walmart-sales-forecast-mlops

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walmart-sales-forecast-mlops

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# Walmart Sales Forecasting end-to-end MLOps

# Architecture
![architecture](https://github.com/user-attachments/assets/8d0c8002-d300-448a-8084-7953f4c8ccde)

# How to run?

Clone the repository

```bash
https://github.com/HarshVBhatt/walmart-sales-forecast-mlops.git
```
## STEP 01- Create a conda environment after opening the repository

```bash
conda create -n sales-forecast
```

```bash
conda activate sales-forecast
```

## STEP 02- install the requirements
```bash
pip install -r requirements.txt
```

## STEP 03- setup aws services

### 1. Login to AWS console.

### 2. Create IAM user for deployment and access key. Note these credentials

#Policy:

1. AmazonEC2ContainerRegistryFullAccess

2. AmazonEC2FullAccess

3. AmazonS3FullAccess

### 3. Set up a public s3 bucket with the name "walmart-sales-forecast"

### 4. Configure to read/write to s3
```bash
pip install awscli
aws configure
```

- Enter the credentials you previously noted.

- Enter your default region for the region prompt

- Enter "json" for output prompt

Run the following to ensure s3 access
```bash
aws s3 ls
```

## STEP 04- Download the original data
	Download the original data from - https://kaggle.com/competitions/walmart-recruiting-store-sales-forecasting

Convert the downloaded .zip files into a single file - raw_data.zip and upload to your github repository (License and competition rule compliance is your responsibility)

Change the field - "data_ingestion: source_url" om config/config.yaml to reflect the raw_file.zip path

## STEP 05- Run the pipeline locally
```bash
python src/main.py
```

You should see log files being generated showing each step and progress of the pipeline.

Once the pipeline completes, check s3 folder for successful file uploads

### MLflow

[Documentation](https://mlflow.org/docs/latest/index.html)

##### cmd
- mlflow ui

### dagshub
[dagshub](https://dagshub.com/)

MLFLOW_TRACKING_URI= <mlflow-repo-uri> \
MLFLOW_TRACKING_USERNAME= <mlflow-username> \
MLFLOW_TRACKING_PASSWORD= <mlflow-passwor> \
python script.py

Run this to export as env variables:

```bash

export MLFLOW_TRACKING_URI=<mlflow-repo-uri>

export MLFLOW_TRACKING_USERNAME=<mlflow-username>

export MLFLOW_TRACKING_PASSWORD=<mlflow-passwor>

```

## STEP 06- AWS-CICD-Deployment-with-Github-Actions

### 1. Login to AWS console.

### 2. Create ECR repo to store/save docker image
    - Save the URI: 050451357525.dkr.ecr.us-east-2.amazonaws.com/repo-name

### 3. Create EC2 machine (Ubuntu)

### 4. Open EC2 and Install docker in EC2 Machine:

#optional

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
	
### 5. Configure EC2 as self-hosted runner:
    setting>actions>runner>new self hosted runner> choose os> then run command one by one

### 6. 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

### 7. Configure AWS network and run app

1. Add an inbound rule with the port 8080 for the instance security group

2. Open the public IPv4 of the instance xxx.x.x.xx:8080 and the app should run

3. Click "Run simulation". You should see a plot being generated and updated regularly with the r2 score per batch.

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