##workflows
- update config.yaml
- update secrets.yaml[optional]
- update params.yaml
- update the entity
- update the configuration manager in src config
- update the components
- update the pipeline
- update the main.py
- update the dvc.yaml
- app.py
###step1:
clone repository
https://github.com/SAMANTA1401/Kidney_Disease_Classification_Ml_Dvc.git
###step2:
create a conda enviroment after opening the repository
conda create -n kidney python=3.11 -y
conda activate kidney
###step3:
pip install -r requirements.txt
-mlflow ui
###dagshub dagshub
###dvc cmd
dvc iniit
dvc repro
dvc dag
This model is not working good as very less number of images are used and epochs is 1 and trained for only one times no evaluation is done .
####with specific access
-
EC2 access : It is virtual machine
-
ECR: Elastic Container registry to save your docker image in aws
####Description: About the deployment
-
Build docker image of the source code
-
Push your docker image to ECR
-
Launch Your EC2
-
Pull Your image from ECR in EC2
-
Lauch your docker image in EC2
####Policy:
-
AmazonEC2ContainerRegistryFullAccess
-
AmazonEC2FullAccess
- Save the URI:987001014426.dkr.ecr.eu-north-1.amazonaws.com/kidney
#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_REGION = us-east-1
AWS_ECR_LOGIN_URI = demo>> 566373416292.dkr.ecr.ap-south-1.amazonaws.com
ECR_REPOSITORY_NAME = kidney