Automatic vehicle license plate detection and recognition is a key technique in most traffic-related applications and is an active research topic in the image processing domain. As a result, different methods, techniques and algorithms have been developed for license plate detection and recognition.
git add .
git commit -m "Updated"
git push origin mainconda create -n visa python=3.8 -yconda activate visapip install -r requirements.txtpython app.py- constant
- config_entity
- artifact_entity
- conponent
- pipeline
- app.py / demo.py
export AWS_ACCESS_KEY_ID=<AWS_ACCESS_KEY_ID>
export AWS_SECRET_ACCESS_KEY=<AWS_SECRET_ACCESS_KEY>#with specific access
1. EC2 access : It is virtual machine
2. ECR: Elastic Container registry to save your docker image in aws
#Description: About the deployment
1. Build docker image of the source code
2. Push your docker image to ECR
3. Launch Your EC2
4. Pull Your image from ECR in EC2
5. Lauch your docker image in EC2
#Policy:
1. AmazonEC2ContainerRegistryFullAccess
2. AmazonEC2FullAccess
- Save the URI: 136566696263.dkr.ecr.us-east-1.amazonaws.com/mlproject
#optinal
sudo apt-get update -y
sudo apt-get upgrade
#required
curl -fsSL https://get.docker.com -o get-docker.sh
sudo sh get-docker.sh
sudo usermod -aG docker ubuntu
newgrp docker
setting>actions>runner>new self hosted runner> choose os> then run command one by one
- AWS_ACCESS_KEY_ID
- AWS_SECRET_ACCESS_KEY
- AWS_DEFAULT_REGION
- ECR_REPO