delete all the resources you created for this workshop. Otherwise you will be charged.