ML_project-_using_MlFlow

End_to_End Machine Learning Project

End-to-end-ML-Project

Workflows

1. update config.yaml
2. update schema.yaml
3. update params.yaml
4. update the entity
5. update the configuration manager in src config
6. update the components
7. update the pipeline
8. update the main.py
9. update the app.py

How to run?

STEPS:

conda create -n mlproj python=3.8 -y 

conda activate mlproj

pip install -r requirements.txt

python app.py

Now open up your local host 0.0.0.0:8080

MLflow

• Documentation

• MLflow tutorial

cmd

• mlflow ui

dagshub

dagshub

MLFLOW_TRACKING_URI=https://dagshub.com/rajatgoel179/ML_project-_of_Honey_price_prediction_using_MlFlow_and_Dvc.mlflow MLFLOW_TRACKING_USERNAME=rajatgoel179 MLFLOW_TRACKING_PASSWORD=afcc0cd2ead5d9b1ab9f24c4e7ef07e3abe30391 python script.py

Run this to export as env variables:

export MLFLOW_TRACKING_URI=https://dagshub.com/rajatgoel179/ML_project-_of_Honey_price_prediction_using_MlFlow_and_Dvc.mlflow

export MLFLOW_TRACKING_USERNAME=rajatgoel179

export MLFLOW_TRACKING_PASSWORD=3afcc0cd2ead5d9b1ab9f24c4e7ef07e3abe30391

DVC cmd

1. dvc init
2. dvc repro
3. 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

MLFLOW_TRACKING_URI=https://dagshub.com/SwapViSonu/ML_project-_using_MlFlow.mlflow MLFLOW_TRACKING_USERNAME=SwapViSonu MLFLOW_TRACKING_PASSWORD=12iD3XhCQfYF5sf6FaMrzrGFxzrKJ4u85L python script.py