End-to-end-Machine-Learning-Project-with-MLflow

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:

Clone the repository

https://github.com/forghmc/ShopTalk.git

Download following files from gdrive - https://drive.google.com/drive/folders/14BcyjB7wbvvgy9g3_suVVey4yufOfz24

1. processed_dataset_target_data_with_captions_only.csv
2. resize.zip -keep resize folder in parent directory and unzip it.

Update Env Variables

Goto setenv.sh in parent folder edit the paths and keys make sure the secret keys are set and csv path should be same as the downloaded file is kept.

After setting variables Run setenv.sh if needed gives executable permission

chmod +x setenv.sh

sh setenv.sh

Create conda env

conda create -p ./env python=3.10 -y

Activate env

conda activate ./env

install the requirements for backend

pip install -r generate_request/requirements.txt' 

Running Build

Resize Image - this is neeeded once

1. Provide the value for extracted image folder with in file
2. run utils/image_exctract.py

To start streamlit app

1. Make sure your path to image in appv2.py are pointing towards extracted resized image folder

2. Update the path the 'processed_dataset_target_data_with_captions_only.csv' in appv2 file.

3. Set pine cone key in env with "export pkey=" and also update index_name.

4. Then

streamlit run appv2.py

python -m streamlit run appv2.py

To Kill streamlit process

ps -ef | grep streamlit | grep -v grep | awk '{print $2}' | xargs kill

Return to your normal shell by typing:

 deactivate

MLflow

Documentation

cmd

• mlflow ui

dagshub

dagshub

MLFLOW_TRACKING_URI=https://dagshub.com/someshnaman/End_to_end_MLOPS_project.mlflow MLFLOW_TRACKING_USERNAME=someshnaman MLFLOW_TRACKING_PASSWORD=6e7e6b4e21fb207c4cbf0d4d7f20506e23e748cc python script.py

Run this to export as env variables:

export MLFLOW_TRACKING_URI=https://dagshub.com/someshnaman/End_to_end_MLOPS_project.mlflow

export MLFLOW_TRACKING_USERNAME=someshnaman 

export MLFLOW_TRACKING_PASSWORD=6e7e6b4e21fb207c4cbf0d4d7f20506e23e748cc

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: 023110776410.dkr.ecr.us-east-1.amazonaws.com/shoptalk

4. Create EC2 machine (Ubuntu)

5. Open EC2 and Install docker in EC2 Machine:

6. To start streamlit app

streamlit run app.py

7. To Kill streamlit process

ps -ef | grep streamlit | grep -v grep | awk '{print $2}' | xargs kill

#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

About MLflow

MLflow

• Its Production Grade
• Trace all of your expriements
• Logging & tagging your model