docs: add documentation for AWS deployment

README.md

@@ -14,34 +14,103 @@ short_description: Object detection Lambda
 
 <b>Aim: AI-driven object detection task</b>
  - Front-end: user interface via Gradio library
- - Back-end: use of AWS Lambda function to run ML models
+ - Back-end: use of AWS Lambda function to run deployed ML models
 
 ## Local development
 
-### User interface
-Use of Gradio library for web interface
-
-Command line:
-> python3 app.py
-
-<b>Note:</b> The Gradio app should now be accessible at http://localhost:7860
-
 
-### Building the docker image:
+### 1. Building the docker image
 
 bash
 > docker build -t object-detection-lambda .
 
-### Running the docker container locally:
+### 2. Running the docker container locally
 
 bash
 
 > docker run --name object-detection-lambda-cont -p 8080:8080 object-detection-lambda
 
+### 3. Execution via user interface
+Use of Gradio library for web interface
+
+<b>Note:</b> The environment variable ```AWS_API``` should point to the local container
+> export AWS_API=http://localhost:8080
+
+Command line for execution:
+> python3 app.py
+
+The Gradio web application should now be accessible at http://localhost:7860
+
 
-### Testing locally:
+### 4. Execution via command line:
 
 Example of a prediction request
 
+bash
+> encoded_image=$(base64 -i ./tests/data/boats.jpg)
+
+> curl -X POST "http://localhost:8080/2015-03-31/functions/function/invocations" \
+> -H "Content-Type: application/json" \
+> -d '{"body": "'"$encoded_image"'", "isBase64Encoded": true, "model":"yolos-small"}'
+
 python
-> python3 inference_api.py --api http://localhost:8080/2015-03-31/functions/function/invocations --file ./tests/data/boats.jpg
+> python3 inference_api.py \
+> --api http://localhost:8080/2015-03-31/functions/function/invocations \
+> --file ./tests/data/boats.jpg \
+> --model yolos-small
+
+
+## Deployment to AWS
+
+### Pushing the docker container to AWS ECR
+
+Steps:
+ - Create new ECR Repository via aws console
+
+Example: ```object-detection-lambda```
+
+
+ - Optional for aws cli configuration (to run above commands):
+> aws configure
+ 
+ - Authenticate Docker client to the Amazon ECR registry
+> aws ecr get-login-password --region <aws_region> | docker login --username AWS --password-stdin <aws_account_id>.dkr.ecr.<aws_region>.amazonaws.com
+
+ - Tag local docker image with the Amazon ECR registry and repository
+> docker tag object-detection-lambda:latest <aws_account_id>.dkr.ecr.<aws_region>.amazonaws.com/object-detection-lambda:latest
+
+ - Push docker image to ECR
+> docker push <aws_account_id>.dkr.ecr.<aws_region>.amazonaws.com/object-detection-lambda:latest
+
+[Link to AWS Documention](https://docs.aws.amazon.com/AmazonECR/latest/userguide/docker-push-ecr-image.html)
+
+### Creating and testing a Lambda function
+
+<b>Steps</b>: 
+ - Create function from container image
+
+Example name: ```object-detection```
+
+ - Notes: the API endpoint will use the ```lambda_function.py``` file and ```lambda_hander``` function
+ - Test the lambda via the AWS console
+
+
+Advanced notes:
+ - Steps to update the Lambda function with latest container via aws cli:
+> aws lambda update-function-code --function-name object-detection --image-uri <aws_account_id>.dkr.ecr.<aws_region>.amazonaws.com/object-detection-lambda:latest
+
+
+### Creating a REST API via API Gateway
+
+
+<b>Steps</b>: 
+ - Create a new ```Rest API``` (e.g. ```object-detection-api```)
+ - Add a new resource to the API (e.g. ```/detect```)
+ - Add a ```POST``` method to the resource
+ - Integrate the Lambda function to the API
+   - Notes: currently using proxy integration option unchecked
+ - Deploy API with a specific stage (e.g. ```dev``` stage)
+
+Example AWS API Endpoint:
+```https://<api_id>.execute-api.<aws_region>.amazonaws.com/dev/detect```
+