- 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
Clone the repository
https://github.com/Shahequa/Chicken-Disease-Classification
conda create -n chicken python=3.8 -y
conda activate chicken
pip install -r requirements.txt
# Finally run the following command
python app.py
Now,
open up your local host and port
- dvc init
- dvc repro
- dvc dag
- mlflow ui
MLFLOW_TRACKING_URI=https://dagshub.com/entbappy/MLflow-DVC-Chicken-Disease-Classification.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/MLflow-DVC-Chicken-Disease-Classification.mlflow
export MLFLOW_TRACKING_USERNAME=entbappy
export MLFLOW_TRACKING_PASSWORD=6824692c47a369aa6f9eac5b10041d5c8edbcef0
#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: 746381594102.dkr.ecr.us-east-1.amazonaws.com/chicken
#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 =
AWS_ECR_LOGIN_URI = demo>> 566373416292.dkr.ecr.ap-south-1.amazonaws.com
ECR_REPOSITORY_NAME = simple-app
s3cEZKH5yytiVnJ3h+eI3qhhzf9q1vNwEi6+q+WGdd+ACRCZ7JD6
docker build -t chickenapp.azurecr.io/chicken:latest .
docker login chickenapp.azurecr.io
docker push chickenapp.azurecr.io/chicken:latest
- Build the Docker image of the Source Code
- Push the Docker image to Container Registry
- Launch the Web App Server in Azure
- Pull the Docker image from the container registry to Web App server and run
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)