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+# ci_temp_dond21 Retemplate
+
+![CiT bARQ](https://github.com/user-attachments/assets/22dafa27-447a-41e3-9465-7c8a9f27b2f6)
+
+![image](https://github.com/user-attachments/assets/9988dfbc-a6b3-496d-96d0-51fc66a0aa01)
+
+![image](https://github.com/user-attachments/assets/3169a3bf-537d-48d9-9c82-bab3448a76f4)
+
+
+
+# för dagshub
+
+## Modelkreation: https://www.mlflow.org/docs/latest/model-registry.html#adding-an-mlflow-model-to-the-model-registry
+
+## *data pipline: https://dagshub.com/docs/tutorial/ *
+
+
+# Resten är 🏛 ett Tem⛩lATE 
+
+Kidney-Disease-Classification-MLflow-DVC
+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
+How to run?
+STEPS:
+Clone the repository
+
+https://github.com/ibanknatoPrad/Kidney-Disease-Classification-Deep-Learning-Project
+STEP 01- Create a conda environment after opening the repository
+conda create -n cnncls python=3.8 -y
+conda activate cnncls
+STEP 02- install the requirements
+pip install -r requirements.txt
+# Finally run the following command
+python app.py
+Now,
+
+open up you local host and port
+MLflow
+Documentation
+
+MLflow tutorial
+
+cmd
+mlflow ui
+dagshub
+dagshub
+
+MLFLOW_TRACKING_URI=https://dagshub.com/entbappy/Kidney-Disease-Classification-MLflow-DVC.mlflow MLFLOW_TRACKING_USERNAME=entbappy MLFLOW_TRACKING_PASSWORD=6824692c47a369aa6f9eac5b10041d5c8edbcef0 python script.py
+
+Run this to export as env variables:
+
+
+export MLFLOW_TRACKING_URI=https://dagshub.com/entbappy/Kidney-Disease-Classification-MLflow-DVC.mlflow
+
+export MLFLOW_TRACKING_USERNAME=entbappy 
+
+export MLFLOW_TRACKING_PASSWORD=6824692c47a369aa6f9eac5b10041d5c8edbcef0
+
+DVC cmd
+dvc init
+dvc repro
+dvc dag
+About MLflow & DVC
+MLflow
+
+Its Production Grade
+Trace all of your expriements
+Logging & taging your model
+DVC
+
+Its very lite weight for POC only
+lite weight expriements tracker
+It can perform Orchestration (Creating Pipelines)
+AWS-CICD-Deployment-with-Github-Actions
+1. Login to AWS console.
+2. Create IAM user for deployment
+#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
+3. Create ECR repo to store/save docker image
+- Save the URI: 566373416292.dkr.ecr.us-east-1.amazonaws.com/chicken
+4. Create EC2 machine (Ubuntu)
+5. 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
+6. Configure EC2 as self-hosted runner:
+setting>actions>runner>new self hosted runner> choose os> then run command one by one
+7. Setup github secrets:
+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 = simple-app