git add .
git commit -m "Updated"
git push origin main
conda create -n visa python=3.8 -y conda activate visa pip install -r requirements.txt python app.py
constant config_entity artifact_entity conponent pipeline app.py / demo.py
export MONGODB_URL="mongodb+srv://:...."
export AWS_ACCESS_KEY_ID=<AWS_ACCESS_KEY_ID>
export AWS_SECRET_ACCESS_KEY=<AWS_SECRET_ACCESS_KEY>
-
Login to AWS console.
-
Create IAM user for deployment #with specific access
-
EC2 access : It is virtual machine
-
ECR: Elastic Container registry to save your docker image in aws
-
Build docker image of the source code
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Push your docker image to ECR
-
Launch Your EC2
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Pull Your image from ECR in EC2
-
Lauch your docker image in EC2
-
AmazonEC2ContainerRegistryFullAccess
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AmazonEC2FullAccess
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Create ECR repo to store/save docker image
- Save the URI: 136566696263.dkr.ecr.us-east-1.amazonaws.com/mlproject
- Create EC2 machine (Ubuntu)
- Open EC2 and Install docker in EC2 Machine: #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 US-Visa-Approval-Prediction/README.md at main · entbappy/US-Visa-Approval-Prediction